Upload
trinhdien
View
229
Download
2
Embed Size (px)
Citation preview
STOCK PRICE INFORMATIVENESS,
CORPORATE EXPENDITURE AND
INFORMATION ASYMMETRY
Lee Mei Yee
Doctor of Philosophy
December 2013
STOCK PRICE INFORMATIVENESS,
CORPORATE EXPENDITURE AND
INFORMATION ASYMMETRY
A thesis submitted in fulfilment of the requirements for the degree of
Doctor of Philosophy
Lee Mei Yee
Bachelor of Accounting (First Class Honours)
University Malaya, Malaysia
Chartered Accountant (CA)
Malaysian Institute of Accountants (MIA)
Certified Public Accountant (CPA)
The Malaysian Institute of Certified Public Accountants (MICPA)
Postgraduate Diploma in Business and Commerce
Monash University Malaysia
Department of Accounting and Finance
School of Business
Monash University Malaysia
December 2013
ADDENDUM
p 3/lines 10 and 11: Delete the words “In this thesis,” and insert the following:
“Based on prior literature, this thesis examines whether stock price informativeness
influences managers in making their corporate expenditure decisions.” Replace “it…”
with “It…”
p 54: After para 2, add the following new paragraph:
“Based on the literature review, prior empirical research has provided evidence on the
determinants of stock price informativeness. However, studies examining the
consequences of stock price informativeness are relatively sparse. This thesis aims to
provide insights on how stock price informativeness influences managers‟ decisions on
corporate expenditure. This study is useful in providing direct evidence of how managers
adjust firms‟ corporate expenditure when stock price informativeness changes. The
findings from this research have fundamental policy implications because firms are more
likely to improve their stock price informativeness through appropriate financial
reporting.”
p 207/ para 2/ line 5: Delete the word “is” appearing after the word “flows”
pp 219 – 222/ Table 5.17 stretching over four pages:
The symbol “?” appearing in the column entitled “Expected Direction” for the variable ψ
should appear as “-ve”
pp 226 – 229/ Table 5.18 stretching over four pages:
The symbol “?” appearing in the column entitled “Expected Direction” for the variable ψ
should appear as “-ve”
pp 233 – 236/ Table 5.19 stretching over four pages:
The symbol “?” appearing in the column entitled “Expected Direction” for the variable ψ
should appear as “-ve”
Copyright Notices Notice 1 Under the Copyright Act 1968, this thesis must be used only under the normal conditions of scholarly fair dealing. In particular no results or conclusions should be extracted from it, nor should it be copied or closely paraphrased in whole or in part without the written consent of the author. Proper written acknowledgement should be made for any assistance obtained from this thesis. Notice 2 I certify that I have made all reasonable efforts to secure copyright permissions for third-party content included in this thesis and have not knowingly added copyright content to my work without the owner's permission.
ii
TABLE OF CONTENTS
TABLE OF CONTENTS ................................................................................................ ii
ABSTRACT ................................................................................................................ viii
ORIGINALITY STATEMENT ...................................................................................... x
ACKNOWLEDGEMENTS ........................................................................................... xi
CONFERENCE PAPERS PRODUCED FROM THESIS .......................................... xiv
LIST OF TABLES ........................................................................................................ xv
LIST OF FIGURES .................................................................................................. xviii
LIST OF ABBREVIATIONS ...................................................................................... xix
CHAPTER 1 INTRODUCTION ............................................................................... 1
1.1 Background of the Research ................................................................................. 1
1.2 Research Problem.................................................................................................. 6
1.3 Research Objectives .............................................................................................. 8
1.4 Research Questions ............................................................................................... 9
1.5 Overview of Research Methods and Findings ...................................................... 9
1.6 Significance of the Study .................................................................................... 14
1.7 Structure of the Thesis ........................................................................................ 16
1.8 Chapter Summary................................................................................................ 20
iii
CHAPTER 2 LITERATURE REVIEW ................................................................. 21
2.1 Introduction ......................................................................................................... 21
2.2 Stock Price Informativeness ................................................................................ 22
2.2.1 Idiosyncratic Volatility ................................................................................ 22
2.2.2 Measurement of Idiosyncratic Volatility ..................................................... 27
2.2.3 Significance of Stock Price Informativeness ............................................... 30
2.2.4 Empirical Research on Stock Price Informativeness ................................... 36
2.2.4.1 Empirical Research – Cross-Country Analysis ........................................ 36
2.2.4.2 Empirical Research – Firm-Level Studies ............................................... 44
2.2.5 Inconsistent Findings ................................................................................... 55
2.3 Corporate Expenditure ........................................................................................ 56
2.3.1 Research and Development Expenditure ..................................................... 57
2.3.1.1 R&D Expenditure and Return ................................................................ 58
2.3.1.2 R&D Expenditure and Risk .................................................................... 62
2.3.1.3 Determinants of R&D Expenditure ........................................................ 64
2.3.2 Capital Expenditure ..................................................................................... 74
2.3.2.1 Significance of CAPEX .......................................................................... 74
2.3.2.2 Determinants of CAPEX ........................................................................ 78
2.3.3 Selling, General and Administrative Costs .................................................. 81
iv
2.3.3.1 Characteristics of SGA Costs ................................................................... 82
2.3.3.2 Value Relevance of SGA Costs ............................................................... 91
2.3.3.3 Determinants of SGA Costs ..................................................................... 93
2.4 Chapter Summary................................................................................................ 95
CHAPTER 3 THEORETICAL FRAMEWORK AND HYPOTHESES
DEVELOPMENT ....................................................................................................... 96
3.1 Introduction ......................................................................................................... 96
3.2 Stock Price Informativeness and Corporate Expenditure ................................... 97
3.3 The Role of Information Asymmetry ................................................................ 106
3.3.1 Firm Size .................................................................................................... 112
3.3.2 Analyst Following...................................................................................... 115
3.3.3 Bid-ask Spreads ......................................................................................... 119
3.4 Research Model ................................................................................................. 124
3.5 Chapter Summary.............................................................................................. 127
CHAPTER 4 RESEARCH METHODOLOGY ................................................... 128
4.1 Introduction ....................................................................................................... 128
4.2 Research Paradigm ............................................................................................ 129
4.3 Population Data ................................................................................................. 130
v
4.4 Sources of Secondary Data ............................................................................... 131
4.5 Variables Measurement ..................................................................................... 131
4.5.1 Dependent Variable ................................................................................... 131
4.5.2 Independent Variable ................................................................................. 133
4.5.3 Proxies of Information Asymmetry ........................................................... 136
4.5.4 Control Variables ....................................................................................... 138
4.5.4.1 Firm Characteristics Control Variables .................................................. 139
4.5.4.2 Corporate Governance Control Variables .............................................. 149
4.6 Model Specification .......................................................................................... 152
4.6.1 Main Model................................................................................................ 153
4.6.2 Robustness Tests ........................................................................................ 155
4.6.2.1 Change Model ........................................................................................ 156
4.6.2.2 Two-Stage Least Squares Regression .................................................... 157
4.7 Sample Selection Procedure .............................................................................. 167
4.8 Statistical Analyses ........................................................................................... 169
4.8.1 Data Screening ........................................................................................... 169
4.8.2 Data Analyses ............................................................................................ 172
4.8.2.1 Univariate Tests ..................................................................................... 172
4.8.2.2 Multivariate Tests .................................................................................. 172
4.8.2.3 Additional Tests ..................................................................................... 178
vi
4.9 Chapter Summary.............................................................................................. 179
CHAPTER 5 RESEARCH FINDINGS AND DISCUSSION ............................. 180
5.1 Introduction ....................................................................................................... 180
5.2 Univariate Results ............................................................................................. 181
5.2.1 Descriptive Statistics of Corporate Expenditure ........................................ 181
5.2.2 Pearson Correlations .................................................................................. 193
5.2.3 Descriptive Statistics by Firm Size ............................................................ 203
5.2.4 Descriptive Statistics by Analyst Following .............................................. 208
5.2.5 Descriptive Statistics by Bid-ask Spreads ................................................. 212
5.3 Hypothesis 1 - Stock Price Informativeness and Corporate Expenditure ......... 216
5.3.1 Multivariate Tests ...................................................................................... 217
5.3.1.1 Direction of Idiosyncratic Volatility ...................................................... 239
5.3.1.2 Summary of Findings ........................................................................... 250
5.3.2 Robustness Tests ........................................................................................ 251
5.3.2.1 Change Model ........................................................................................ 251
5.3.2.2 Two-Stage Least Squares Regression .................................................... 276
5.3.3 Summary of Findings – Hypothesis 1........................................................ 284
5.4 Hypotheses 2a to 2c – The Role of Information Asymmetry ........................... 288
5.4.1 Firm Size .................................................................................................... 288
vii
5.4.2 Analyst Following...................................................................................... 303
5.4.3 Bid-ask Spreads ......................................................................................... 318
5.4.4 Summary of Findings – Hypotheses 2a to 2c ............................................ 331
5.5 Additional Tests ............................................................................................... 332
5.5.1 Different Measures of Idiosyncratic Volatility .......................................... 333
5.5.2 Controlling for the Effect of Global Financial Crisis ................................ 334
5.5.3 Controlling for Time-Series and Cross-Sectional Correlations ................. 335
5.6 Chapter Summary.............................................................................................. 335
CHAPTER 6 CONCLUSION ................................................................................ 337
6.1 Introduction ....................................................................................................... 337
6.2 Summary of Key Research Findings ................................................................ 338
6.2.1 Stock Price Informativeness and Corporate Expenditure .......................... 340
6.2.2 The Role of Information Asymmetry ........................................................ 343
6.3 Contributions of the Study ................................................................................ 348
6.4 Limitations of the Study .................................................................................... 351
6.5 Recommendations for Future Research ............................................................ 354
6.6 Concluding Remarks ......................................................................................... 355
REFERENCES ............................................................................................................ 357
viii
ABSTRACT
This study examines how firm-level corporate expenditure represented by R&D, capital
expenditure (CAPEX) and selling, general and administrative (SGA) costs responds to
stock price informativeness. Using data from the United States public listed companies
for the years 2003 to 2009 covering the post-Sarbanes Oxley Act period, a current year‟s
stock price informativeness, proxied by idiosyncratic volatility, is found to be negatively
associated with the subsequent year‟s R&D expenditure and SGA costs. However, it
was observed there is no relationship between a current year‟s idiosyncratic volatility
and CAPEX level of the subsequent year.
Additional insights are revealed by using a change model which examines the
relationship between changes in a current year‟s stock price informativeness and
changes in the subsequent year‟s R&D and SGA expenditure. The results exhibit that
when firm-level stock price informativeness is strengthening, a change in the current
year‟s idiosyncratic volatility is positively related to changes in R&D and SGA
expenditure in the following year. When stock price informativeness is deteriorating,
firm managers do not react immediately to modify R&D and SGA costs. This
asymmetric cost response is attributable, in part, to the cost “stickiness” behaviour of
firm managers when it comes to changing R&D investment and SGA costs. These
managers may be reluctant to increase corporate expenditure when stock price
informativeness worsens as they need to assess whether the declining idiosyncratic
ix
volatility is temporary or permanent in nature. It is found that firm managers will only
react by intensifying R&D expenditure and SGA costs when it is critically compelling,
that is, when the relative idiosyncratic volatility (1-R2) drops significantly by 20 per cent.
This finding is consistent with the learning theory that managers learn about firms‟
fundamental values from the market‟s feedback and they incorporate this new private
information to make efficient corporate decisions in R&D expenditure and SGA costs.
However, this study finds that firm managers do not rely on the input from the capital
markets to make their capital investment decisions.
Given the significance of stock price informativeness in enhancing allocation of firms‟
scarce resources, this study provides useful insights and understanding on how firms
modify their corporate expenditure in response to changes in stock price informativeness.
Further analyses show that the relationship between stock price informativeness and
corporate expenditure is dependent on information asymmetry, proxied by firm size,
analyst following and bid-ask spreads. The analyses highlight that managers respond
and learn more quickly from the new firm-specific information available in small firms
and in firms with low analyst following as well as in firms with high bid-ask spread.
These findings are consistent with the learning theory and information asymmetry
theory. Consequently, firm managers react more “aggressively” by altering R&D
expenditure and SGA costs in the subsequent year as stock price informativeness of a
current year changes.
x
ORIGINALITY STATEMENT
“I hereby declare that this thesis contains no material which has been accepted for the
award of any other degree or diploma in any university or other institution and I hereby
affirm that to the best of my knowledge, the thesis contains no material previously
published or written by another person, except where due reference is made in the text
of this thesis.”
Signed: Mei Yee LEE
Dated: December 27, 2013
xi
ACKNOWLEDGEMENTS
I would like to acknowledge and extend my heartfelt gratitude to the following
individuals for their wisdom, encouragement, inspiration, understanding and guidance in
completing this thesis. Without them, this research would not have been possible.
First and foremost, I would like to express my greatest appreciation and gratitude to my
principle supervisor, Professor Ferdinand A. Gul for his mentoring and direction in
completing this thesis. Without his scholarly guidance, expert knowledge in the field of
accounting and finance and invaluable insights, this research would not have been
embarked. His intellectual advice and encouragement inspires me perpetually and it is
my privilege and honour to be his first PhD student in Monash Malaysia.
I am grateful to my associate supervisor, Professor Jeyapalan Kasipillai for his relentless
effort and motivation as well as invaluable advice particularly during the writing stage
of this thesis. His professional guidance, continuous assessments and generous support
have contributed greatly to the successful completion of this thesis. I am also indebted to
him for supervising me during the pursuance of my Postgraduate Diploma in Business
and Commerce degree in this University.
xii
I am indeed thankful to Professor Susela Devi Selvaraj for her inspiration, guidance and
continuous encouragement in the undertaking of this academic endeavour. I would wish
to thank my research panel members, namely, Associate Professor Gareth Leeves,
Professor Mahendhiran Nair and Dr Teh Chee Ghee for their insightful comments and
suggestions during the progressive reviews of this thesis. Not forgotten, my sincere
appreciation goes to expert advice rendered by Professor Bin Srinidhi, Angel Sung, Dr
Anthony Ng, Professor Min Chung-Ki, Associate Professor Santha Vaithilingam, Dr
Foo Yee Boon and Dr Karen Lai. I duly acknowledge the invaluable feedback received
from the chairs, discussants and participants during my presentations at the Jornal of
Contemporary Accounting and Economics (JCAE) Doctoral Consortium 2013 held in
Hong Kong and the Accounting and Finance Association of Australia and New Zealand
(AFAANZ) 2013 Conference in Perth.
I would like to extend my sincere gratitude to Monash Malaysia for the granting of
financial support for my postgraduate studies. I am thankful to two academics, namely
Norita Nasir and Shyamala Dhoraisingam for their guidance and encouragement during
my teaching tenure. I am also grateful to research and administrative staff for their kind
assistance and co-operation during my postgraduate journey, especially to Stephanie
Phang, Loke Bee Khum, Veenee Ooi and Parameswari Sithamparam. My sincere thanks
go to my doctorate colleagues for their encouragement during these challenging three
years, specifically to Pak Mei Sen, Hasuli Perera, Sanjuktha Choudhury and Mary Gu.
xiii
I am grateful to my parents for their understanding and support given to me to embark
and complete this doctoral journey. Most importantly, I am eternally grateful for the
patience and continued support given to me by my beloved husband, Hooi Yew Chong
and my two loving sons, Kai Cheng and Kai Xin throughout the duration undertaken to
complete my postgraduate degrees.
I thank you all for being with me in realising my dream. To all of you, I dedicate this
thesis.
xiv
CONFERENCE PAPERS PRODUCED FROM THESIS
1. Lee, M. Y., Gul, F. A. & Kasipillai, J. (2013). Stock price informativeness and its
impact on corporate strategies. Paper presented at the Accounting and Finance
Association of Australia and New Zealand (AFAANZ) Conference on 7-9 July,
2013 held at Perth, Australia.
2. Lee, M. Y. (2013). The impact of stock price informativeness on corporate
strategies. Paper presented at the Journal of Contemporary Accounting and
Economics (JCAE) Doctoral Consortium on 3 January, 2013 held at Hong Kong.
3. Lee, M. Y. (2012). The impact of stock price informativeness on corporate
strategies, corporate governance and firm performance. Paper presented at the 5th
Annual Doctoral Colloquium, Monash University Malaysia on 26-28 September,
2012 held at Pulau Tioman, Malaysia.
4. Lee, M. Y. (2011). The impact of stock price informativeness on corporate
strategies, corporate governance and firm performance. Paper presented at the 4th
Annual Doctoral Colloquium, Monash University Malaysia on 1-3 December,
2011 held at Malacca, Malaysia.
I am honoured to be invited as a discussant at the Concurrent Paper Session of the
forthcoming JCAE Symposium held on January 3-4, 2014 in Kuala Lumpur, Malaysia.
The title of the paper is “Impact of International Financial Reporting Standards on Stock
Price Synchronicity in Asian Countries”.
xv
LIST OF TABLES
Table Contents Page
2.1 Value of Relative Idiosyncratic Volatility (1-R2) 26
4.1 Definition of Variables 162
4.2 Sample Selection Procedure 167
5.1 US Corporate Expenditure of Years 2004-2010 (in USD million) 182
5.2 Descriptive Statistics – R&D Expenditure 186
5.3 Descriptive Statistics – Capital Expenditure 188
5.4 Descriptive Statistics – Selling, General and Administrative Costs 190
5.5 Pearson Correlations – R&D Expenditure 194
5.6 Pearson Correlations – Capital Expenditure 198
5.7 Pearson Correlations – Selling, General and Administrative Costs 201
5.8 Descriptive Statistics – R&D Expenditure by Firm Size
204
5.9 Descriptive Statistics – Capital Expenditure by Firm Size 205
5.10 Descriptive Statistics – Selling, General and Administrative Costs
by Firm Size
206
5.11 Descriptive Statistics – R&D Expenditure by Analyst Following
209
5.12 Descriptive Statistics – Capital Expenditure by Analyst Following 210
5.13 Descriptive Statistics – Selling, General and Administrative Costs
by Analyst Following
211
5.14 Descriptive Statistics – R&D Expenditure by Bid-ask Spreads
213
5.15 Descriptive Statistics – Capital Expenditure by Bid-ask Spreads 214
xvi
LIST OF TABLES (Continued)
Table Contents Page
5.16 Descriptive Statistics – Selling, General and Administrative Costs
by Bid-ask Spreads
215
5.17 Effect of Stock Price Informativeness on R&D Expenditure (H1) 219
5.18 Effect of Stock Price Informativeness on CAPEX (H1) 226
5.19 Effect of Stock Price Informativeness on SGA Costs (H1) 233
5.20 Effect of Stock Price Informativeness on R&D Expenditure (H1) –
by Direction of Idiosyncratic Volatility‟s Movement
240
5.21 Effect of Stock Price Informativeness on CAPEX (H1) – by
Direction of Idiosyncratic Volatility‟s Movement
244
5.22 Effect of Stock Price Informativeness on SGA Costs (H1) – by
Direction of Idiosyncratic Volatility‟s Movement
247
5.23 Changes in R&D Expenditure Following Changes in Stock Price
Informativeness
253
5.24 Changes in CAPEX Following Changes in Stock Price
Informativeness
263
5.25 Changes in SGA Costs Following Changes in Stock Price
Informativeness
268
5.26 Results of Two-Stage Least Squares Regressions 279
xvii
LIST OF TABLES (Continued)
Table Contents Page
5.27 Effect of Stock Price Informativeness on R&D Expenditure – by
Firm Size
289
5.28 Effect of Stock Price Informativeness on CAPEX – by Firm Size
(H2a)
295
5.29 Effect of Stock Price Informativeness on SGA Costs – by Firm Size
(H2a)
298
5.30 Effect of Stock Price Informativeness on R&D Expenditure – by
Analyst Following (H2b)
304
5.31 Effect of Stock Price Informativeness on CAPEX – by Analyst
Following (H2b)
310
5.32 Effect of Stock Price Informativeness on SGA Costs – by Analyst
Following (H2b)
313
5.33 Effect of Stock Price Informativeness on R&D Expenditure – by
Bid-ask Spreads (H2c)
319
5.34 Effect of Stock Price Informativeness on CAPEX – by Bid-ask
Spreads (H2c)
324
5.35 Effect of Stock Price Informativeness on SGA Costs – by Bid-ask
Spreads (H2c)
327
6.1 Summary of Research Objectives, Corresponding Hypotheses and
Research Findings
339
xviii
LIST OF FIGURES
Figure Contents Page
1.1 Structure of the Thesis 17
2.1 Stock Return Synchronicity in Various Countries in 1995 37
2.2 Asymmetric Behaviour of “Sticky” Costs 86
3.1 Research Model 124
5.1 Trend Analysis of US Corporate Expenditure from 2004-2010 184
5.2 Association between Current Year‟s Idiosyncratic Volatility and
R&D Expenditure of the Subsequent Year
242
5.3 Association between Current Year‟s Idiosyncratic Volatility and
SGA Costs of the Subsequent Year
249
5.4 Association between Changes in Current Year‟s Idiosyncratic
Volatility and Changes in R&D Expenditure of Subsequent Year
when Idiosyncratic Volatility Increases
259
5.5 Association between Changes in Current Year‟s Idiosyncratic
Volatility and Changes in R&D Expenditure of Subsequent Year
when 1-R2 Drops 20%
260
5.6 Association between Changes in Current Year‟s Idiosyncratic
Volatility and Changes in SGA Costs of Subsequent Year when
Idiosyncratic Volatility Increases
273
5.7 Association between Changes in Current Year‟s Idiosyncratic
Volatility and Changes in SGA Costs of Subsequent Year when 1-
R2 Drops 20%
274
xix
LIST OF ABBREVIATIONS
CAPEX Capital Expenditure
CAPM Capital Asset Pricing Model
CEO Chief Executive Officer
CMG Capital Market Governance
CRSP Centre for Research in Security Prices
GAAP Generally Accepted Accounting Principles
GDP Gross Domestic Product
IAS International Accounting Standards
I/B/E/S Institutional Brokers‟ Estimate System
IFRS International Financial Reporting Standards
NYSE New York Stock Exchange
OLS Ordinary Least Squares
PIN Probability of Informed Trading
PLCs Public Listed Companies
R&D Research and Development
ROA Return on Assets
ROE Return on Equity
ROI Return on Investments
SGA Selling, General and Administrative
SIC Standard Industrial Classification
xx
LIST OF ABBREVIATIONS (Continued)
UK United Kingdom
US United States
VIF Variance Inflation Factor
2SLS Two-Stage Least Squares
1
CHAPTER 1
INTRODUCTION
1.1 Background of the Research
A recent strand of research has examined issues related to stock price informativeness.
These studies are motivated by the belief that high stock price informativeness is
associated with more efficient allocation of capital (Durnev, Morck & Yeung, 2004;
Chen, Goldstein & Jiang, 2007; Gul, Srinidhi & Ng, 2011b; Xu, Chan, Jiang & Yi,
2013). When firm-specific information is conveyed to the capital markets in an accurate
and timely manner, firms‟ stock prices are tracking closer to their fundamental values,
thereby reflecting a more efficient market. This phenomenon enables the appropriate
pricing of capital according to its different uses and provides meaningful feedback to
firm managers when stock prices move in response to their investment decisions
(Durnev, Morck, Yeung & Zarowin, 2003). Consequently, scarce resources can be
channelled to its highest value use or are withdrawn from sectors with poor financial
performance (Tobin, 1984). Furthermore, higher stock price informativeness are
associated with better management decisions (Durnev et al., 2004; Chen et al., 2007;
Frésard, 2012) and provides more information about a firm‟s future earnings (Durnev et
al., 2003; Jiang, Xu & Tong, 2009).
2
Technically, all private information is integrated into stock prices by the end of the
trading day (Kyle, 1985). Stock price informativeness is measured by idiosyncratic
volatility1 (Morck, Yeung & Yu, 2000; Durnev et al., 2004; Jin & Myers, 2006; Ferreira
& Laux, 2007). Idiosyncratic volatility measures the capitalization rate of firm-specific
information into stock prices through informed trading. Idiosyncratic volatility is higher
when a stock return is less correlated with market and industry returns, that is, when it
has lesser co-movement with the market (French & Roll, 1986; Roll, 1988). Thus,
idiosyncratic volatility is equal to the logistic transformation of a firm‟s 1-R2 value
(Ferreira & Laux, 2007) and is the inverse of stock price synchronicity proxied by R2
value (Morck et al., 2000; Jin & Myers, 2006).
Drawing from the learning hypotheses2 (or learning theory) of Dow and Gorton (1997)
and Subrahmanyam and Titman (1999), managers learn about the fundamental value of
firms from their own stock prices and incorporate this new private information in
allocating corporate resources (Luo, 2005; Chen et al., 2007; Frésard, 2012).
Information flows from firms to capital markets and also from the capital markets to
firms through stock prices (Dow & Gorton, 1997; Dye & Sridhar, 2002). Only new
1Idiosyncratic volatility is also referred to as firm-specific stock return variation or price non-
synchronicity (Durnev et al., 2004; Frésard, 2012). It is used interchangeably with the term “stock price
informativeness” in this thesis.
2 Some authors, for example Foucault and Frésard (2012) and Fresard (2012) name it as „managerial
learning channel‟. In this thesis, the term “learning theory” is used interchangeably with learning
hypothesis.
3
firm-specific information is able to influence managerial decisions whilst information
that managers are already aware of would not have any impact on their corporate
expenditure decisions (Chen et al., 2007). The new information that firm managers may
not know includes growth opportunities, future demand for a firm‟s products, market
competition, relationships with diverse stakeholders and financing opportunities (Dow
& Gorton, 1997; Subrahmanyam & Titman, 1999). As such, informed stock prices
convey information of market‟s assessment on firms‟ potentials and provide meaningful
signals to firm managers about the quality of their decisions. Managers can then use this
firm-specific information that they have yet to possess to improve the efficiency of their
corporate decisions, thus enhancing firm value (Chen et al., 2007; Frésard, 2012). In this
thesis, it is argued that stock price informativeness motivates firm managers to initiate
changes to their corporate expenditure in three specific areas, namely, research and
development (R&D) expenditure, capital expenditure (CAPEX) and selling, general and
administrative (SGA) costs. This thesis examines the association between a current
year‟s stock price informativeness and the subsequent year‟s corporate expenditure.
Firms‟ “information environment” has a role in deciding the extent of the agency
conflict between managers and investors. At various times, managers possess superior
firm-specific information, compared to investors (Armstrong, Guay & Weber, 2010),
and this results in problems of adverse selection (hidden information) as well as moral
hazard (hidden action) (Arrow, 1985). The former causes a failure to identify the true
value of firms (Akerlof, 1970; Healy & Palepu, 2001) while the latter results in earnings
4
management (Richardson, 2000) and non-disclosure of information (Verrecchia, 2001)
to meet the personal interests of managers. Information asymmetry also occurs among
investors and creates adverse selection problem when informed investors3 trade on firm-
specific information while uninformed investors depend more on publicly available
information (Brown & Hillegeist, 2007). Uninformed traders demand a discount in
buying firm shares, especially in illiquid markets, to price-protect themselves against
potential losses from trading with informed traders (Myers & Majluf, 1984; Merton,
1987). This results in a lower amount of share issuance proceeds and a higher cost of
capital (Bhattacharya & Spiegel, 1991; Leuz & Verrecchia, 2000). The unequal
dissemination of information among different parties affects the efficiency and
transparency of the capital markets (Armstrong et al., 2010). It is therefore important to
reduce information asymmetry, as superior information about firms helps to boost
market liquidity (Bushman & Smith, 2001) and improve the allocation of scarce
resources, thereby resulting in long-term economic growth (Merton, 1987).
Prior research shows that information moves faster not only in large firms (Atiase, 1985;
Bhushan, 1989a), but also in firms with high analyst coverage (Hong, Lim & Stein,
3 There are two types of investors: informed and uninformed (Grossman, 1976). According to Grossman
(1976), informed traders invest their time and resources to learn about a firm and thereby making their
investment decisions. Examples of informed market participants are institutional investors, insiders and
financial analysts (Piotroski & Roulstone, 2004). Uninformed traders, on the other hand, do not engage in
information collection activities but instead learn by observing the movement of the stock prices to make
their judgement about the true future price of a firm.
5
2000; Frankel & Li, 2004) and in firms with low bid-ask spreads (Welker, 1995;
Richardson, 2000). As such, these firms are expected to be associated with low
information asymmetry. Drawing ideas from the learning theory, firm managers learn
new private information that they have yet to possess from stock prices, in order to make
appropriate corporate decisions (Dow & Gorton, 1997; Subrahmanyam & Titman, 1999).
While large firms generate huge amounts of public information through public
announcements and financial disclosures, this information has already been utilised by
their managers in past investment decisions. As such, the information gathered does not
have any effect on firms‟ corporate expenditure decisions. Managerial learning is also
expected to be lower for firms with higher analyst following since most of the
information produced by analysts is derived from firm managers (Agrawal, Chadha &
Chen, 2006) or has already been factored into managers‟ past investment decisions
(Chen et al., 2007) and hence, is unlikely to affect managerial decisions. Analysts do not
seem to produce new private information as most of the information they generate
reveals greater industry and market-level information (Piotroski & Roulstone, 2004);
they instead bring in more uninformed or noise trading to the stocks (Easley, O'Hara &
Paperman, 1998). This reduces the private information content in stock prices (Chen et
al., 2007) and discourages both managerial learning and prompt firms‟ reaction in
making corporate decisions.
On the other hand, empirical findings show that more private information is produced
for small firms (Chen et al., 2007; Bakke & Whited, 2010), firms with low analyst
6
following (Chen et al., 2007) and firms with high bid-ask spreads (Chan, Hameed &
Kang, 2013). Managers of firms with a greater extent of new firm-specific private
information are induced to learn more quickly and react more “aggressively” by altering
corporate expenditure when stock price informativeness changes. This thesis
investigates whether the relationship between a current year‟s stock price
informativeness and the subsequent year‟s corporate expenditure is dependent on
information asymmetry. Three proxies of information asymmetry, namely, firm size,
analyst following and bid-ask spreads, are employed in this study.
This introductory chapter provides background information of the thesis. The remaining
sections are organized as follows. Section 1.2 deliberates the research problem
statements. This is followed by the identification of research objectives in Section 1.3
and the elaboration of the research questions in Section 1.4. Section 1.5 presents an
overview of the research methods applied and briefly outlines the study‟s findings.
Section 1.6 delineates the significance of the study, while Section 1.7 presents the
structure of this thesis. A summary of this chapter is provided in Section 1.8.
1.2 Research Problem
Prior empirical research has established a connection between stock price
informativeness and corporate investment decisions (Durnev et al., 2004; Chen et al.,
2007). These studies, however, do not provide any direct guidance as to the relationship
7
between the levels of stock price informativeness and corporate expenditure. The links
of stock price informativeness to corporate expenditures such as R&D expenditure and
SGA cost also remains an unexplored area of study. This study bridges the gap by
investigating whether stock price informativeness motivates firm managers to change
corporate expenditure as proxied by R&D expenditure, CAPEX and SGA costs.
In view of the significance of stock price informativeness and its possible impact on
corporate expenditure decisions through the learning theory, it is pertinent to address the
relevant issues in examining the relationship between stock price informativeness and
corporate expenditure. For example, it would be useful to ascertain how this observed
relationship differs when idiosyncratic volatility strengthens or weakens. There is also
some concern as to how much managers would adjust corporate expenditure decisions
when stock price informativeness changes. This is because, there may not be
proportionate changes in the subsequent year‟s corporate expenditure arising from
changes in a current year‟s idiosyncratic volatility. As suggested by prior studies
(Anderson, Banker & Janakiraman, 2003; Balakrishnan & Gruca, 2008), asymmetric
cost behaviour is evident in corporate expenditure especially SGA costs.
The issue of information asymmetry is pivotal in finance theory because it is essential to
improving the information environment of firms to enhance the efficiency and
transparency of the capital markets. It is useful to unveil whether information
asymmetry plays a role in the association between stock price informativeness and
8
corporate expenditure. While small firms, firms with low analyst following and firms
with high bid-ask spreads are expected to be linked to higher information asymmetry,
empirical studies show that more private information is produced for these firms (Chen
et al., 2007; Bakke & Whited, 2010). A greater volume of private information enables
better managerial learning of information that managers have yet to possess from the
stock prices of firms. Consequently, managers of these firms are induced to respond
more enthusiastically by changing corporate expenditure in the subsequent year when
stock price informativeness of a current year changes. This study, therefore, investigates
whether the relationship between stock price informativeness of a current year and
corporate expenditure in the subsequent year is dependent on information asymmetry.
As mentioned in item 1.1, three proxies of information asymmetry, namely, firm size,
analyst following and bid-ask spreads are employed in this study.
1.3 Research Objectives
The objective of this research is two-fold. First, it investigates how firm-level corporate
expenditure responds to stock price informativeness. Second, this study seeks to assess
whether information asymmetry plays a role in the relationship between stock price
informativeness and corporate expenditure.
9
1.4 Research Questions
In order to achieve the objectives of this study, the following research questions are
formulated:
a) Does stock price informativeness induce changes in a firm‟s corporate
expenditure in the subsequent year?
b) Is the association between a current year‟s stock price informativeness and the
subsequent year‟s corporate expenditure dependent on information asymmetry?
1.5 Overview of Research Methods and Findings
Data from United States (US) public listed companies (PLCs) for the years 2003 to 2009
(inclusive of both years), covering the post Sarbanes Oxley Act period, is evaluated in
this study. Discretionary corporate expenditures such as R&D expenditure, CAPEX and
SGA costs are examined due to their significant contribution to total corporate
expenditure (inclusive of operating expenses and capital expenditure) of sample firms in
this study. For example, SGA costs make up 32 per cent of total corporate expenditure
while R&D expenditure and capital expenditure comprise 16 and eight per cent of total
corporate expenditure respectively. These three types of corporate expenditure represent
essential strategic tools that firms use to improve their performance. They have also
been widely employed in the strategic management literature (McAlister, Srinivasan &
10
Kim, 2007; Zhang & Rajagopalan, 2010; Srinivasan, Lilien & Sridhar, 2011; Pierce &
Aguinis, 2013).
This study adopts a lead-lag approach as there might be a time delay in the reaction of
stock prices to managerial learning and corporate expenditure decisions. The assumption
of Fama‟s (1970) Efficient Market Hypothesis4 on the immediate dissemination of all
available information and the instant reactions of investors does not always hold true
(Merton, 1987).
Hypothesis 1 of the study posits that stock price informativeness of the current year is
negatively associated with corporate expenditure in the subsequent year, ceteris paribus.
It is predicted that a low level of stock price informativeness is more likely to induce
firm managers to intensify their corporate expenditure to provide positive signals about
firms‟ prospects and future cash flows. This positive news is expected to increase
investors‟ confidence in view of the significance of corporate expenditure such as R&D
expenditure, CAPEX and SGA costs to firm performance as suggested by prior studies.
In contrast, when stock price informativeness is at a high level where investors are well
4 The Efficient Market Hypothesis posits that stock market prices are rational and reflect a high quality of
information about a firm‟s future expected profits. Stock prices may be modelled as a random walk as
they are driven by expectation on firms‟ future profits. Changes in these expectations are incorporated
fully in stock prices with immediate effect (Fama, 1970).
11
informed on firms‟ efficient resource allocation and potential earnings, firm managers
are most likely to maintain a relatively low level of corporate expenditure.
This study finds a significant negative association between a current year‟s stock price
informativeness and the subsequent year‟s R&D expenditure and SGA costs after
considering endogeneity. The impact of stock price informativeness on the level of the
subsequent year‟s R&D expenditure and SGA costs is greater (lower) when the
idiosyncratic volatilities of firms are weakening (strengthening) from the previous year.
It is observed that there is no relationship between a current year‟s stock price
informativeness and CAPEX in the subsequent year.
A “change model”5 is used to investigate the relationship between changes in a current
year‟s stock price informativeness and changes in the subsequent year‟s corporate
expenditure. The results reveal a positive relationship between a change in the current
year‟s idiosyncratic volatility and changes in subsequent year‟s corporate expenditure
represented by R&D expenditure and SGA costs when firm-level stock price
informativeness is improving from the previous year. These findings indicate that firm
managers are likely to learn positive signals from the stock markets as stock price
informativeness strengthens and thereby escalating R&D expenditure and SGA costs in
5 Change model is used to address the issue of reverse causality. In this study, the direction of impact is
specified from a current year‟s stock price informativeness to the subsequent year‟s corporate expenditure.
12
the subsequent year to capture a higher contribution of these corporate expenditures to
shareholders‟ wealth in the long-term.
When stock price informativeness is deteriorating when compared to the previous year,
there is no immediate reaction from firm managers to modify R&D expenditure and
SGA costs. This asymmetric cost response is partly caused by the cost “stickiness”
behaviour of managers. These managers may need to consider whether the phenomenon
of reducing stock price informativeness is temporary or long-term. Thus, they are
unwilling to change corporate expenditure as idiosyncratic volatility declines. This study
observes that firm managers respond by increasing R&D expenditure and SGA costs
only when it is crucial, that is, when the relative idiosyncratic volatility (1-R2) reduces
significantly by 20 per cent. The test of change model, however, suggest that changes in
a current year‟s idiosyncratic volatility is insignificantly related to changes in CAPEX in
the subsequent year regardless of the directions of movement in stock price
informativeness
The findings of asymmetric cost response in R&D and SGA expenditure are consistent
with the cost “stickiness” behaviour demonstrated in the seminal paper by Anderson et
al. (2003). In their study, managers make asymmetric cost adjustments when firms‟
sales revenue moves in different directions as a result of their expectation on future sales
demand. Other studies analyse cost “stickiness” behaviour under different business
environments, for example, Balakrishnan, Petersen and Soderstrom (2004) suggest cost
13
stickiness is dependent on capacity utilisation. Balakrishnan and Gruca (2008) identify
that “cost stickiness” is more prominent in firms‟ core activities compared to their
auxiliary services while Dalla Via and Perego (Forthcoming) examine cost “stickiness”
of small and medium-sized firms.
These findings on R&D and SGA expenditure are consistent with the learning theory
that highlights managers learn valuable private information from firms‟ stock prices and
integrate this new information to improve their corporate expenditure decisions. The
results also indicate that stock price informativeness in a current year is not related to
CAPEX in the subsequent year suggesting that private information obtained from the
capital markets is not a significant determinant of firms‟ capital decisions. Firm
managers are most likely to rely on other factors such as availability of internal cash
flow as well as assessment of risk and return of capital projects in planning firms‟
capital investment.
Hypotheses 2a to 2c posit that the negative associations between a current year‟s stock
price informativeness and subsequent year‟s corporate expenditure is likely to be
stronger in small firms, firms with low analyst following and firms with high bid-ask
spreads respectively. These firms are expected to be linked to higher information
asymmetry. Empirical studies, however, show that more private information is produced
for these firms (Chen et al., 2007; Bakke & Whited, 2010). A greater volume of private
14
information in these firms enables better managerial learning of information that
managers have yet to possess from firms‟ stock prices.
This study finds that the inverse relationship between a current year‟s stock price
informativeness and the subsequent year‟s corporate expenditure represented by R&D
expenditure and SGA costs is stronger for firms with high information asymmetry,
proxied by small firm size, low analyst following and high bid-ask spreads. The findings
also demonstrate that the effect of stock price informativeness on R&D expenditure and
SGA costs is greater (smaller) in these firms when their idiosyncratic volatilities are
weakening (strengthening). These findings are in line with the ideas of the learning
theory and information asymmetry theory (Chen et al., 2007; Bakke & Whited, 2010).
Firm managers of these firms learn from the new firm-specific information available and
are induced to respond more enthusiastically by making changes to firms‟ R&D and
SGA expenditure in the subsequent year when a current year‟s stock price
informativeness changes.
1.6 Significance of the Study
This study offers several essential insights on the multi-faceted linkages between
finance, accounting, strategic management and cost management. First, while existing
empirical studies examine the effects of the learning theory on corporate investment
(Durnev et al., 2003; Durnev et al., 2004; Chen et al., 2007; Foucault & Frésard, 2012)
15
and cash savings (Frésard, 2012), this study provides additional empirical evidence on
how stock prices influence other fundamental corporate actions. It does so by examining
three key corporate expenditures, namely, R&D expenditure, CAPEX and SGA costs.
Adopting the same theoretical principles of the learning theory, this study argues that
firm managers extract valuable private information from the stock prices of their own
firms and make necessary strategic changes in R&D and SGA costs to uphold firms‟
competitive advantage and survival. The results also indicate that stock price
informativeness in a current year is not associated with CAPEX in the subsequent year,
suggesting that capital investment decisions are not dependent on feedback derived from
the capital markets.
Second, this study presents direct evidence of how a current year‟s stock price
informativeness has an effect to the level of corporate expenditure and its changes in the
subsequent year. Hence, this research provides useful understanding of how firms adjust
their corporate expenditure in the subsequent year as stock price informativeness
changes.
Third, this study expands management accounting research by providing new insights
into the outcomes of managers‟ asymmetric cost response in determining corporate
expenditure. The results of this study display cost “stickiness” behaviour of firm
managers when it comes to changing R&D investment and SGA costs in response to
strengthening or weakening idiosyncratic volatility. In this regards, this study
16
contributes to the existing literature on “sticky cost” (Anderson et al., 2003; Weiss, 2010;
Chen, Lu & Sougiannis, 2012b; Kama & Weiss, 2013).
Fourth, this research adds to the literature on information asymmetry by assessing its
role in the relationship between stock price informativeness and corporate expenditure
using three specific proxies: firm size, analyst following and bid-ask spreads. The
outcome is that the relationship between a current year‟s stock price informativeness and
corporate expenditure in the following year is stronger in small firms and in firms with
low analyst following as well as in firms with high bid-ask spreads, hence further
highlighting the significance of this study.
1.7 Structure of the Thesis
This thesis consists of six chapters and it is outlined in Figure 1.1.
17
Figure 1.1 Structure of the Thesis
Chapter 1
Introduction
Chapter 2
Literature Review
Chapter 3
Theoretical Framework & Hypotheses Development
Chapter 4
Research Methodology
Chapter 5
Research Findings &
Discussions
Chapter 6
Conclusion
18
A summary of each chapter is presented as follows:
Chapter 1: Introduction
Chapter 1 begins with a brief background of this research that relates stock price
informativeness to corporate expenditure as well as to information asymmetry. This is
followed by the identification of: research problem statements, research objectives and
the study‟s research questions. Next, an overview of the research methods employed by
this study and the empirical findings obtained from it are presented. The significance of
the research is then delineated. The chapter concludes with an outline of the thesis
structure and a summary of the chapter.
Chapter 2: Literature Review
Chapter 2 evaluates the extant literature on stock price informativeness and corporate
expenditure. The in-depth evaluation of the literature on stock price informativeness
covers its characteristics, measurement and significance, followed by the relevant
empirical evidence provided by both cross-country and firm-level studies. The relevant
literature on R&D expenditure, CAPEX and SGA costs is also presented. It is centred on
their characteristics, benefits and determinants. A summary of the chapter is then
provided.
19
Chapter 3: Theoretical Framework and Hypotheses Development
Chapter 3 outlines the theoretical framework of the current study and formulates the
hypotheses connecting stock price informativeness, corporate expenditure and
information asymmetry. This chapter also presents an overview of the empirical
literature on the theories underpinning the current study. Next, four hypotheses are
developed to predict the association between stock price informativeness and corporate
expenditure as well as to determine whether the relationship is dependent on information
asymmetry. A research model is then presented, followed by a summary of the chapter.
Chapter 4: Research Methodology
Chapter 4 outlines the research methodology employed to examine the hypotheses
developed for this study. The research paradigm adopted for this study is elaborated and
then, the principal population data, sources of secondary data and variables
measurement are outlined. This is followed by a description of the model specification,
as well as the selection procedure used for choosing the sample. An overview of
statistical methodology used and a chapter summary are provided.
Chapter 5: Research Findings and Discussion
Chapter 5 presents the research findings, followed by a discussion of the results of the
current study. It begins with the description of the univariate results. Multivariate results
are then presented, followed by a discussion of the results with regards to the association
between stock price informativeness and corporate expenditure, as well as how this
20
association is dependent on information asymmetry. A chapter summary is then
provided.
Chapter 6: Conclusion
Chapter 6 presents a summary of the research findings in relation to the study‟s
objectives and hypotheses, and is followed by implications of the research. The study‟s
contributions to existing literature are highlighted and this is followed by an outline of
the limitations of the study, recommendations for future research directions and
concluding remarks of the thesis.
1.8 Chapter Summary
This chapter provides the background information related to stock price informativeness,
corporate expenditure and information asymmetry. It further identifies the research
problem statements, research objectives and research questions. An overview of the
research methods used in the study and the empirical findings derived from it are then
presented, followed by an outline of the study‟s significance. The organization of the
thesis is also outlined.
The next chapter presents a review of the existing literature on stock price
informativeness and corporate expenditure, from which the current state of knowledge is
identified.
21
CHAPTER 2
LITERATURE REVIEW
2.1 Introduction
The previous chapter (Chapter 1) provides the background information of the thesis
which constitutes the foundation of this study. It specifies the research problem
statements, outlines the research objectives and states the research questions. It also
provides a snapshot of the research methods used and reports on the empirical findings,
followed by an outline of the significance of the study and finally presents the structure
of this thesis.
This chapter (Chapter 2) evaluates the relevant literature of stock price informativeness
and corporate expenditure. The review begins with an overview of literature on
informativeness of stock prices in Section 2.2 in order to comprehend its characteristics,
measurement and significance, followed by both cross-country and firm-level evidence
of the empirical research in stock price informativeness. Section 2.3 deliberates the
characteristics, benefits and determinants of corporate expenditure, particularly in the
area of research and development (R&D) expenditure, capital expenditure (CAPEX) and
22
selling, general and administrative (SGA) costs. Section 2.4 provides the summary of
this chapter.
2.2 Stock Price Informativeness
Stock price informativeness is perceived as uncertainty-reduction in the value of stocks
due to knowledge of the price (Kyle, 1985). Durnev et al. (2003) describe it as “the
extent of information that stock prices contain about future earnings”. Variation in stock
returns is mainly caused by public news and is also attributable to the availability of
firm-specific private information by investors (Ferreira & Laux, 2007). Public
announcements and financial disclosures are integrated into stock prices directly while
firm-specific information gathered by informed investors is embedded into stock prices
through the process of trading (Piotroski & Roulstone, 2004; Gul et al., 2011b).
Grossman and Stiglitz (1980) predict that higher intensity of informed trading is due to a
lower cost of private information. This phenomenon results in higher firm specific
variation and consequently more informative stock pricing (Durnev et al., 2004).
2.2.1 Idiosyncratic Volatility
Stock price informativeness is measured using idiosyncratic volatility. Morck et al.
(2000) observe a long-term rise in idiosyncratic variation of US stock returns from 1926
to 1995 while Campbell, Lettau, Malkiel and Xu (2001) document a substantial upward
pattern in firm-level idiosyncratic volatility relative to market volatility for the years
23
1962 to 1997. Xu and Malkiel (2003) also show that idiosyncratic volatilities of
individual stocks increased during the 1980s and 1990s.
The measure of idiosyncratic volatility was first proposed by Roll (1988). He argues that
the extent to which stocks prices move together is determined by the relative amount of
firm-level and market-level information integrated into stock prices. Roll (1988) uses
Capital Asset Pricing Model (CAPM) to regress firm returns on market returns and
considers the cross-sectional R2
value, being the coefficient of determination, as a
measure of the explanatory power. He observes that a significant portion of stock return
variation is not due to market-wide and industry factors as the market model of his US
sample using 1982-1987 data reports an average adjusted R2
value of only about 35%
using monthly data and a low 20% relying on daily returns.
Roll (1988) further expects an increase in R2 value in asset pricing regression models
when excluding all identifiable public news released by comparing regression models
with and without observations of firms‟ events reported in the financial press.
Nevertheless, he finds no significant improvement in the R2 value, indicating that the
stock return variation is not explained by public announcements. Roll (1988) therefore
suggests that the firm-specific stock price movement noted in the US stock prices is
either attributable to firm-specific private information, or “occasional frenzy” which is
not associated with the firm‟s fundamentals. French and Roll (1986) find that US stock
returns for the years 1963 to 1982 are more volatile during the trading period compared
24
to non-trading period. This is because higher variances occur during the trading period
are attributable to firm-specific information.
Whether stock price movement is explained by firm-specific private information or
“occasional frenzy”, i.e., noise constitutes an empirical issue. However, the first
conjecture of Roll (1988) that firm-specific return variation reflects arbitrageurs trading
on private information is strongly supported by a large body of empirical work and will
be elaborated in detail in item 2.2.4.
A firm‟s stock returns convey new market-level and firm-specific information (Morck et
al., 2000). Idiosyncratic volatility is the component of a firm‟s stock return variation not
explained by market return. It is computed based on the correlation between stock‟s
return and the return of the corresponding industry and that of the market. The rationale
is if a firm‟s stock return is strongly correlated with the market returns, i.e., co-move
with the market, the stock prices are less likely to reflect firm-specific information
(French & Roll, 1986; Roll, 1988). Idiosyncratic volatility would be higher when the
stock return is less correlated with market returns, indicating higher stock price
informativeness (Morck et al., 2000). Thus, idiosyncratic volatility represents the
impounding of firm-specific information into stock prices by informed trading instead of
using public information (Roll, 1988). Idiosyncratic volatility is the logistic
transformation of the value of 1-R2 of a firm (Ferreira & Laux, 2007). It is also the
25
inverse of stock price synchronicity proxied by R2 value (Morck et al., 2000; Jin &
Myers, 2006).
The value of R2 is measured for each firm-year in the sample. By construction, low
values of R2 or alternatively, high values of 1-R
2 (relative idiosyncratic volatility)
indicate that firms‟ stock returns are not closely tied to market and industry returns,
thereby reflecting relatively high firm-specific information (Morck et al., 2000; Stowe &
Xing, 2011).
Table 2.1 summarises the mean and maximum values of relative idiosyncratic volatility
(1-R2) presented by empirical research in the area of stock price informativeness for US
and Chinese companies in the years 2000 to 2013.
26
Table 2.1 Value of Relative Idiosyncratic Volatility (1-R2)
Empirical Research Sample
Size
Years
Covered
1-R2
Mean
1-R2
Max
US Companies
Morck et al. (2000) 7,241 1995 0.979 N.A.
Wurgler (2000) 868 1963-1995 0.874 N.A.
Durnev et al. (2004) 4,029 1990-1992 0.781 0.960
Chen et al. (2007) 68,277 1981-2001 0.830 1.000
Ferreira and Laux (2007) 161,691 1990-2001 0.854 1.000
Fernandes and Ferreira (2009) N.A. 1980-2003 0.807 N.A.
Hutton, Marcus and Tehranian
(2009)
40,882 1991-2005 0.750 N.A
Dasgupta, Gan and Gao (2010) 89,010 1976-2004 0.870 N.A
Ferreira, Ferreira and Raposo
(2011)
11,755 1990-2001 0.738 0.917
Crawford, Roulstone and So
(2012)
613,111 1996-2006 0.890 N.A
Frésard (2012) 88,501 1970-2006 0.790 N.A.
An and Zhang (2013) 79,932 1987-2010 0.842 N.A.
Chan et al. (2013) 26,853 1989-2008 0.777 N.A.
Chinese Companies
Gul, Kim and Qiu (2010) 6,120 1996-2003 0.546 N.A
Gul, Cheng and Leung (2011a) 1,227 2001-2005 0.552 0.988
Xu et al. (2013) 10,326 2003-2010 0.471- 0.537* N.A N.A. - Not available
* A range of mean values is reported.
The regression models applied to calculate the value of relative idiosyncratic volatility
(1-R2) for each of the empirical research listed in Table 2.1 may not be similar, thus
comparisons should be done with caution. The mean value of relative idiosyncratic
volatility (1-R2) of US companies ranges from 0.738 to 0.979 for varying periods
27
covered in these studies. While the maximum value of relative idiosyncratic volatility
(1-R2) is unavailable for most research, it is reported as 1.000 in both studies by Chen et
al. (2007) and Ferreira and Laux (2007), indicating that 100% of the firms‟ stock return
variations are explained by firm-specific information. Compared to US companies,
Chinese companies achieved lower levels of relative idiosyncratic volatility (1-R2).
These results portray higher co-movement between individual firms‟ stock returns with
the market and industry returns among Chinese firms, thereby signifying a lower level
of stock price informativeness.6
2.2.2 Measurement of Idiosyncratic Volatility
Stock price changes are explained by general systematic variation, industry influences
and firm-specific events. Thus, the volatility of a stock‟s return is made up of systematic
risk (as explained by market and industry return) and idiosyncratic volatility (referred to
as unsystematic risk). Following Ferreira and Laux (2007) and Gul et al. (2011b),
idiosyncratic volatility in this study is measured based on a regression estimation of
stock returns for each firm for all fiscal years on the returns of the market index as
follows:
6 Morck et al. (2000) find that China ranked second, after Poland, in stock price synchronicity in their
cross-country study (see Figure 2.1) and they attribute it to poor investor protection. Gul, Kim and Qiu
(2010) explain that low stock price informativeness in China is caused by a lack of enforcement in its
disclosure regulations and managerial entrenchment arising from concentrated ownership in Chinese firms.
28
(2.1)
where:
is daily excess stock returns for firm i and is the daily value-weighted excess
stock return of the market portfolio when the CAPM is used as the model of market
equilibrium. Both coefficients and are estimated for each fiscal year using
regression analysis. In this model, the coefficient is assumed to capture all systematic
risk while idiosyncratic volatility is the variance of being the unsystematic risk that
can be diversified.
The above model is generally estimated using ordinary least squares (OLS) regression
by assuming that ( ) ( ) .
The variance of the stock return, ( ) is the aggregate of two variances measured
as follows:
( ) ( ) (2.2)
Consequently,
where:
and .
29
The first component of Equation (2.2) is the systematic risk while the second is the
idiosyncratic variance( ) The idiosyncratic variance is presented as follows:
(2.3)
where:
The relative idiosyncratic volatility, being the ratio of idiosyncratic variance to total
volatility (
⁄ for each firm-year t, is the proportion of volatility that is not
explained by systematic components. It is equal to the value of 1- from regression
(2.1), where is the coefficient of determination of firm i in year t.
The value of relative idiosyncratic volatility, 1-R2, is not suitable to be used as a variable
in regressions because it is bound within the intervals [0, 1]. A standard econometric
remedy is adopted by applying logistic transformation on the ratio of (1-R2)/R
2
following Morck et al. (2000). The transformed measure generates from a variable
originally bound by zero to an unbound continuous variable with a more normal
distribution.
Formally, idiosyncratic volatility Ψi,t is defined as:
Ψi,t = Ln
= Ln
(2.4)
30
where:
the variable Ψ measures the firm-specific stock return variation relative to market-wide
variation and is the coefficient of determination of firm i in year t.
Idiosyncratic volatility is scaled by total variation in returns as firms in some industries
are more affected by economy-wide shocks, resulting in higher level of firm-specific
activities (Ferreira & Laux, 2007). Idiosyncratic volatility can also be computed using
the Fama and French three-factor model (Fama & French, 1993, 1995, 1996) and
Brockman and Yan (2009) model. The estimations for these regression models are
presented in item 4.5.2.
2.2.3 Significance of Stock Price Informativeness
Higher idiosyncratic volatility is associated with more informative stock prices (Morck
et al., 2000). Goyal and Santa-Clara (2003) found that idiosyncratic volatility constitutes
approximately 80% of total risk on average. Understanding stock price informativeness
is important because of its direct implications to efficient capital allocation (Wurgler,
2000; Durnev et al., 2003), comprehending managerial decisions (Durnev et al., 2004;
Chen et al., 2007; Frésard, 2012) and information gathering about firms‟ future earnings
(Durnev et al., 2003; Jiang et al., 2009). Further, idiosyncratic volatility is tied up with
the following, namely, firms valuation (Stowe & Xing, 2011), corporate governance
31
(Holmström & Tirole, 1993; Ferreira et al., 2011) and cost of equity (Fernandes &
Ferreira, 2009).
An efficient stock market serves to process information to facilitate channelling of
scarce resources to their greatest economic use (Durnev et al., 2003). Idiosyncratic
volatility reflects the integration of firms‟ private information into stock prices and
therefore signifies active trading by informed arbitrageurs. According to Durnev et al.
(2003), this phenomenon denotes that firms‟ stock prices are closer to their fundamental
(full information) value and exhibits an efficient stock market. As such, capital is
suitably priced in its varying uses and firm managers receive meaningful responses
when stock prices change in accordance to their corporate decisions. The authors
suggest that these effects lead to an efficient allocation of capital, as scarce resources
can be invested in sectors that have high returns or are withdrawn from sectors with
deteriorating prospects (Tobin, 1984).
Wurgler (2000) provides evidence that stock markets exhibiting higher firm-specific
stock price movements provide more constructive public signals of investment
opportunities. He examines financial markets across 65 countries and finds that stock
price synchronicity, being the inverse of stock price informativeness, is negatively
associated with the efficiency of capital allocation. This behaviour suggests capital is
better allocated when there is lesser co-movement of stock prices with the market.
According to Wurgler (2000), larger capital markets such as Germany, Japan, the United
32
Kingdom (UK) and the US produce better informed stock prices due to more effective
arbitrage facilitated by higher stock liquidity and lower transaction costs. This
phenomenon allows better differentiation of investment quality by investors and
managers, thereby leading to better capital allocation.
Durnev et al. (2004) document a positive relationship between firm-specific return
variation and the efficiency of corporate investment. They explain that higher stock
price informativeness encourages better corporate governance, resulting in capital
investment decisions that are more efficient and better aligned with shareholder value
maximisation. As such, capital investments are more efficient when the stock prices
convey more firm-specific information. In a similar vein, Chen et al. (2007) investigate
the relationship between the amount of private information in stock price (proxied by
value of 1-R2) and the sensitivity of investment to stock price and they find robust
positive correlation. This result portrays that investment responds more to stock prices
when the stock price informativeness is higher. According to Chen et al. (2007), firm
managers apply information they learn from the stock markets to make investment
decisions. Applying the same rationale, Frésard (2012) demonstrates that corporate cash
savings are more sensitive to stock prices when the prices convey more information that
is new to firm managers.
Higher level of stock price informativeness is also linked to more information about
firms‟ future earnings embedded in current stock prices. This finding is supported by
33
Durnev et al. (2003) who find a significant positive association between idiosyncratic
volatility and accounting measures of stock price informativeness (which represents how
much information stock prices reveal about future earnings by estimating from a
regression of current stock returns against future earnings). Their findings conclude the
linkage of greater idiosyncratic volatility and better functioning of stock markets. Jiang
et al. (2009) further suggest that idiosyncratic volatility reflects information of firms‟
future earnings.
Stock price informativeness affects investors‟ evaluation of firm value. Stowe and Xing
(2011) used a sample of 90,111 firm-year observations for the years 1990 to 2004 and
document that firms with higher R2 value tend to be overpriced. This is because
investors depend more on market-wide information in the valuation of the high-R2 firms
and tend to ignore some firm-specific information leading to infrequent evaluation.7
High-R2 firms under-perform in the long-term when compared to low-R
2 firms,
suggesting that the former are more likely to be riskier. Hence, these high-R2 firms are
likely to be more susceptible to agency problems as their managers tend to resort to
manipulation when they discover subsequently that they cannot achieve the required
firm performance level so as to maintain high stock prices (Jensen, 2005).
7 According to Benartzi and Thaler‟s (1995) theory of myopic loss aversion, when an investment is
evaluated less frequently, the same investment would appear to be more valuable to investors, who are
typically loss averse. Thus, high-R2 firms are likely to be overvalued. Alternatively, high-R
2 firms tend to
behave like the market (as co-move with the market) and thus seem to be more attractive to the investors
leading to higher tendency of overpricing (Stowe & Xing, 2011).
34
Firm-specific information conveyed by stock prices can also affect corporate
governance as it provides warning to the financial market to interfere especially when
firms are not managed well. Durnev et al. (2004) suggest that stock prices determine the
corporate governance mechanisms, for example, shareholder lawsuits, executive options,
pressure from institutional investors, and the market for corporate control. Holmström
and Tirole (1993) investigate whether stock prices monitor managerial performance.
They demonstrate that stock prices integrate information that cannot be obtained from
firms‟ current and future financial data but is effective to discipline managers. A firm
with deteriorating financial performance may become a takeover target and result in
possible dismissal of their managers if a takeover occurs. This risk will assist in
curtailing misconduct among firm managers. Further, a more informative stock market
facilitates the designing of executive compensation packages in accordance to firms‟
stock prices performance.
Apart from improving external monitoring mechanisms by disciplining managers
(Holmström & Tirole, 1993), information revealed by stock prices play a fundamental
role in enhancing the internal monitoring role of the board of directors. Ferreira et al.
(2011) find that stock price informativeness determines board structure. They argue that
information reflected by stock prices affects the manner how managers are monitored in
two ways. First, more informative stock prices would enable better external monitoring
mechanisms. Consistent with Holmström and Tirole‟s (1993) rationale, Ferreira et al.
35
(2011) opine that firms become cheaper takeover caused by plummeting stock prices
due to value-detrimental projects. This threat prevents firm managers from making
unfavourable corporate decisions. Second, more informative stock prices provide new
firm-specific information to both the markets and boards of directors whereby informed
directors can utilise the information conveyed by stock prices to improve their
monitoring responsibility. Using a sample of 9,447 firm-year observations for the years
1990 to 2001, Ferreira et al. (2011) found robust negative association between stock
price informativeness and board independence, suggesting that stock market monitoring
and board monitoring are substitutes. The authors opine that firms with better informed
stock prices are associated with less demanding boards, for example, a lower level of
board independence and fewer board meetings. They conclude that board structure is
dependent on firms‟ stock price informativeness.
Empirical research also shows that stock price informativeness helps to reduce cost of
equity. In an investigation of the relationship between initial enforcement of insider
trading laws and stock price informativeness in 48 countries, Fernandes and Ferreira
(2009) discover that idiosyncratic volatility reduces the risk for uninformed investors
and contributes to the reduction in cost of equity arising from implementing insider
trading laws.
36
2.2.4 Empirical Research on Stock Price Informativeness
The following sub-items briefly elaborate on the cross-country and firm-level evidence
gathered from empirical findings on stock price informativeness.
2.2.4.1 Empirical Research – Cross-Country Analysis
A developing body of finance literature provides evidence on the information-based
interpretation of idiosyncratic volatility using cross-country analysis. It demonstrates the
importance of strong macro infrastructure in terms of efficiency of judicial systems,
investor protection and financial reporting to ensure the effectiveness of stock price
informativeness.
Morck et al. (2000) examine worldwide stock return synchronicity (inverse of
idiosyncratic volatility) at the country-level in 1995 using R2 value. The R
2 value
measures the percentage of total bi-weekly firm-level return variation of each country
that is explained by the local and US value-weighted market indexes. Figure 2.1
graphically highlights the differences of stock return synchronicity across countries.
37
Figure 2.1 Stock Return Synchronicity in Various Countries in 1995
Source: Morck et al. (2000), pg 227
The leading country for stock return synchronicity is Poland (about 58%) while
Malaysia (about 43%) is in the third position after China (about 46%). United States has
the lowest stock return synchronicity at less than five per cent suggesting that stock
38
prices in the US reflect fundamental values. Morck et al. (2000) observe that stock
prices move more in the same direction in poor economies than in rich economies,
indicating low idiosyncratic volatility in emerging markets but the reverse is true in
developed markets. They argue that the finding is not due to market size, country size,
nor economy diversification but attribute it to stronger perception of “investors‟ private
property rights” in developed countries. Better investor protection against corporate
insiders discourages income shifting by major shareholders and promotes informed
arbitrage. This leads to capitalization of more firm-specific information in share prices
and results in less co-movement in stock returns across firms in developed countries,
consistent with the explanation of Roll (1988).
Irvine and Pontiff (2009) provide a new interpretation of evidence from Morck et al.
(2000). They relate the higher idiosyncratic risks observed in the US for the years 1964
to 2003 to an increasingly competitive environment in which firms have lesser market
power. Information opacity discourages product market competition of a country,
thereby affecting its primary business environment (instead of through stock trading)
which in turn affects R2
value of the stock return. Cross-country analysis by Irvine and
Pontiff (2009) show countries with more competitive economies experience greater
growth in idiosyncratic risk.
Jin and Myers (2006) further explain that information opacity combined with minimal
shareholder protection enable managers to appropriate firms‟ cash flow and lowers stock
39
price informativeness. Idiosyncratic volatility is reported to be higher in countries with
developed and transparent financial markets where informed traders are motivated to
gather private information. However, countries with weak governance and opaque
accounting would induce low idiosyncratic volatility. Moreover, Jin and Myers (2006)
find that opaque stocks with higher R2 value (lower idiosyncratic volatility) have higher
likelihood to crash, that is, to deliver large negative returns when compared to stocks in
relatively transparent countries. Higher crash rates are associated with lower
idiosyncratic volatilities of 40 stock markets around the world in the years 1990 to 2001.
This is because managers of opaque firms conceal bad news from investors and reduce
their extraction of cash flows to safeguard their own positions. However, the
accumulated bad news finally reaches a maximum level when all bad news are released
simultaneously which results in a stock price crash.
Li, Morck, Yang and Yeung (2004) compare the co-movement of individual stock
returns across 17 emerging countries for the years 1990 to 2001. They observe a
significant positive association between capital market openness and idiosyncratic
volatility in most emerging markets economies, especially those with financially stable
institutions. The authors further find that higher firm-specific return variation is strongly
correlated with greater capital market openness, but not products market openness.
Extending studies on emerging countries, Hsin and Tseng (2012) focus on whether stock
price informativeness is determined by investors‟ trading behaviour and the level of
globalization of the market. They observe that the likelihood to be involved in
40
speculative trade and a lower connection with the world market results in lower stock
price informativeness in emerging countries. Difficulties arise in valuing firm-level
fundamentals in a speculative market as it generates more “noise” trading. The stock
price co-movement is also found to be more pronounced in bearish markets implying
that investors may be more loss averse during down markets and this restricts informed
arbitrage, thereby reducing stock price informativeness.
Brockman, Liebenberg and Schutte (2010) discover counter-cyclical patterns in stock
return co-movement in 36 countries for the years 1980 to 2007, that is, co-movement
increases during economic recessions and decreases during economic expansions. The
authors suggest that stock return movement is caused by information production as both
the volume and attributes of information produced are more (less) superior during
economic expansions (recessions), resulting in reduction (increase) in stock return co-
movement. They further find that the relationship between business cycle and co-
movement is weaker in high-income countries as well as those countries that have more
developed financial markets and transparent environment. This finding suggests that
both financial development and transparency are instrumental to ensure a better flow of
information under any economic situation.
In another related study, Daouk, Lee and Ng (2006) developed a Capital Market
Governance (CMG) index from individual stock exchanges of 32 countries for the years
1969 to 1998. The CMG index encapsulates three dimensions of security laws: the
41
extent of earnings opacity, the implementation of insider laws, and the impact of
eliminating constraint of short-selling. Their empirical results show that the
improvement in CMG index is linked to increased idiosyncratic volatility.
Furthermore, Fernandes and Ferreira (2009) examine the relationship of initial
enforcement of insider trading laws and stock price informativeness in 48 countries for
the years 1980 to 2003. The implementation of insider trading laws prohibits trading by
insiders and reduces instances of crowding-out, i.e., preventing others to gather
information. These regulations encourage informed trading by outside investors, hence
more informed market participants are prepared to invest their resources to collect
information about a firm, thereby increasing informativeness of stock prices. Fernandes
and Ferreira (2009) observe that firm-specific return variation is greater after the
enforcement of insider trading laws but only in developed markets. The enforcement of
insider trading laws is insignificantly related to the impounding of information into
stock prices in emerging countries with poor legal institutions. Insider trading
contributes to the role of price discovery, that is, integration of information into stock
prices in emerging countries. Other informed market participants such as analysts,
cannot add to the missing information caused by the discontinuation of insider trading
upon enforcement of insider trading laws, resulting in little advancement in stock price
informativeness in emerging countries.
42
Fernandes and Ferreira (2008) find that cross-listing in US increases price
informativeness of firms located in developed markets as a result of additional
disclosure and scrutiny, but decreases price informativeness of firms in emerging
markets. This is mainly because greater analyst coverage arising from cross-listing in
emerging markets facilitates production of market wide information, instead of firm-
specific information, in line with the findings in Chan and Hameed (2006). The
increased disclosure associated with US exchange rules can crowd out private
information gathering in emerging countries, showing that disclosure standards must be
complemented with other policy initiatives to motivate better production of firm-specific
information in emerging countries.
Both voluntary and mandatory adoption of International Financial Reporting Standards
(IFRS) increase stock price informativeness. Kim and Shi (2010) analyse 15,382 firm-
year observations of both IFRS adopters and non-adopters from 34 countries for the
years 1998 to 2004. Their results highlight that stock price informativeness improves
among voluntary IFRS adopters compared to IFRS non-adopters. Voluntary IFRS
adoption improves firms‟ financial disclosures, thereby motivating investors to gather,
process and trade on firm-specific information to increase informativeness of stock
prices. On the other hand, Beuselinck, Joos, Khurana and Van der Meulen (2009) found
a V-shaped pattern in synchronicity (the inverse of non-synchronicity) during IFRS
adoption among 2,071 mandatory IFRS adopters in 14 European Union countries for the
years 2003 to 2007. The V-shaped curve displays reduction in synchronicity around the
43
time of mandatory IFRS adoption and increases in stock return synchronicity in the
post-IFRS adoption period. The authors explain that the improvement in stock price
informativeness around mandatory IFRS adoption period is contributed by a reduction
in firm opacity arising from required IFRS disclosures. A greater amount of transparent
information, however, has reduced the anticipated “surprise” events that could occur in
the future, leading to decrease in informativeness of stock prices after the IFRS adoption
period.
Broadly speaking, foreign investors are expected to play a significant role in influencing
stock price informativeness. In this regard, He, Li, Shen and Zhang (2013) find that
large foreign ownership8
is positively related to price non-synchronicity. The
researchers used a cross-sectional data of 3,189 firms in 40 markets for the year 2002.
Large foreign shareholders are found to improve the informativeness of stock prices via
informed trading as they are more likely be motivated and are competent in gathering
and processing value-relevant information. The information-based trading by large
foreign shareholders helps the incorporation of private information into stock prices, for
instance, selling their shares when receiving negative information. Large foreign
shareholders can also boost stock price informativeness through enhanced firms‟
corporate governance and disclosure quality. Large foreign shareholders are able to
8 Large foreign ownership is defined as those ultimate owners who own more than 5% of outstanding
shares of a firm and are domiciled outside the country of the invested firm.
44
monitor managers attentively and reduce agency costs, leading to more informative
stock prices. Consistent with other international studies, He et al. (2013) discover that
large foreign ownership has a stronger impact on stock price informativeness in
developed markets with higher investor protection and a transparent information
environment.
In summary, these empirical studies exhibit that stock price informativeness is higher
when property rights are stronger with improved corporate governance control.
2.2.4.2 Empirical Research – Firm-Level Studies
The use of idiosyncratic volatility as a proxy of stock price informativeness at firm level
is empirically supported by several studies in the area of corporate governance (Ferreira
& Laux, 2007; Khanna & Thomas, 2009; Gul et al., 2011a; Gul et al., 2011b), firms‟
ownership structure (Brockman & Yan, 2009; Gul et al., 2010; Hou, Kuo & Lee, 2012;
An & Zhang, 2013; Ding, Hou, Kuo & Lee, 2013) and earnings management (Hutton et
al., 2009). Other studies are equally well supported in the areas of financial reporting
(Haggard, Martin & Pereira, 2008), market competition (Gaspar & Massa, 2006; Peress,
2010), cross-listing (Foucault & Gehrig, 2008; Foucault & Frésard, 2012), earnings
growth (Xu & Malkiel, 2003) and market participants (Piotroski & Roulstone, 2004;
Crawford et al., 2012; Xu et al., 2013).
45
a) Corporate governance
Ferreira and Laux (2007) examine the relationship between corporate governance and
stock price informativeness and they find firms with fewer anti-takeover provisions
exhibit more advanced level of stock price informativeness. According to them, fewer
anti-takeover provisions signify openness to market for corporate control as well as
openness to information sharing. This leads to greater shareholder protection and in turn
improves stock price informativeness through increased transparency. Ferreira and Laux
(2007) suggest that fewer anti-takeover provisions in a firm induce its investors to
gather more firm-specific private information to evaluate the probability of a potential
takeover. Exposure to market forces also discipline managers from expropriating outside
investors and attracts more uninformed investors to trade based on private information.
Informed trading improves the impounding of private information into stock prices and
increases stock price informativeness.
Gul et al. (2011b) show a positive connection between gender diversity in corporate
boards and stock price informativeness for US companies during 2001 to 2007. Gender-
diverse boards improve stock price informativeness by improving voluntary public
disclosures in large firms and by motivating gathering of firm-specific information in
small firms. On the other hand, Khanna and Thomas (2009) examine the relationship
between stock price synchronicity (inverse of stock price informativeness) and joint
46
control of firm activities using a unique data set. This data set provides information on
the extent of equity cross-ownership, common shareholders and director interlocks9 in
Chile, an emerging country. Lower stock price informativeness is found to be
significantly related to joint control activities especially for firms possessing
interlocking directorships. Firms‟ stock prices are more likely to have co-moving returns
with the existence of interlocking directorship. This is because director relationships are
viewed by the capital market as reducing corporate transparency. As such, Khanna and
Thomas (2009) suggest weak corporate governance leads to deteriorating stock price
informativeness.
Gul et al. (2011a) relate perks and informativeness of stock prices in the Chinese market.
Chinese firms with better perquisites are linked to lower stock price informativeness as
high perk consumption, representing agency problems, is perceived negatively by the
market. Further, this negative relationship between perks and stock price
informativeness is attenuated in firms with better quality of financial reporting,
reflecting the positive impact of corporate governance mechanism. As such, Gul et al.
(2011a) suggests that stock price informativeness is dependent on both perks and
financial report quality, in line with the study of Jin and Myers (2006).
9 In the data used by Khanna and Thomas (2009), there are three types of interlocks between firms: (1)
firms‟ ownership of equity stake in each other, (2) partial ownership in firms by the same individual(s),
and (3) common directors of both firms.
47
Auditor quality is shown by Gul et al. (2010) to be positively related to stock price
informativeness. Auditors are important in improving the quality of the information
contained in financial statements and reducing information asymmetries between
insiders and investors (Dopuch & Simunic, 1982). They facilitate the flow of more
reliable firm-specific information to the capital markets by increasing credibility to
firms‟ financial reports, thereby improving the capitalization of firm-specific
information to the stock prices. Gul et al. (2010) examine this phenomenon among
Chinese listed firms which have relatively weak shareholder protection and they do not
generally appoint Big-4 auditors. The researchers found that the appointment of Big-4
auditors is related to higher level of idiosyncratic volatility. Strict adherence to firm-
level corporate governance is shown to improve the information environment in
emerging countries where country-level shareholder protection is relatively weak.
b) Ownership structure
Ownership structure plays an essential role in firms‟ information environment.
According to Brockman and Yan (2009), block ownership (concentrated ownership) in
US firms for the years 1996 to 2001 tend to have access to more precise firm-specific
information at a lower cost as opposed to diffuse shareholders, leading to higher
idiosyncratic volatility.
48
By contrast, Gul et al. (2010) found a concave association between stock price
synchronicity and ownership by the largest shareholder of listed firms in China with its
peak at an approximately 50 per cent shareholding. Their Chinese sample consists of
6,120 firm-year observations for the years 1996 to 2003. This is because stock price
synchronicity initially escalates at a declining rate due to entrenchment effect whereby
major shareholders are induced or provided with opportunities to expropriate firm
resources at the expense of minority shareholders (Johnson, La Porta, Lopez-de-Silanes
& Shleifer, 2000). When the percentage of ownership by the largest shareholders
exceeds certain level, the stock price synchronicity starts to diminish, indicating
improving stock price informativeness. This is mainly attributable to the incentive
alignment effect because firms now feature as if it is a private company in the
possession of major shareholders. In addition, Gul et al. (2010) find that stock price
informativeness is lower when the largest shareholder is government-related. Ownership
of firms by government results in problems of poor investor protection and a lower level
of transparency in financial reporting, leading to lower integration of firm-specific
information into stock prices. Their investigation also reveals that foreign ownership is
positively associated with stock price informativeness as it enables a better flow of
reliable private information into the capital markets.
Hou et al. (2012) further examine the consequences of state ownership on stock price
informativeness in China using a distinctive example of the Split Share Structure
49
Reform. Prior to the reform, the restricted shares held by state shareholders could be
traded freely in the Chinese stock market compared to shares owned by private
shareholders. Thus, the wealth of state shareholders was not dependent on the share
price movement in the capital market. In the year 2005, the trading constraint imposed
on restricted shares was removed and therefore the wealth of these stakeholders is now
influenced by share price movements. This aligns the interests of both state and private
shareholders in monitoring firm managers and therefore, lowers information asymmetry
problems. The empirical results of Hou et al. (2012) show that the Split Share Structure
Reform increases stock price informativeness, especially among those who owned more
restricted shares.
In another Chinese study, Ding et al. (2013) show that mutual fund ownership improves
stock price informativeness as institutional investors are more knowledgeable and
motivated to monitor firms when compared to individual shareholders. Mutual funds are
more developed and play an external monitoring role to improve transparency, thereby
increasing the stock price informativeness. However, the positive effect of mutual funds
on the revelation of firm specific information in stock prices in China is attenuated in
state-controlled firms. The state ownership of listed firms decreases the reliance on
Chinese capital markets for external funding, thereby weakening monitoring capabilities
of mutual funds on managers.
50
An and Zhang (2013) provide collaborative evidence using their US study. Stock price
informativeness is positively associated with shareholdings by dedicated institutional
investors. These investors hold huge shareholdings on long investment horizons, hence
are motivated to scrutinize their portfolios. Management entrenchment is constrained by
better institutional monitoring, thereby leading to impounding of more firm-specific
information into stock prices. Nevertheless, the relationship between institutional
ownership and transient institutional investors shows the opposite direction as these
investors are more interested to trade rather than for long-term investment. Therefore,
firm managers are able to expropriate firms‟ cash flows due to lesser monitoring by
transient institutional investors who hold smaller interest in those firms, thereby
resulting in lower stock price informativeness.
c) Earnings management
Hutton et al. (2009) establish a negative link between opacity (measured by earnings
management) and idiosyncratic volatility using 40,882 US firm-year observations for
the years 1991 to 2005. Their observations suggest that earnings manipulation impedes
the flow of firm-specific information to the capital markets and it predicts crash risk of
opaque firms reliably, thereby confirming the findings of Jin and Myers (2006).
However, this phenomenon seems to disappear after the enforcement of Sarbanes Oxley
Act in the year 2002, suggesting that earnings management has reduced considerably
due to better monitoring.
51
d) Financial reporting
Improving disclosures and the quality of financial reporting alleviate information
asymmetries (Diamond & Verrecchia, 1991). Haggard et al. (2008) provide evidence
that enhanced voluntary disclosure increases stock price informativeness through a
lower cost of acquiring information and improvement in corporate transparency.
Applying analysts‟ assessment of disclosure policies adopted by firms for the years 1982
to 1995, Haggard et al. (2008) demonstrate that their finding is consistent with the study
by Jin and Myers (2006) as greater firm transparency is shown to improve stock price
informativeness.
By contrast, Rajgopal and Venkatachalam (2011) observe that weakening financial
reporting quality, which is represented by accrual based measures of earnings quality, is
linked to an improvement in idiosyncratic volatility for the years 1962 to 2001 in the US.
They claim that their finding is consistent with the practitioners‟ viewpoint on opacity
and irrelevancy of financial reports. Chen, Huang and Jha (2012a) also document that
information quality revealed in managerial discretion is associated with idiosyncratic
volatility whereby firms with weaker information quality have greater idiosyncratic
volatility.
e) Market competition
Competitive environment faced by firms can also influence the idiosyncratic volatility
of their stock returns. Gaspar and Massa (2006) report that low idiosyncratic volatility is
52
observed in firms possessing high market power or firms that are well established in
concentrated industries. According to them, market power acts as a hedging instrument
that eases idiosyncratic fluctuations whereby a large component of any idiosyncratic
cost shocks is transferred to the firms‟ customers to protect their profits. In addition,
high market power implies lesser information ambiguity for the investors that results in
lower stock return volatility. Their findings is consistent with a cross-country study by
Irvine and Pontiff (2009) that show countries experiencing intense competition exhibit
greater growth in idiosyncratic risk. However, Peress (2010) has a different view. He
demonstrates that firms with more market power generate higher trading volumes as
their profits are less risky. The enhanced trading activity for these firms, especially
among the informed investors, improves the impounding of fundamental information
into stock prices. Therefore, Peress (2010) suggests that firms with higher market power
has more informative stock prices.
f) Cross-listing
A study by Foucault and Gehrig (2008) reveals that cross-listing intensifies informed
trading, thereby strengthening the informativeness of firms‟ stock prices on their future
cash flows and growth opportunities. Managers of cross-listed firms benefit from more
informative stock prices by making more efficient investment decisions. Accordingly,
cross-listed firms achieve higher firm value as compared to non-cross-listed firms. This
finding is empirically supported by Fernandes and Ferreira (2008) who report that cross-
listing is positively linked to stock price informativeness. Further, the findings in
53
Foucault and Gehrig (2008) help to explain why cross-listed firms in the US possess
higher sensitivity of investment-to-stock prices than firms that were never cross-listed as
documented by Foucault and Frésard (2012).10
g) Earnings growth
Xu and Malkiel (2003) investigate the influence of earnings growth on idiosyncratic
volatility and they observe a positive relationship between long-term growth rate and
idiosyncratic volatility. This confirms their hypothesis that high-growth firms operating
in an industry with rapid technological changes are required to invest in distinctive
projects and hence, are associated with higher level of idiosyncratic risk.
h) Market participants
Piotroski and Roulstone (2004) examine how three different types of informed market
participants, namely, financial analysts, institutional investors and insiders determine the
information environment. They find that analyst activities are positively related to stock
return synchronicity (inverse of idiosyncratic volatility) while insider trading is
negatively related to stock return synchronicity. This implies analysts increase the
amount of industry information in prices while insiders convey firm-specific
information into stock prices.
10 Refer to item 3.2 for discussion of findings in Foucault and Frésard (2012).
54
Chan and Hameed (2006) also find that greater analyst coverage in emerging countries
is associated with lower stock price informativeness. Xu et al. (2013) observe similar
findings in China but they extend the study by examining whether highly regarded
analysts, or known as star analysts, are able to reduce the extent of stock price
synchronicity. They found that the star analysts are able to produce firm-specific
information and increase stock price informativeness as a result of their outstanding
human capital and firm-specific experience, whilst non-star analysts generate more
market and industry-level information in stock prices, thereby reducing stock price
informativeness.
Crawford et al. (2012) also investigate how analysts contribute to the mixture of firm-
specific, industry and market-wide information available about a firm and they focus on
the analysts‟ initiation of coverage on a firm. When analysts initiate coverage on firms
with no prior following, the stock price informativeness is lower, indicating that analysts
provide low-cost industry and market-wide information so that they can increase their
coverage to greater number of firms. However, when analysts initiate coverage on firms
with existing analyst following, they tend to generate firm-specific information to
discriminate themselves from the existing analysts. Therefore, Crawford et al. (2012)
conclude that the type of information that analysts produce at initiation of coverage is
dependent on the information provided by other analysts.
55
2.2.5 Inconsistent Findings
While the literature has provided a variety of reasoning for using idiosyncratic volatility
as a proxy of stock price informativeness, other empirical studies have contradicting
views on whether this measure is capable of capturing the amount of private information,
for example, Kelly (2005), Dasgupta et al. (2010) and Alves, Peasnell and Taylor (2010).
Kelly (2005) finds that low R2 (indicating higher 1-R
2) in a firm does not reflect
incorporation of greater extent of firm-specific information into stock prices while
Dasgupta et al. (2010) argue that a more transparent firm can have a higher R2 value
(inverse of idiosyncratic volatility), contrary to the conventional wisdom. Using data
from 40 countries over 20 years, Alves et al. (2010) conclude that R2 is insufficient to
represent the quality of the information environment for a country. They perceive it is
beyond belief that R2 of a country can change considerably when a country‟s corporate
governance and investor protection level are assumed constant.
On the other hand, Lee and Liu (2011) contend that the relationship between stock price
informativeness and idiosyncratic volatility is U-shaped. In their model, idiosyncratic
volatility is decomposed into two elements, namely, noise element (arising from demand
by liquidity traders) as well as information element comprising of an information-
updating portion and an uncertainty-resolving portion. Lee and Liu‟s (2011) model
shows that the noise element decreases with price informativeness while the information
element firstly decreases and then increases with price informativeness. As stock price
56
informativeness increases, the information-updating portion increases but the
uncertainty-resolving portion declines. Thus, the information element of idiosyncratic
volatility being the aggregate of information-updating portion and uncertainty-resolving
portion has a U-shape with price informativeness because the average variance over
time is the lowest when the uncertainty is resolved steadily. Lee and Liu (2011) attempt
to reconcile two opposing views in the research on stock price informativeness: Morck
et al. (2000) and Jin and Myers (2006) conclude firms with greater information in stock
price reveal higher idiosyncratic volatility while Kelly (2005) and Dasgupta et al. (2010)
find the reverse is true.
2.3 Corporate Expenditure
Three types of corporate expenditure are selected in this study to examine how each of
them responds to stock price informativeness: R&D expenditure, CAPEX and SGA
costs. R&D expenditure and CAPEX are basic corporate resource allocations while
SGA costs address a firm‟s expense structure. They contribute significantly to total
expenditure (inclusive of operating expenses and capital expenditure) of the sample
firms in this study, for example, SGA costs comprise 32 per cent of firms‟ total
expenditure while R&D expenditure and CAPEX contribute 16 and eight per cent of
total expenditure respectively. These corporate expenditures are discretionary in nature
as they are determinable and controllable by managers and they have a significant
impact on firm performance. Furthermore, they are complementary and represent
57
different features of a firm‟s strategic profile (Finkelstein & Hambrick, 1990; Carpenter,
2000; Zhang & Rajagopalan, 2010). Drawing from the strategic management literature,
R&D expenditure, CAPEX and SGA costs are also part of the six key strategic
dimensions11
used in measuring strategic change12
(Finkelstein & Hambrick, 1990;
Carpenter, 2000; Zhang & Rajagopalan, 2010). Strategic changes are undertaken by
firms in their alignment with external environment (Rajagopalan & Spreitzer, 1997) to
provide competitive advantage and ensure firm survival (Carpenter, 2000).
The following items briefly provide relevant literature on R&D expenditure, CAPEX
and SGA costs, with a focus on their benefits and determinants.
2.3.1 Research and Development Expenditure
Research and development activity generates knowledge assets that allow firms to
produce new and more superior products, more efficient production methods and
innovated form of business organizations (Erickson & Jacobson, 1992). R&D activities
not only result in the radical production of new products, processes or organizational
forms but also involves incremental improvements to existing goods and methods of
11 The other three key strategic dimensions used to measure strategic change are advertising expenditure,
inventory levels and financial leverage (Finkelstein & Hambrick, 1990; Carpenter, 2000; Zhang &
Rajagopalan, 2010).
12 Strategic change is the variation over a long period of time in the firm‟s pattern of resource allocation in
various key strategic dimensions (Finkelstein & Hambrick, 1990; Zhang & Rajagopalan, 2010).
58
production (Shadab, 2008). Cohen and Levinthal (1989) suggest that in addition to
producing new information, R&D initiatives improve firms‟ absorptive capacity to
recognise, adapt and develop knowledge from the environment.
A review of literature on R&D expenditure is presented from the perspective of
economic return and risk. The study also explores the determinants of R&D expenditure.
2.3.1.1 R&D Expenditure and Return
There is a large body of research in finance, management and marketing that relates
R&D activities to firms‟ financial performance. Investment in R&D contributes
substantially to firms‟ productivity and output, hence, shareholders‟ wealth, and its
benefit extends over a long period of time (Lev, 1999). R&D expenditure is viewed as
an intangible capital with positive consequences on sustained future cash flows
(Hirschey, 1985; Chauvin & Hirschey, 1993). Connolly and Hirschey (2005) document
a significant positive impact of R&D intensity on Tobin‟s Q, consistent with Griliches
(1981) and Pakes (1985). Capon, Farley and Hoenig (1990) perform a meta-analysis of
320 published studies between 1921 and 1987 to examine the determinants of financial
performance and observe that R&D expenditure is significantly associated to increased
profitability.
59
Sougiannis (1994) demonstrates that reported accounting earnings reflect benefits from
past R&D expenditure. Eberhart, Maxwell and Siddique (2004) find a positive abnormal
long-term operating performance following an unexpected increase in R&D expenditure,
thereby indicating that R&D initiatives are beneficial investment. Value creation via
R&D projects, in combination with value appropriation (through advertising activities),
enhances firm value (Mizik & Jacobson, 2003). Innovation is more likely to generate
persistently high level of profits (Roberts, 2001) and brings greater welfare to
consumers, shareholders, firms and economies (Shadab, 2008). Ho, Tjahjapranata and
Yap (2006) find R&D investment has a positive impact on firms‟ growth opportunities
while Erickson and Jacobson (1992) opine that increasing R&D expenditure provides a
positive signal to the market that the firms possess the discretionary funds needed to
embark on R&D projects.
Past research findings also show that the market considers corporate R&D activities as
investments that generate future benefits, thus assigning significant valuation to firms
undertaking such projects (Kothari, Laguerre & Leone, 2002). For example, Lev and
Sougiannis (1996) and Chan, Lakonishok and Sougiannis (2001) find a significant
positive association between R&D investment and excess risk-adjusted returns in the
subsequent years, hence confirming the value relevance of R&D expenditure to
investors. Penman and Zhang (2002) also report a positive association between R&D
growth (changes in R&D investment) and subsequent excess return. The tentative
explanation offered by these studies is that shares of R&D-intensive firms are mispriced
60
as investors are unable to see through earnings distortion caused by the nature of
accounting for R&D costs, that is, by immediately expensing R&D expenditure in the
current year‟s financial statements. Firms‟ earnings are likely to be understated when
R&D investment increases and vice versa. Therefore, R&D investment does not appear
to be fully reflected in current year stock prices but instead are seen in future stock
returns. Eberhart et al. (2004) also observe significantly positive abnormal stock returns
for a period of five years after unexpected increases in R&D expenditure. They interpret
it as an evidence of investors‟ under-reaction to the intangible information that R&D
investment reflects on future cash flows. However, Chan et al. (2001) opine that
managers who are confident about firms‟ future growth will continue to invest heavily
on R&D activities in spite of unfavourable past firm performance as well as the
necessity to minimise cost but the market tends to overlook the positive signals of R&D
investment.
Lev and Sougiannis (1996) offer an alternative explanation of substantial future risk-
adjusted stock returns: investors‟ requirements of higher expected returns from these
R&D-intensive firms as a reward for risk-taking. Chambers, Jennings and Thompson
(2002) distinguish between systematic mispricing and risk explanations for R&D-related
excess returns. They demonstrate that excess returns reported in previous studies are
attributable to risk associated with R&D activities. Nevertheless, Ciftci, Lev and
Radhakrishnan (2011) compare high and low R&D-intensity firms. They discover that
high R&D-intensity firms tend to innovate by developing new products while low
61
R&D-intensity firms are more likely to imitate, indicating lower level of risks. High
R&D-intensity firms are also found to produce larger risk-adjusted returns in the short-
term. Subsequently, the excess returns of these firms become identical to those of the
low R&D-intensity firms. Thus, Ciftci et al. (2011) claim that the pattern of returns
reversal is consistent with mispricing explanation but inconsistent with risk justification.
Srinivasan et al. (2011) investigate the effects of R&D spending on profits and stock
returns during recessionary periods. They observe that firms with greater market share
gain more from R&D expenditure during recessions, leading to higher profits and stock
return. Firms with higher market share are in a better position to achieve economies of
scale in their R&D projects and leverage their R&D outputs effectively during
recessions. Srinivasan et al. (2011) highlight that increased investors‟ anticipation of
future risk-adjusted cash flows leads to higher stock returns. The positive impact of
R&D investment on firms‟ profits and stock returns are portrayed in highly leveraged
firms which increase their R&D expenditure during recessions. This phenomenon
provides a positive signal to the market that these firms are of good quality as they are
able to lower their cost of capital during recessions, hence increasing security for their
lenders.
Bublitz and Ettredge (1989) examine the endurance of R&D expenditure by using stock
returns and disaggregated earnings data. They reveal that the expected benefits from
R&D expenditure are long-lasting. Lev and Sougiannis (1996) relate company‟s
62
operating profits to the time series of their yearly R&D expenditure up to 10 years back
and suggest that the general lifetime of R&D projects range from five to nine years.
According to Lev and Sougiannis (1996), the shortest life of R&D projects is found in
scientific instruments industry while the longest life is in chemical and pharmaceuticals
companies. This is consistent with Nadiri and Prucha‟s (1996) estimate of the useful life
of R&D projects that vary between seven and nine years. Nevertheless, Erickson and
Jacobson (1992) find that R&D expenditure do not have any substantial impact on
accounting and stock market returns in their US sample covering the years 1972 to 1986.
They attribute this to minimal barriers to imitation of R&D innovation to generate long-
term benefit from these projects.
2.3.1.2 R&D Expenditure and Risk
Investment in R&D activities generally bears greater risk and requires long-term
obligations as it can take several years before a particular innovation is successfully
commercialized. Firms may have to sacrifice short-term profits to gain in the long run
(Shadab, 2008). Chambers et al. (2002) and Berk, Green and Naik (2004) suggest that
risks are markedly larger for R&D-intensive firms. This is mainly due to the technical
uncertainty arising from R&D projects and risk associated with firms‟ cash flows after
development is completed (Berk et al., 2004). Ho, Xu and Yap (2004) also find a
positive association between R&D intensity and systematic risk in the stock market
especially for firms which are more profitable. This also applies to large firms and firms
63
that make intensive R&D investments. Inherent business and operating risks lead to
higher systematic risk. Nevertheless, McAlister et al. (2007) examine 644 public-listed
firms between 1979 and 2001 and discover that R&D investment reduces firms‟
systematic risks. They suggest that R&D expenditure generates strategic differentiation,
efficiency and flexibility, thus protecting firms from market downturn, thereby lowering
their systematic risk.
Numerous studies demonstrate that risk associated with R&D investment may outweigh
their potential benefits. For example, Kothari et al. (2002) examine contribution of R&D
investment to future earnings variability using a large sample of 50,000 firm-year
observations for the years 1972 to 1992 and learn that R&D investment produces more
doubtful future benefits compared to capital expenditure. Shi (2003) discovers that firms‟
R&D intensity is inversely associated with bond rating and premiums at issuance, thus
affecting bondholders negatively. Conversely, Eberhart, Maxwell and Siddique (2008)
evaluate using a different measure of R&D intensity, that is, dividing R&D expenditure
by total assets or total sales, compared to “R&D expenditure scaled by market value of
equity” applied by Shi (2003). They notice that R&D investment is beneficial to
bondholders as increase in R&D investment lowers the firm‟s default risk.
64
2.3.1.3 Determinants of R&D Expenditure
The following item briefly summarizes past research findings on the determinants of
firm-level R&D expenditure.
a) Firm size
Firm size is shown to be related postively to R&D spending (Rothwell, 1984). Large
firms are more willing than small firms to engage in R&D activities especially those
from the concentrated industries as they are better insulated from competition and are
able to internalise the benefits of R&D investment (Kamien & Schwartz, 1982). Larger
firms possess the required resources to enable them to deal with higher risk (McEachern
& Romeo, 1978), thus they are able to enjoy the greatest economies of scale in R&D
activities (Baysinger & Hoskisson, 1989). However, low R&D expenditure is also found
in large firms or firms with substantial market shares as their managers may be less
motivated to invest in innovations (Graves, 1988; Hansen & Hill, 1991).
Firm size is also related to R&D productivity. In a seminar study, Schumpeter (1942)
concludes that larger firms tend to be better off in implementing and exploiting R&D
efforts. Chauvin and Hirschey (1993) investigate the effectiveness of R&D expenditure
across firm size and industry group using data for the years 1988 to 1990. They
demonstrate that the market value effect of a dollar in R&D expenditure tends to be
stronger for larger firms, compared to smaller firms, in both manufacturing and non-
manufacturing sectors. Subsequent research provides evidence that R&D efforts of
65
larger firms are more productive. Cohen and Kleppler (1996) suggest that the advantage
of averaging the R&D costs over their higher level of output allow big firms to benefit
more from such an activity. Henderson and Cockburn (1996) explain that larger firms
are more capable in realizing economies of scale (arising from specialization and
sharing of fixed costs) and economies of scope by sustaining diverse portfolios of
research projects. Larger firms are also able to capture knowledge spillover13
internally
and externally within firms. Connolly and Hirschey (2005) extend the studies to firms
for the years 1997 to 2001 and found consistent results: the effect of R&D investment
on Tobin‟s Q is stronger for larger firm size and for manufacturing firms.
Ciftci and Cready (2011) analyse whether firm size plays a fundamental role in R&D-
related earnings performance and earnings volatility. Consistent with larger-firm
advantage in R&D productivity, the positive relationship between R&D intensity and
realisation of future earnings is stronger when firm size increases. In terms of earnings
volatility, larger firms are able to diversify the risk of R&D investment, thus
experiencing lower future earnings variability.
13 Jaffe (1986) explore the concept of knowledge spillover and show that R&D activities of neighbouring
firms have considerable impact to firms‟ own R&D success.
66
b) Internal finance
Kamien and Schwartz (1978) suggest that internal finance is the most leading
determinant of industrial R&D projects, consistent with Schumpeter‟s (1942) view on
the potential importance of internal finance for innovation. Based on a panel study of
179 small firms14
in high technology industries, Himmelberg and Petersen (1994) find
that internal finance determines R&D investment expenditure and they attribute the need
of internal finance to the existence of information asymmetries between firms and
providers of external financing. R&D investment is featured as high information
asymmetry because it is a poorly disclosed productive input, thus affecting outsiders in
making appropriate evalution of a firm‟s R&D performance (Aboody & Lev, 2000).
According to Grabowski (1968), funds available in a firm improve its ability to invest in
R&D projects. He opines that managers are generally unwilling to rely on external
funding to finance their investment as it involves higher risks and transaction costs. In
addition, the riskiness of R&D activities also discourages the use of borrowing to
finance R&D investment (McEachern & Romeo, 1978). Thus, internal finance is
directly related to R&D expenditure.
14 Large firms have better access to external resources and generate cash flows in excess of investment
needs. Thus, small firms are examined in the study of Himmelberg and Petersen (1994).
67
c) Financial leverage
Financing-related activities of firms are closely related to financial leverage. Some high-
leveraged firms have to preserve their current cash flow for debt services, thus are
discouraged from investing in R&D initiatives (Barker & Mueller, 2002). In addition,
high debt implies creditors‟ control over strategy formulation where financial
institutions are more risk-averse than shareholders. As such, Baysinger and Hoskisson
(1989) suggest that to the extent that a firm‟s debt position reflects the influence of
financial institutions on decision making, debt is negatively related to R&D expenditure.
Ho et al. (2006) find firm size advantage of R&D investment on firms‟ growth
opportunities disappears as the financial leverage level rises. In particular, they discover
that highly-leveraged small firms enjoy the greatest growth opportunities, suggesting the
need to consider factors of firm size and financial leverage together in evaluating the
value relevance of R&D expenditure.
d) Industry
Previous research has shown that the level of firms‟ R&D investment is dependent on
the industry membership of the firms (Scherer, 1984). Industries vary in the extent of
their basic scientific knowledge in the area of their business activities (Baysinger &
Hoskisson, 1989). According to Connolly and Hirschey (2005), R&D intensity is more
likely to be higher in manufacturing industries. They show that the average R&D
intensity of the manufacturing sector is almost 10 times than that of non-manufacturing
firms. Their sample consists of 15,709 firm-year observations across 80 countries for the
68
years 1997 to 2001. R&D spending during the years 1988 to 1990 is found by Chauvin
and Hirschey (1993) to be the highest, particularly in industries such as industrial
machinery, computer equipment, measuring equipment, photography, electronic
equipment, as well as chemical and allied products. However, lesser R&D projects are
found in service industries, as well as financial and retail sectors. The sectoral
differences among industries reflect the varying technological opportunities available,
that is, to what extent market accepts product innovations (Baysinger & Hoskisson,
1989; Connolly & Hirschey, 2005). Lev (1999) in his trend analysis of US R&D
expenditure from the 1970s to the late 1990s show that R&D investment has increased
smoothly in emerging technologies such as biotechnology, computers and
telecommunications due to constantly growing opportunities.
e) Geographic location
Audretsch and Feldman (1996) change the usual product dimension to a geographic
location. They find geographical concentration of innovative activities and output are
linked to R&D-intensive industries. This is mainly attributable to the existence and
effects of knowledge spillover of R&D investments (Jaffe, 1986; Griliches, 1998). The
spillover tends to be geographically localized, thus new technological knowledge can
flow easier locally than over distant places. Technological spillover to the research
community plays an essential role in creating more knowledge, thereby increasing
returns and ultimately spurring economic growth (Grossman & Helpman, 1991, pp. 17-
18).
69
f) Industrial clusters
Baptista and Swann (1998) reveal that the likelihood for innovation is higher for firms
situated in strong industrial clusters as opposed to firms situated outside these clusters.
Porter (1990) indicates that clustering facilitate information flow and interchanging
between rival firms, thereby fostering innovation, entry of new firms to the market as
well as growth. This is consistent with the concept of knowledge spillover of R&D
investment (Jaffe, 1986; Griliches, 1998) whereby greater knowledge spillover is
generated leading to higher innovative output.
g) Diversification strategy
Diversification strategy can affect R&D investment decisions in large mutiproduct firms.
Baysinger and Hoskisson (1989) show that the degree of diversification has a significant
negative relationship with R&D intensity in diversified firms. R&D intensity in
dominant-business firms (i.e. firms engaging in limited diversification) was found to be
significantly greater than highly-diversified firms. Baysinger and Hoskisson (1989)
correlate these findings to firms‟ internal control systems and managers‟ willingness to
invest in R&D projects. Highly diversified firms apply a system of strict financial
control (Gupta, 1987), hence their managers are more likely to avoid risky R&D
investment to meet short-term financial performance goals. By contrast, firms
implementing dominant-business diversification (limited diversification) emphasize on
both financial and strategic controls. They use subjective data in performance evaluation
70
(Gupta, 1987), hence fostering risk-taking behaviour that leads to higher R&D
expenditure.
h) Corporate governance
R&D expenditure is discretionary in nature, i.e., controllable and determinable by
managers. Fama and Jensen (1983) suggest that firms with high R&D investment suffer
from severe agency problems. According to them, agency costs arise when managers
possess power over firms‟ resources and pursue opportunistic activities to expropriate
wealth from firms and shareholders for their own gains. High investments in R&D
projects are generally perceived as a high risk venture but a high return strategy that
could likely provide future benefits to shareholders. Managers, however, are not willing
to invest in long-term R&D projects as they do not generate short-term returns
(Baysinger, Kosnik & Turk, 1991) and are risky in nature (Haynes & Hillman, 2010).
Stock market participants, especially institutional investors, emphasize greatly on
current year results and the short time horizon, therefore, prohibit firms from making
necessary R&D investments for long-term growth (Erickson & Jacobson, 1992). When a
company‟s control and evaluation system places emphasis on financial performance
criteria such as return on investment, managers will avoid risky projects and sacrifice
long-term innovation projects to achieve immediate financial performance goals
(Baysinger & Hoskisson, 1989). Consequently, conflict of interest exists between
managers and shareholders in making R&D investment decisions (Jensen & Meckling,
71
1976). There is higher propensity for managers to uphold their own interests when there
is a lack of proper governance controls (Fama & Jensen, 1983).
Agency theory stresses the role of independent board of directors in monitoring
managerial actions. Past research shows that corporate governance influences
management‟s discretion in either investing or avoiding risky R&D projects through
monitoring via board composition and ownership structure. For example, Baysinger et al.
(1991) report that high insider representation on board of directors jointly with a
concentration of institutional investors is positively associated with corporate R&D
expenditure. The positive association between management representation in board of
directors and R&D expenditure is consistent with the results reported by Hill and Snell
(1988). According to Hill and Snell (1988), more insiders are included in the board of an
R&D firm for the incorporation of firm‟s functional activities around its R&D strategy.
Baysinger and Hoskisson (1990) relate the control emphasis of the board of directors,
that is, either applying strategic control or financial control on R&D expenditure. Firms
that place emphasis on strategic control focus on long-term performance, promote long-
term risk taking and reward activities resulting in innovation (Shadab, 2008). Managers
in R&D-intensive firms have to bear higher risk of technological ambiguity and
obsolescence whereas shareholders can diversify their risk by holding a diversified
portfolio of stocks. Thus, there is a higher likelihood for managers who are assessed by
strategic control to value more towards uncertain cash flows arising from risky R&D
activities. Baysinger et al. (1991) suggest that managers are more like to support risky
72
R&D initiatives if they are well-represented in the board of directors to make decisions.
Baysinger and Hoskisson (1990) argue that boards dominated by outsiders are related to
a lower R&D investment if they emphasize more on financial controls.
In addition, Baysinger et al. (1991) provide empirical evidence on the positive
relationship between equity concentration among institutional investors and R&D
expenditure, contrary to the myth that institutional investors are short-term oriented.
Institutional investors, adopting high risk-high return strategy, value R&D investment
positively and are able to diversify their R&D risk better than individual investors by
holding diversified investment portfolios. Hansen and Hill (1991) found consistent
results and suggest that individual investors are more risk-averse and they opt for lower-
risk strategies.
i) Top management team (TMT)
Chief Executive Officer (CEO) attributes are found to predict firms‟ R&D expenditure.
For example, Barker and Mueller (2002) show that higher R&D expenditure is observed
in firms where CEOs are younger and invest more in firms‟ shares. Their CEOs are also
more likely to be educated in advanced science-related areas and are more experienced
in marketing or engineering fields.
Barker and Mueller (2002) find that R&D expenditure increases with longer CEO tenure,
signifying that CEO personalities shape strategic decisions to match their own choices.
73
This is contrary to the studies which find that CEOs tend to make fewer strategic
changes as their years of service increases (Grimm & Smith, 1991; Hambrick &
Fukutomi, 1991). Retiring CEOs may emphasize stability and efficiency and thus, are
unwilling to undertake innovation strategies through increased R&D expenditure. CEOs
also face incentives to restrict R&D expenditure nearing their retirement to improve
short-term performance (Dechow & Sloan, 1991) and to conserve their CEOs‟ pension
entitlements (Sundaram & Yermack, 2007). Conversely, Cazier (2011) find no evidence
of reduced R&D costs during periods close to CEOs‟ retirement by tracing R&D
expenditure made by the same CEOs over a period of time. He explains that extant
evidence regarding R&D curtailment among short-horizon CEOs is caused by empirical
research designs that results in invalid inferences.
Both upper-echelon and resource-based theories focus on the relevance of top
management team‟s (TMT) firm-specific knowledge, collective confidence, and top
management diversity to R&D investment decisions (Finkelstein & Hambrick, 1996;
Penrose, Penrose & Pitelis, 2009). Kor (2006) examines the direct and interaction
effects of TMT and board composition on R&D intensity. His results show that
managers‟ tenure is negatively and non-linearly associated to R&D intensity as long-
tenured managers are more risk-averse and prefer not to commit high level of R&D
expenditure. The presence of founders in the TMT is associated with higher R&D
investment. Managers‟ shared team-specific experience is also positively correlated with
R&D investment as the trust and common understanding among TMT members allows
74
firms to make risky investments. Kor (2006) also exemplifies that conflicts between
TMT members and boards can undermine the effectiveness of board monitoring, thereby
resulting in decreased R&D expenditure.
2.3.2 Capital Expenditure
The second type of corporate expenditure that is examined in the study is capital
expenditure. It is an essential component of a nation‟s aggregate demand and gross
domestic product (GDP), contributing to world economic growth (Dornbusch & Fischer,
1992, p. 22). The following extant literature on CAPEX is reviewed based on its
significance to firms and market, as well as factors that determine the level of capital
investments.
2.3.2.1 Significance of CAPEX
There is a large volume of literature describing the role of CAPEX. Among others,
McConnell and Muscarella (1985) show that announcements of increased (decreased)
capital investments are generally related to significantly positive (negative) excess stock
returns. This is in line with market value maximisation hypothesis which suggest that
managers seek to maximise firms‟ market value in making CAPEX decisions. Managers
would invest as long as the marginal rate of returns on investment exceeds the market‟s
required rate of return. As such, unexpected increase in CAPEX is accompanied by an
increase in market value of industrial firms, and vice versa.
75
Chung, Wright and Charoenwong (1998) have differing views. They evaluate the
market responses to firms‟ CAPEX decisions from the perspective of firms‟ growth
prospects. They find that the response to the announcements of the quantum of CAPEX
by firms is linked to level of Tobin‟s Q representing investment opportunities. Therefore,
they argue that the market‟s evaluation of the quality of firms‟ investment opportunities
affects the share price reaction to capital investment decisions. Analysing from the angle
of investment opportunities, Chen (2006) suggest that the announcement of capital
investment exerts a positive effect on market reaction in focused firms than in
diversified firms.15
This is because better investment opportunities exist in focused firms
and their managers tend to invest in projects with positive net present values.
CAPEX conveys additional valuable and relevant information about firms‟ future
earnings. When the market is able to predict managers‟ investment decisions from the
existing information, all appropriate information has already been incorporated in the
share prices. Thus, Kerstein and Kim (1995) hypothesize that unexpected increases in
CAPEX would only be value-relevant when managers react to private information about
firms‟ future demand or cost structure and make proper investment decisions. The
additional capital investments provide positive signals to the market about prospects of
projects undertaken whereas the decrease in CAPEX indicates that the reverse is true.
The empirical results highlights a significant positive relationship between changes in
15 In Chen (2006), focused firms are firms with a single segment of business while diversified firms are
firms with multiple business segments.
76
CAPEX and excess returns, after controlling for concurrent earnings information and
pre-disclosure information differences, and thereby providing support to Kerstein and
Kim‟s (1995) view.
An unexpected increase in CAPEX may however, result in an unfavourable economic
value. According to McConnell and Muscarella (1985), size maximisation hypothesis,
contrary to market value maximisation hypothesis, posits that managers tend to over-
invest where marginal returns of investment are below the market‟s required return to
increase firm size. In this instance, any unexpected increase in CAPEX would
negatively affect the market value of firms whereas any unexpected reduction in
CAPEX would increase firms‟ market value. This empire-building implication of
increased capital expenditures arises from managers who invest for their own benefits
rather than to maximise shareholders wealth (Jensen, 1986).
Titman, Wei and Xie (2004) find a significant negative relationship between unexpected
CAPEX and future stock returns. Firms increasing their capital investment recorded
negative benchmark-adjusted returns for five years subsequent to the expenditure. These
observations are interpreted as investors‟ under-reaction to the tendencies of over-
investment or empire building. Furthermore, Titman et al. (2004) discover that the
negative association between CAPEX and stock return is stronger in firms that have
higher cash flows and lower financial leverage. These firms are mostly managed by
individuals who possess higher discretionary power in making investment decisions and
77
thus, have a higher likelihood to over-invest. As such, capital expenditure does not
necessarily improve firm performance especially when the firm has free cash flow
(Jensen, 1986) and when the investment is wasteful (Lang, Stulz & Walkling, 1991;
Chen, 2006).
Capital investment is also found to affect earnings. Inci, Lee and Suh (2009) provide
international evidence from their investigation undertaken by using firms in 40 countries
for the years 1988 to 2004. They highlight the importance of legal system and financial
development in exploring the association between CAPEX and earnings. According to
Inci et al. (2009), the CAPEX-earnings‟ relationship is generally positive for firms in
common law and financially developed countries but is generally negative in firms
domiciled in civil law and financially developing countries. This is similar with the
over-investment problem faced at the firm level. A lack of proper governance systems
and under-developed capital markets, especially in civil law and financially under-
developed countries, could retard generation of positive returns from their capital
investments. Consequently, Inci et al. (2009) recommended that policy makers should
develop the legal environment such as corporate governance, shareholder protection and
monitoring mechanisms to motivate managers to invest in value-increasing capital
projects.
78
2.3.2.2 Determinants of CAPEX
In view of the significance of CAPEX, a considerable amount of literature has been
published on its determinants. The determinants are briefly outlined below.
a) Internal cash flow
Internally generated cash flow is fundamental in determining capital expenditure (Vogt,
1994; Griner & Gordon, 1995; Gordon & Iyengar, 1996). Two hypotheses, namely, the
pecking order hypothesis and free cash flow hypothesis, have been developed to explain
the rationale behind the use of internal cash flow to finance CAPEX. According to the
pecking order hypothesis developed by Myers (1984) and Myers and Majluf (1984),
managers prefer internal cash flow over external financing for the purpose of funding
capital projects to mitigate dilution of current shareholder value. This is mainly caused
by information asymmetry problem (in the form of adverse selection)16
that arises when
firm managers are better informed about the firm value as opposed to new potential
investors. Firm managers may under-invest by turning down profitable projects to avoid
issuing new shares. Availability of internal cash flow minimises external financing cost
and determines the level of capital expenditure. Fazzari, Hubbard and Petersen (1988)
confirm the significance of cash flow to liquidity-constrained firms as sensitivity of
capital investment-to-cash flow is stronger for these companies. Griner and Gordon
16 More discussion on adverse selection is deliberated in item 3.3.
79
(1995) and Carpenter and Guariglia (2008) show consistent results and interpret it as
evidence of imperfect information linked to external financing.
The alternative hypothesis used to explain why internal cash flow determines CAPEX
level is the free cash flow hypothesis. Jensen (1986) contends that instead of paying out
free cash flow in the form of dividends and debt-financed share repurchase, managers
choose to over-invest firms‟ free cash flow wastefully on value-decreasing projects so as
to increase their personal wealth. Therefore, agency problem plays an important role in
the relationship between internal cash flow and capital expenditure level, as evidenced
by Strong and Meyer (1990) and Devereux and Schiantarelli (1990). However, Griner
and Gordon (1995) find that the cash flow-CAPEX relationship is not due to conflict of
interest between managers and shareholders.
Vogt (1994) examines whether pecking order model or free cash flow hypothesis prevail
in the relationship between internal cash flow and capital investment. He suggests that
pecking order behaviour is mostly found in small firms while the free cash flow
hypothesis is portrayed in large firms. Both types of firms choose to make low dividend
payout to preserve their cash flows.
Vogt (1997) provides two additional evidence in explaining the role of internal cash
flow on CAPEX by focusing on excess returns around firms‟ CAPEX announcements.
Firstly, he finds a significant positive relationship between internal cash flow and
80
announced CAPEX, consistent with findings by McConnell and Muscarella (1985).
Secondly, it is observed that both small firms and firms with concentrated insider
ownership record significant positive excess returns around CAPEX announcements.
The excess returns increase as the abilities to finance CAPEX by internal cash flow rise.
This is consistent with findings by Vogt (1994) that small firms are following the
behaviour portrayed by the pecking order hypothesis (Myers, 1984; Myers & Majluf,
1984).
Inci et al. (2009) also show a positive association between earnings (representing
internal generated cash flows) and CAPEX in nearly all 40 countries surveyed,
regardless of the form of legal system and extent of financial development in these
countries. A similar relationship between earnings and CAPEX is also prevalent in
financially developed markets as these markets have easy access to external financing.
This indicates that internal financing is crucial for capital investments and thus is
consistent with the pecking order hypothesis. However, free cash flow behaviour is
identified in firms with low insider ownership indicating higher levels of agency costs.
As such, following the pecking order theory and the free cash flow hypothesis, firm size
and insider ownership are expected to indirectly determine capital expenditure.
81
b) Return on investment
According to Gordon and Iyengar (1996), managers consider the incremental effect of a
project on firms‟ average return on investments (ROI) when they make capital
investment decisions. There is a high possibility for managers to reject a profitable
capital project if the investment is expected to reduce a firm‟s overall ROI. Gordon and
Iyengar (1996) observe a positive association between ROI and capital expenditure,
suggesting ROI maximisation is an essential determinant of CAPEX. They also find that
the relationship between ROI and CAPEX remains robust even when ROI is maximised
but not for the best interests of firms‟ shareholders.
c) Sales growth
Griner and Gordon (1995) suggest that sales growth represent investment opportunities
while Gordon and Iyengar (1996) argue that sales growth contributes to company funds
and enables capacity expansion. As such, sales growth is expected to be positively
associated with capital expenditure.
2.3.3 Selling, General and Administrative Costs
The third proxy of corporate expenditure examined in this study is selling, general and
administrative (SGA) costs. These costs form a major proportion of the business
operation expenses (Chen et al., 2012b), therefore representing a fundamental aspect of
firms‟ strategic tools (Finkelstein & Hambrick, 1990). As reported by Banker, Huang
82
and Natarajan (2011b), the average ratio of SGA costs to total assets is 31 per cent while
R&D expenditure is merely three per cent of total assets for their US sample for years
1970 to 2004. SGA costs comprise input resource expenditure in advertising, selling
(including marketing as well as distributing products and services), information
technology, human resources and R&D expenditure, thereby creating intangible assets
and generating long-term value. These costs are required by the International
Accounting Standards (IAS) to be expensed immediately in the current year (Banker et
al., 2011b).
2.3.3.1 Characteristics of SGA Costs
The SGA cost to sales ratio is closely monitored by investors and analysts as any
changes in this ratio provide meaningful signals with regards to firms‟ future
performance (Anderson, Banker, Huang & Janakiraman, 2007). In a conventional
fundamental analysis,17
an increase in the ratio of SGA cost/sales from the previous
period represents operational inefficiency as managers are deemed to be incapable of
controlling costs, thus providing unfavourable signals about firms‟ future profitability. A
17 Fundamental analysis is a diagnostic procedure used to analyse financial statement items to assess firm
performance and forecast future earnings. Lev and Thiagarajan (1993) examined 12 fundamental signals
that provide incremental information. This involves interpretations of financial ratios and their changes in
the area of inventory, accounts receivables, CAPEX, R&D expenditure, SGA costs, gross margin,
provision of doubtful debts, taxation, order backlog, labour force, last-in-first-out (LIFO) earnings and
audit qualifications.
83
decreasing SGA cost/sales ratio, on the other hand, indicates managerial ability to
contain costs and consequently are linked to future increases in firms‟ value (Lev &
Thiagarajan, 1993). In determining the value relevance of SGA costs, Lev and
Thiagarajan (1993) reveal a significant negative relationship between an increasing SGA
cost/sales ratio and contemporaneous excess stock return. Abarbanell and Bushee (1997)
investigate whether the changes in fundamental ratios are associated with the changes in
firms‟ future earnings. They do not find any significant association between changes in
SGA costs/sales ratio and changes in the subsequent year‟s earnings but instead observe
significant negative associations between these two variables when firms‟ earnings were
worsened from the previous year and also during period of low GDP at the
macroeconomics level. This finding provides some support for the idea that an increase
in SGA costs/sales ratio conveys negative signals about future earnings change, at least
when sales and earnings are deteriorating.
The implicit assumption used in the fundamental analysis is that costs (SGA costs)
change proportionately with activity levels (sales) (Noreen, 1991). Traditional models
view costs as fixed or variable and the magnitude of cost change is not subject to the
direction of the changes in activity levels (i.e. when sales increase or decrease). Studies
have been carried out to investigate the validity of the proportionality assumption
(Noreen & Soderstrom, 1994; Balakrishnan & Soderstrom, 2000). Noreen and
Soderstrom (1997) examine hospital overhead costs and discover that it is more accurate
to predict costs by assuming all costs are fixed instead of being under the conjecture that
84
they are variable and vary proportionately to activities. They also find evidence that
costs are more inclined to adjust when there is an increase in activities, as opposed to a
decrease in the cost drivers. This is consistent with Cooper and Kaplan (1988) who
observe that managers more readily raise costs when activities intensify than lower costs
when the activity levels decline.
The asymmetric behaviour of cost is termed as “sticky” in the seminal paper by
Anderson et al. (2003). “Sticky” costs elevate more rapidly as the volume of activities
increase than when they decline as volume of activities plunge with an equivalent
amount. Anderson et al. (2003) examine the “sticky” cost behaviour by associating
movements in SGA costs to contemporaneous changes in net sales during revenue-
increasing and revenue-decreasing periods. SGA expenditure is considered as a
significant variable in their model because many of its components are sales-driven.
Anderson et al. (2003) demonstrate that the directions of the movement in activity levels
do affect the extent of changes in cost: SGA costs rise 0.55 per cent for every one per
cent increase in sales revenue, but drop only 0.35 per cent for every one per cent
decrease in sales revenue. SGA costs are “sticky” because managers are making
asymmetric adjustments in firms‟ committed resources as a result of their expectation of
future demand. Managers intensify necessary resources in response to increased demand
and incur additional SGA expenditure but when demand falls, managers need to assess
its nature. If the deteriorating demand is more likely to be permanent, managers need to
eliminate committed resources quickly and to substitute such resources only when the
85
sales are reinstated subsequently. This involves adjustment costs such as firing costs for
labour (i.e. severance pay to retrenched employees), re-hiring costs (for example,
searching and training costs for new employees) as well as installing and disposal costs
for equipment. If managers anticipate the fall in demand as temporary in nature and
expect it to recover soon, they prefer to hold on to the unutilised committed resources
and incur operation costs, thus “stickiness” of SGA costs occurs. Consequently, the
costs are inclined to “stick” and do not reduce proportionately even when activity level
drops (Weiss, 2010).
The findings of asymmetric behaviour of SGA costs by Anderson et al. (2003)
motivated researchers to undertake studies to validate their results. Balakrishnan,
Petersen and Soderstrom (2004) examine therapists‟ salaries in the US and found that
the level of capacity utilisation in therapy clinics affects managers‟ reaction to changes
in activity levels, thereby determining the extent of cost “stickiness”. Figure 2.2
illustrates three cost functions, namely, “sticky” cost, normal cost and “anti-sticky” cost
under varying capacity utilisation as described by Balakrishnan, Petersen and
Soderstrom (2004).
86
Figure 2.2 Asymmetric Behaviour of “Sticky” Cost
Source: Weiss (2010), pg 1444.
In Figure 2.2, “sticky” cost function is depicted by the bold line when firms are
operating at high capacity at level A while “anti-sticky” cost function is represented by
the dashed line when firms are experiencing idle capacity at level A. The line in between
shows the normal cost function when firms are utilising their normal capacity at level A.
When firms are under high capacity utilisation (strained capacity), managers do not
reduce their committed resources immediately if there is a drop in the activity level so as
to ease pressure on the strained resources. This is also applicable when the declining
demand is viewed as temporary. However, any increase in demand is likely to prompt an
A
Cost
Activity level
"Sticky"
Normal
"Anti-Sticky"
87
increase in resources that are required when firms are already experiencing high
capacity utilisation. Thus, “sticky” cost behaviour will be portrayed because the
response to a drop in activity level is smaller than the reaction to an equivalent increase
in activity level. This phenomenon is seen by the bold line representing the “sticky”
cost function in Figure 2.2.
Conversely, managers tend to use idle resources or “slack” to fulfil the increased
demand if firms are experiencing an excess capacity condition. When there is a drop in
activity level, managers may view it as confirmation of a permanent reduction in
demand, hence react more responsively. This result in “anti-sticky” costs as the cost
reaction to the activity decrease is more than the cost response to activity increase and it
is shown by the dashed line in Figure 2.2. However, when the firms are operating at
normal capacity utilisation, Balakrishnan et al. (2004) found no significant differences
in cost response when there is either an increase or a decrease in activities. Their
findings propose that capacity utilisation determine cost “stickiness”, thereby suggesting
extra care in interpreting the conclusion of Anderson et al. (2003) on “sticky” costs.
Banker, Byzalov, Ciftci and Mashruwala (2012) propose that managerial expectations
arising from prior sales changes play a critical role in deciding the structure of cost
asymmetry, i.e., “stickiness” and “anti-stickiness”. After a prior sales increase
(decrease), managers are optimistic (pessimistic) on future demand and more (less)
likely to increase their investments in resources when current sales increase and have a
88
lower (higher) tendency to remove committed resources when current sales decrease. As
a result, cost “stickiness” is noted after prior sales improvement whereas “anti-stickiness”
is demonstrated after a drop in prior sales.
Other studies investigate the different aspects of asymmetric behaviour of SGA costs,
for example, Balakrishnan and Gruca (2008) identify that cost “stickiness” is greater in
core activities of organizations compared to their auxiliary or supportive services. This
is due to the vital nature of core activities associated to the firms‟ missions and thus,
greater adjustment costs are involved when needs arise to change firms‟ capacities.
Dalla Via and Perego (Forthcoming) examine cost “stickiness” of Italian small and
medium-sized firms but find no evidence of asymmetric cost behaviour in their SGA
expenditure. Weidenmier and Subramaniam (2003) investigate the magnitude of
revenue change and discover that there is no “sticky” cost behaviour in SGA
expenditure when there is little changes in revenue. However, when the revenue change
is more than 10 per cent, SGA costs will be “sticky”. They have also found varying
degrees of SGA costs “stickiness” among different industries due to firms‟ inventory
and fixed assets holdings as well as employee intensity.
Cost “stickiness” also behaves differently across countries. Calleja, Steliaros and
Thomas (2006) reveal that operating costs18
of firms located in France and Germany are
18 Operating costs are made up of cost of goods sold and SGA costs.
89
more likely to be “sticky” than those of the US and UK firms. This is explained by the
different systems of corporate governance as well as varying degrees of pressure from
the market for corporate control. On the other hand, Banker and Chen (2006b) analyse
asymmetric cost response from a macroeconomic perspective using 19 countries who
are members of the Organization for Economic Co-operation and Development (OECD)
for the years 1996 to 2005. They found that the level of cost “stickiness” among these
countries is determined by labour market characteristics, for instance, collective
bargaining of labour agreements, level of unemployment benefits and the stringency of
employee protection law. Banker, Byzalov and Chen (2013) confirm that stricter
employment protection legislation gives rise to a higher degree of firm-level cost
“stickiness”. They attribute this scenario to the “economic theory of sticky cost”, i.e., the
presence of adjustment costs in managerial decisions on resource commitment.
Cost “stickiness” may also arise due to agency problem (Anderson et al., 2003).
Managers with empire building instincts have a greater propensity to build their firms
beyond theirs optimal levels or maintain “slack” for their own personal interests (Jensen,
1986). According to Chen et al. (2012b), these managers are more likely to boost SGA
expenditure in the form of office payroll, sales commissions, travel, entertainment,
office rental and office expenses when sales grow but prefer to delay the removal of
such costs when demand drops, resulting in cost asymmetry behaviour. Furthermore,
managers‟ reluctance to downsize adds to the degree of SGA costs “stickiness”. Indeed,
Chen et al. (2012b) document a positive connection between agency problem and the
90
asymmetry degree of SGA costs, but the positive impact is mitigated by strong corporate
governance. Conversely, Kama and Weiss (2013) discover that self-interested managers
who are motivated to meet earnings target or wanting to achieve financial analysts‟
earnings forecast will accelerate the removal of committed resources when sales drop.
This is so even if the sales demand is expected to be recovered soon. As such, Kama and
Weiss (2013) find that deliberate managerial action in adjusting corporate resources has
reduced, rather than increased, the extent of cost “stickiness”.
The asymmetric cost behaviour affects the accuracy of analysts‟ earnings forecast and
analyst coverage. Weiss (2010) observes that firms with more explicit cost “stickiness”
exhibit higher volatility in future earnings, thus lessening the accuracy of analysts‟
earnings forecast. This is in line with Banker and Chen (2006a) who suggest the
significance of incorporating “sticky” cost behaviour in predicting firms‟ future
profitability. Weiss (2010) also confirms that firms with “stickier” cost attract lesser
analyst following, nonetheless, investors seem to take into consideration the asymmetric
cost behaviour when they evaluate these firms.
Banker, Basu, Byzalov and Chen (2012) discover that both cost “stickiness” (asymmetry
cost response) and conservatism (asymmetric earnings response to news) (Basu, 1997)
jointly explain asymmetric relation between earnings and stock returns, i.e., the
association between earnings and stock returns is stronger for negative returns rather
than for positive returns.
91
Recent studies, for example, Anderson and Lanen (2009) and Balakrishnan, Labro and
Soderstrom (2011) disagree with the “sticky” cost behaviour from previous studies as
they found there is no asymmetric cost response. Nevertheless, Banker, Byzalov and
Plehn-Dujowich (2011a) argue that the shortcomings in the assumptions and
econometric analyses presented in the studies by both Anderson and Lanen (2009) and
Balakrishnan et al. (2011) have rendered their viewpoints unjustifiable.
2.3.3.2 Value Relevance of SGA Costs
Due to the nature of “stickiness” of SGA costs, Anderson et al. (2007) question the
validity of interpreting a rising SGA costs/sales ratio in the fundamental analysis as an
inefficient way of doing business particularly when revenue declines. They extended
Abarbanell and Bushee‟s (1997) earnings prediction model but divided firms into
increasing and decreasing sales. Their empirical evidence shows that SGA costs provide
different signals during revenue-increasing and revenue-decreasing periods. An
improvement in SGA costs/sales ratio during declining sales reflects managerial
optimistic expectation of forthcoming recovery of sales demand, resulting in retention of
committed resources. Consequently, a positive relationship is observed between an
increase in SGA costs/sales ratio and future earnings when sales falls whereas a negative
association is found when sales increase.
92
Few empirical studies on information contents suggest that SGA costs exhibit a positive
influence on future profitability. Banker, Huang and Natarajan (2006) justify that SGA
costs (excluding R&D costs and advertising expenditure) create intangible assets in the
form of customer royalty and operating efficiency, thereby depicting a six-year positive
economic benefit on current and future operating income. They also demonstrate that
investors appear to recognise the implicit assets value in SGA expenditure even though
it is required by the IAS to be expensed off immediately. Banker et al. (2011b) proceed
to examine the impact of SGA costs in the executive labour market. They predict that
the future value-creation ability of SGA expenditure encourages managers to increase
such expenditure after receiving long-term compensation. Banker et al. (2011b) find that
an increase in SGA costs after managers received new equity incentive is only depicted
in firms where SGA expenditure creates high future value. Conversely, SGA costs do
not increase in companies where low future values are generated after firm managers
receive their long-term compensation. This is because managers acknowledge the ability
of equity incentive in generating long-term value for firms when their SGA costs create
increased earnings.
To better understand value relevance of SGA costs, Baumgarten, Bonenkamp and
Homburg (2010) argue that it is critical to differentiate whether an increase in SGA
costs/sales ratio arises from management intention to improve profitability or is due to
deficiencies in cost control. An increase in the SGA costs/sales ratio is considered as
intended by management if firms‟ SGA costs/sales ratio is below its industry average,
93
indicating effective cost management and therefore should increase future firm
profitability. By contrast, an increase in the SGA costs/sales ratio is not to be expected
by the management if firms‟ past SGA costs/sales ratio is above the industry average
and thus would not have any impact on firms‟ profit. Baumgarten et al. (2010) find a
positive impact of an increasing SGA costs/sales ratio on firms‟ future profitability only
for SGA costs-efficient companies and conclude that an intentional increase in SGA
costs/sales ratio exerts positive consequences on operational efficiency leading to
escalating profitability.
2.3.3.3 Determinants of SGA Costs
R&D expenditure forms part of SGA costs and thus, factors that determine R&D
expenditure are also determinants of SGA costs. These determinants were outlined in
item 2.3.1.3 and they are firm size, internal finance, financial leverage, industry,
geographic location, industrial clusters, diversification strategy, corporate governance
and the top management team. The following items (a) to (e) outline five additional
determinants of SGA costs which are derived from Banker et al. (2011b) whose study
examine future value creation of SGA costs.
94
a) Property, plant and equipment
Banker et al. (2011b) suggest that firms spending more in tangible assets like property,
plant and equipment will reduce their SGA costs. This reflects the resource constraints
faced by most firms and highlights the importance of efficient capital allocation.
b) Industry concentration
Firms operating in competitive industries are prone to spend more in SGA costs.
Therefore, industry concentration is negatively associated with SGA costs.
c) Growth opportunities
Higher growth opportunities are positively associated with SGA costs as high-growth
firms are able to generate more funds for capacity expansion.
d) Standard deviation of return on assets
Banker et al. (2011b) suggest that firms operating under uncertainty are more likely to
invest more in SGA costs to derive more future value. Firms operating under uncertain
business environment possess high standard deviation of return on assets.
e) Employee intensity
Employee intensity is derived by dividing the number of employees by sales. Firms
hiring a higher number of employees tend to invest more in costs related to human
resources, thus incurring higher SGA expenditure (Chen et al., 2012b).
95
2.4 Chapter Summary
This chapter critically reviews the existing literature on stock price informativeness and
corporate expenditure. It commences with a review of literature on the characteristics,
measurement and significance of stock price informativeness, followed by the empirical
evidence provided by cross-country and firm-level studies in this area. Then, the chapter
evaluates the relevant literature on corporate expenditure comprising R&D expenditure,
CAPEX and SGA costs, particularly on their characteristics, benefits and determinants.
The next chapter (Chapter 3) outlines the theoretical framework of the current research
and develops the hypotheses that are inter-related to stock price informativeness,
corporate expenditure and information asymmetry.
96
CHAPTER 3
THEORETICAL FRAMEWORK AND HYPOTHESES DEVELOPMENT
3.1 Introduction
The preceding chapter (Chapter 2) presents the literature review of two main
components of this study, namely, stock price informativeness and corporate
expenditure. The literature on stock price informativeness includes an in-depth review of
its characteristics, measurement and significance as well as the relevant empirical
evidence provided by cross-country and firm-level research. The previous chapter also
presents the relevant literatures on R&D expenditure, CAPEX and SGA costs,
particularly on the characteristics, benefits and determinants in relation to corporate
expenditure.
This chapter (Chapter 3) presents the theoretical framework and hypotheses
development of the current study. An overview of the empirical literature on the theories
underpinning the current study is provided under the following two captions, namely,
“stock price informativeness and corporate expenditure” and “information asymmetry”
in Sections 3.2 and 3.3 respectively. Accordingly, four hypotheses are formulated to
predict the association between stock price informativeness and corporate expenditure as
well as to examine whether this relationship is dependent on information asymmetry. A
97
research model is then presented in Section 3.4 while Section 3.5 provides the summary
of this chapter.
3.2 Stock Price Informativeness and Corporate Expenditure
This study investigates the association between stock price informativeness and
corporate expenditure. In doing so, this item outlines the learning theory and
subsequently formulates the hypothesis that is to be tested.
Existing corporate finance literature asserts that managers can learn about their firms
from their own stock prices (Dow & Gorton, 1997; Subrahmanyam & Titman, 1999). It
is referred to as “learning hypothesis” or “learning theory” and the idea originates from
Hayek (1945) who opines that when information is conflicting and imperfectly
possessed by different individuals, the price system efficiently combines the dispersed
information and transmits it across diverse market participants. It enables efficient re-
coordination of scarce resources that can benefit the whole economy. In the stock
market, information is produced, aggregated and communicated into market prices
through trading activities of various investors and speculators (Grossman & Stiglitz,
1980; Glosten & Milgrom, 1985; Kyle, 1985). Dow and Gorton (1997) and
Subrahmanyam and Titman (1999) provide the theoretical framework of learning theory.
These authors suggest stock prices may possess some new information that managers do
98
not have and in turn will induce them to deduce this information to improve their
corporate decisions.
Dow and Gorton (1997) developed a stock market model whereby managers have the
discretionary power to make making investments decisions.19
According to them,
managers have some private information about the value of the firms but do not know
all the relevant information to make prudent investment decisions. They explain that the
flow of information in the capital market is bi-directional as the market ascertains the
quality of managers‟ decisions while at the same time, managers learn more about the
market‟s valuation of future investment opportunities of their firms. Information about
firms‟ growth opportunities may flow from stock market into the companies through
stock prices. More specifically, high stock prices may convey information of market‟s
assessment on firms‟ potentials. Firm managers can then improve their decisions by
observing the stock prices, resulting in greater number of investment projects.
Consequently, Dow and Gorton (1997) suggest that, in an equilibrium situation, stock
markets lead corporate investment by communicating essential information to managers.
Moreover, Subrahmanyam and Titman (1999) highlight that information conveyed by
stock prices guide financing decisions. They scrutinize the connection between
informational substance of stock prices and firms‟ options to solicit for either private or
19 The managers, however, must be given the appropriate incentives that are linked to stock prices in their
remuneration contracts to reduce agency costs (Dow & Gorton, 1997).
99
public financing. The researchers highlight that public financing is more beneficial than
private financing in providing managers with information derived from the stock market.
This is because the aggregate information generated across many stock market
participants, albeit its diversity, could provide more meaningful signals, thereby
improving the allocation of scarce resources. Other related models on learning theory
are developed by Dye and Sridhar (2002), Dow and Rahi (2003) and Goldstein and
Guembel (2008).
Dye and Sridhar (2002) show that stock prices combine new private information from
various investors that managers have yet to possess. This is probably caused by a lack of
appropriate direct communication channel between firms and various market
participants outside the trading process or even due to investors‟ reluctance to share
information (Chen et al., 2007; Bakke & Whited, 2010; Frésard, 2012). This new
information, for instance, is about appraisal of future investment or growth prospects;
project‟s appropriate cost of capital; future demand of firms‟ products; firms‟
relationships with various stakeholders such as competitors, employees and suppliers; as
well as future financing opportunities (Chen et al., 2007; Frésard, 2012). The
information, however, is unlikely to be related to the technology used by firms as the
managers are expected to know more about technological issues of their firms (Chen et
al., 2007).
100
Stock prices play the fundamental role of information production (Dow & Gorton, 1997)
and resource allocation (Dye & Sridhar, 2002; Goldstein & Guembel, 2008) as the
feedback generated from stock prices can direct corporate investment. Consequently, the
learning theory implies that the financial market influences real economic activity
(Morck, Shleifer, Vishny, Shapiro & Poterba, 1990).
There is growing empirical evidence supporting the learning theory. For example, Chen
et al. (2007) examine the effect of stock price informativeness on the sensitivity of
investment-to-price. They hypothesize that managers learn new valuable information
from the private information embedded in the stock prices to make corporate decisions.
Generally, firm managers make decisions based on public and private information
revealed by stock prices as well as private information they themselves know but has yet
to be reflected in the stock prices. Chen et al. (2007) argue that corporate investment is
more sensitive to stock prices if the prices reveal information that is new to the
managers for their decision making. Therefore, any piece of information revealed by
stock prices that is known by firm managers will not have any impact on managerial
investment decisions. This information is already factored in managers‟ past investment
decisions and will, in turn, reduce the sensitivity of investment-to-stock prices.
The variable of interest in Chen et al. (2007) is represented by an interaction between a
measure of private information (using either value of 1-R2 or Probability of Informed
101
Trading, PIN measure)20
and Tobin‟s Q. They found a significant positive relationship
between the interaction variable and investment, indicating that firms‟ corporate
investment is more sensitive to stock prices when the informativeness of stock prices is
greater. The positive association displayed also implies that the stock prices reveal new
private information that managers have learnt and used in their corporate decisions,
thereby providing evidence of the learning theory.
Bakke and Whited (2010) examine whether corporate investment decisions are
determined either by private information reflected by stock prices or caused by
mispricing. They use an econometric model to isolate stock price movements that are
pertinent to investment and other irrelevant effects. Bakke and Whited (2010) observed
that investment decisions are not influenced by stock market mispricing. Their results
are consistent with Chen et al. (2007) and it is likely that managers exploit valuable
information learnt from stock prices to make their corporate decisions.
Frésard (2012) expects that the private information feedback effect of stock prices on
firms‟ future strategic issues can improve the information asymmetry environment,21
thereby influencing corporate actions such as corporate cash savings. Adapting the
20 Probability of Informed Trading (PIN) measure was developed by Easley, Kiefer and O'Hara (1996). It
is a measure of private information in stock prices by estimating directly the probability of informed
trading in a stock.
21 This is because investors possess some information that managers may have neglected or be unaware of,
resulting in imperfect information environment (Frésard, 2012).
102
approach of Chen et al. (2007), Frésard (2012) investigates the link between stock price
informativeness and the sensitivity of cash savings-to-stock prices. He reports that cash
savings are more sensitive to stock prices when the prices incorporate greater
information that is new to the managers. This finding provides evidence that corporate
cash savings are affected by managerial learning from the stock market.
Foucault and Frésard (2012) document that the investment-to-price sensitivity of cross-
listed firms in the US is significantly greater than those of non-cross-listed-firms. Cross-
listed firms are those that have listed their equity shares in several stock exchanges
(Foucault & Gehrig, 2008). Foucault and Frésard (2012) analyse 633 foreign firms that
are cross-listed on US exchanges for the years 1989 to 2006 by following Chen et al.‟s
(2007) methodology. Relating learning theory to cross-listing, Foucault and Frésard
(2012) suggest the latter provides additional trading platforms where new private
information can be exploited. Furthermore, informed traders could be more superior
than firm managers in evaluating firms‟ corporate strategy as they are probably better in
gaining access to information on foreign demand of firms‟ products or growth prospects
of foreign operations. Therefore, cross-listing improves the informativeness of stock
prices about the value of firms‟ future cash flows (Foucault & Gehrig, 2008), thus
conveying more private information that is new to managers. Firm managers learn and
extract valuable feedback for their corporate decisions, thus making investment of cross-
listed firms more sensitive to stock prices and more advanced in resource allocation.
103
Other studies also report that corporate decisions are influenced by the information
content of stock prices. Luo (2005) presents that managers learn from observing the
market reaction to conclude the merger and acquisition deals of their firms. He suggests
that market participants, such as financial analysts and institutional investors could be
more superior than the companies involved in the merger and acquisition deal in
evaluating relevant issues. These market participants will then provide appropriate
signals to the firms as to their acceptance or refusal of the merger and acquisition
proposals by taking their positions in the stocks of the companies involved. Luo (2005)
analyses a total of 2,114 merger and acquisition announcements in the 1990s and
discovers that the abnormal return experienced around the announcement date strongly
predicts whether the deals are finally carried out or are cancelled. He attributes the
findings to learning theory and opines that managerial learning frequently takes place
when it is easier to cancel the announced merger and acquisition deal or when the
market is expected to know more than the managers.
Giammarino, Heinkel, Hollifield and Li (2004) investigate a total of about 3,500
seasoned equity offerings in the US, of which 96 per cent are finally completed while
the remaining are withdrawn subsequently. They observe that stock prices movement
after announcement of the equity issue leads to managers‟ decision to either proceed or
withdraw their placement. Consistent with the learning theory, the researchers suggest
that the registration of seasoned equity offerings encourages information collection by
the market on the relevant information that managers do not have. The market will
104
reflect the impact of the new issue on firm value through stock prices and managers
seem to learn from the prices and respond accordingly.
In summary, the learning theory suggests that firm managers learn new valuable private
information by observing their own stock prices. They will then incorporate this
feedback about the fundamentals or prospects of their firms to improve their corporate
investment decisions, leading to increased firm value (Luo, 2005; Chen et al., 2007;
Frésard, 2012). Firms allocate capital more efficiently when their stock prices convey
larger private information (Durnev et al., 2004). Thus, the stock market is a good
predictor of business cycles as it guides investment decisions and managers will
continue to react to stock prices even when the prices fluctuate excessively (Fischer &
Merton, 1984). Nonetheless, empirical research does not seem to provide any direct
guidance on the relationship between the level of idiosyncratic volatility and corporate
expenditure.
It is predicted that when firms‟ stock price informativeness is at a low level, i.e., when
firms‟ stock prices co-move more extensively with the market and industry, firm
managers learn this feedback from the stock prices. It is argued that firm managers are
likely to actively respond by increasing their corporate expenditure level. All these
corporate expenditures are shown in prior studies to contribute to firm performance. For
example, R&D expenditure creates knowledge assets and contributes to firms‟
productivity and long-term shareholders‟ wealth, CAPEX contributes to firms‟
105
profitability while SGA expenditure is a type of input resource expenditure that creates
intangible assets and generates long-term firm value. Managers aim to provide positive
signals to market participants as well as to convey additional valuable and relevant
information about firms‟ future earnings, thereby boosting investors‟ confidence level
with regards to firms‟ future performance.
When firms level stock price informativeness is at a high level, firms‟ stock prices track
closely to their fundamental values, exhibiting high efficiency of resource allocation in
these firms (Durnev et al., 2003). Market participants are better informed of firms‟
future cash flows and growth opportunities from the current stock prices (Durnev et al.,
2003; Jiang et al., 2009). In addition, high stock price informativeness is also associated
with better management decisions (Chen et al., 2007; Frésard, 2012). Firms‟ future
performance is perceived positively by the capital market and thus, it may not be
necessary for managers to maintain a high level of corporate expenditure. It is argued
that managers extract valuable information from the stock market and are likely to react
by maintaining a lower level of corporate expenditure. Consequently, a negative
relationship between stock price informativeness and corporate expenditure is predicted.
How quickly managers can adjust to the corporate expenditure level is a matter of
interest. There might be a delay of stocks price reactions as managers take time to gather
private information from the capital markets and then initiate relevant changes to
corporate expenditure. Formal procedures such as board approval might also be required
106
to effect any variation in R&D expenditure, CAPEX and SGA costs. Merton (1987)
explains that the assumption of immediate dissemination of all available information
and instant investors‟ reactions of Efficient Market Hypothesis (Fama, 1970) do not
always hold. There is also increasing evidence that the capital market is not
informationally efficient and stock prices might take years before they fully reflect
available information (Kothari, 2001). Therefore, a lead-lag approach is likely to be
more appropriate in this study by linking firms‟ current year stock price informativeness
to their subsequent year‟s corporate expenditure level.
Consequently, this study examines the following hypothesis in an alternative form:
H1: The stock price informativeness of a current year is negatively associated with
corporate expenditure in the subsequent year, ceteris paribus.
3.3 The Role of Information Asymmetry
This study further examines how information asymmetry plays a role in the relationship
between stock price informativeness and corporate expenditure. Information asymmetry
theory is applied to analyse the impact of imperfect information environment on the
relationship between stock price informativeness and corporate expenditure. The
development of three hypotheses is then outlined in detail for each of the proxies of
107
information asymmetry adopted in this study, namely, firm size, analyst following and
bid-ask spreads.
The issue of information asymmetry is central in the finance theory and has attracted
much attention from the capital markets and regulators (Armstrong, Balakrishnan &
Cohen, 2012). Information asymmetry occurs when one party enjoys “informational
advantage” over others (Brennan & Tamarowski, 2000). This market friction hinders
efficient allocation of resources in the capital markets and is primarily caused by
unequal dissemination of information between different parties. Information asymmetry
introduces two types of problems, namely, adverse selection and moral hazard. These
problems are also known as “hidden information” and “hidden action” respectively
(Arrow, 1985).
The first problem of information asymmetry, adverse selection or “hidden information”
occurs when the contracting parties are assumed to possess heterogeneous information.
One party is more informed than the other party, in other words, possess some private
information about the transaction (Armstrong et al., 2010). The extent of informational
awareness between parties brings about inability to distinguish between good and bad
quality of products or services. This is known as “lemons” problem as illustrated by
Akerlof (1970) using the automobile market as an example. He explains that
information asymmetry exists, for example, when car sellers know more about the
quality of their used cars than the potential buyers. The prospective buyers are only
108
willing to pay a certain price regardless of the quality of the used cars as it is difficult for
them to identify cars that are of better quality versus lemons (bad quality). The sellers,
on the other hand, are unable to acquire the true value of these cars and are therefore
reluctant to trade their used cars or may even withdraw totally from the market. Akerlof
(1970) concludes that the adverse selection may have downgraded the average quality of
goods and services.
The second problem of information asymmetry, namely, moral hazard or “hidden action”
arises when individual actions cannot be effectively observed during execution of
contracts although both parties are assumed to have homogeneous information at the
inception of the contracts (Hölmstrom, 1979; Armstrong et al., 2010). “Hidden action”
applies an ex-post concept22
as private information pertaining to the unobservable
behaviour of one party may develop during the execution of a contract and this
unobservable action could be detrimental to the other party‟s interest (Douma &
Schreuder, 2008, p. 73). The problem of moral hazard is prevalent in the insurance
business and among employment contracts (Pauly, 1974; Hölmstrom, 1979). Arrow
(1963) relates moral hazard to the incentive effect of insurance on insured‟s behaviour.
The insured tend to behave incautiously, or may even act with bad intention. As it is
impossible for the insurance companies to observe the real cause of the claim, i.e.,
22 While moral hazard or “hidden action” is an ex-post scenario, adverse selection or “hidden information”
relates to an ex-ante situation as private information exists before transactions took place.
109
whether it is due to negligence or fraud, the insurance coverage will be discontinued
when the insurance claims multiply.
After analysing the adverse selection and moral hazard problems highlighted by Arrow
(1985), one can conclude that information asymmetry plays a fundamental role
especially in an agency relationship. According to Jensen and Meckling (1976), agency
relationship is a contract where the principals appoint agents to carry out some duties on
their behalf and this involves delegation of authority in making decisions. Thus, in terms
of the firm-specific information hierarchy, managers (agents) are on average viewed as
the most informed while shareholders (principals) are the least informed (Armstrong et
al., 2010). Healy and Palepu (2001) suggested a situation where there is a mixture of
good and bad (lemon) business proposals. Both investors and managers are rational and
they evaluate these business ideas based on their own information. In the event investors
are unable to discriminate between two types of business proposals, these proposals will
be valued on an average basis. Therefore, the capital markets will inaccurately value
those business plans if adverse selection problem prevails.
Moral hazard problems (hidden action) can also take place as a result of information
asymmetry existing between principals and agents. The principals (shareholders) are not
able to observe the agents‟ (managers) behaviour, or obtain perfect information about
the agents‟ actions (Armstrong et al., 2010). Firm managers possess private information
about firms‟ prospective earnings stream that current and potential shareholders do not
110
have. Richardson (2000) suggests that information asymmetry allows managers to
engage in earnings management (hidden action) for empire building and deriving
perquisites while Verrecchia (2001) observes that self-interested managers withhold
information when firms are performing badly.
Furthermore, information asymmetry occurs among investors, that is, between informed
and uninformed investors. This creates adverse selection problem as the informed
investors trade on private information they possess about the true value of the firms
while the uninformed investors rely more on public information available (Brown &
Hillegeist, 2007). In addition, individual investors may not have the time, capacity or
access to collect and process information on firms and managers (Levine, 1997;
Richardson, 2000). Thus, investors will “price-protect” themselves against potential
losses from trading with informed investors by demanding a discount to purchase the
firm shares (Myers & Majluf, 1984; Merton, 1987). This price-protection is reflected in
reduced market liquidity (Glosten & Milgrom, 1985; Welker, 1995). Firms will then
have to issue shares at a discount to motivate potential shareholders to buy firms‟ shares
in illiquid markets, resulting in lower share issuance proceeds and higher cost of capital
(Bhattacharya & Spiegel, 1991; Leuz & Verrecchia, 2000).
Consequently, it is important to alleviate information asymmetry to strengthen the
efficiency and transparency of the capital markets. Better information about firms would
enhance capital market liquidity to facilitate long-term high-return corporate
111
investments (Bushman & Smith, 2001) and reallocate resources to their most efficient
uses leading to long-run economic growth (Merton, 1987). This can be achieved by
timely disclosures of value-relevant information by firms and information collection by
investors (Frankel & Li, 2004). Monitoring can also be carried out to solve moral hazard
problem (Hölmstrom, 1979). An example is to design an efficient compensation contract
where the principal (board of directors or shareholders) identifies observable
performance measures and provides incentives to the agent (CEO) to be accountable and
takes the desired actions (Armstrong et al., 2010) such as voluntary disclosures (Healy
& Palepu, 2001).
Financial reporting has been recognized as a tool to mitigate information asymmetry
problem (Healy & Palepu, 2001). Firms‟ commitment to timely voluntary disclosure of
high-quality accounting information serves to lessen the potential losses from trading
with better informed investors (Bushman & Smith, 2001), to mitigate mispricing
dilemma (Healy & Palepu, 2001) and to reduce cost of capital (Verrecchia, 2001).
Mandatory disclosure of private information as required by regulation can also provide
shareholders and outside directors with relevant and reliable information and for
monitoring purpose. Information intermediaries like financial analysts and rating
agencies also contribute to the gathering and production of private information about
firms and aid in detecting managerial misbehaviour (monitoring role) (Healy & Palepu,
2001).
112
The economics, accounting and finance literature introduces various proxies for the
information asymmetry. The proxies frequently used and adopted in this study are firm
size, analyst following and bid-ask spreads.
3.3.1 Firm Size
Prior research suggests that firm size is a proxy for the extent of information available
about a firm (Grant, 1980; Armstrong et al., 2010). Large firms in general experience a
greater flow of information than small firms (Beaver, 1968). Atiase (1985) confirms that
the amount of private pre-disclosure information produced and disseminated is higher
for large firms. According to Bhushan (1989a), large firms possess more sources of
information and in general make more public announcements as opposed to small firms.
He reports that the marginal cost of information collection declines as firm size
increases. Collins, Kothari and Rayburn (1987) find that a higher number of traders and
analysts are processing information that is mainly available for large firms. These
market participants invest additional resources to gather more information about these
firms and the stock prices of large firms will then become even more informative
(Grossman, 1976; Grossman & Stiglitz, 1980). Hence, big firms are shown to be more
efficient in information production and dissemination (Collins et al., 1987).
Stock prices incorporate information more rapidly in large firms than in small firms
(Brown & Hillegeist, 2007). Freeman (1987) discovers that stock prices of large firms
113
predict accounting earnings more rapidly compared to small firms. This is because
information gathering activities are more intense for large firms as motivated by their
higher market capitalization. Hong et al. (2000) highlight that news flows faster in large
firms. This is mostly likely due to investors‟ preference to focus more on stocks in large
firms compared to small firms as a result of fixed costs of information acquisition faced
regardless of firm size. Diamond and Verrecchia (1991) denote that large firms
generally disclose more public information to reduce information asymmetry as there
will be higher opportunities for them to benefit from increased liquidity and diminished
cost of capital when compared to small firms.
It appears that stock prices of large firms are relatively more informative and small firms
would experience more severe information asymmetry difficulties. However, Bakke and
Whited (2010) show that small firms exhibit larger private information as shown in their
higher idiosyncratic volatility, as opposed to big firms. Chen et al. (2007) also express
that their private information measure, the value of 1-R2, is negatively correlated with
firm size, indicating that more private information is produced for small firms.
Firm managers make corporate decisions using their own private information as well as
public and private information reflected by stock prices. Drawing from the learning
theory, managers extract private information that they have yet to possess from firms‟
stock prices to make appropriate investment decisions (Chen et al., 2007; Bakke &
Whited, 2010; Frésard, 2012). Therefore, only private information that is new to
114
managers is able to influence managerial decisions whilst information that managers
already are aware of would not have any impact on corporate expenditure. Large firms
produce voluminous public information through public announcements, financial
disclosures and information gathering activity by market participants, including analysts
(Atiase, 1985; Bhushan, 1989a). However, this information is already made use by
managers in making their past investment decisions, hence would not be effective for
their current corporate expenditure decisions.
On the other hand, it is anticipated that managers of small firms are able to derive a
greater extent of new firm-specific private information from their stock prices. This
private information could be in a form of market‟s assessments of firms‟ growth
prospect, sales demand or competitiveness that firm managers have yet to possess (Dow
& Gorton, 1997; Subrahmanyam & Titman, 1999). This feedback serves as an input to
managers‟ corporate expenditure decisions.
Consequently, it is predicted that the relationship between a current year‟s stock price
informativeness and corporate expenditure of the subsequent year is dependent on
information asymmetry. The stock prices of small firms reveal new private information
that managers can learn and use in making their decisions on discretionary expenditure.
Therefore, managers of small firms will be more responsive to changes in stock price
informativeness and modify their corporate expenditure accordingly. This reasoning
lead to the following hypothesis:
115
H2a: The negative relationship between a current year‟s stock price informativeness and
the subsequent year‟s corporate expenditure is likely to be stronger for small firms.
3.3.2 Analyst Following
Analyst following (or analyst coverage) proxies for the effort and resources dedicated to
information gathering and it is represented by the number of analysts that cover a firm
(Hong et al., 2000; Frankel & Li, 2004; Frankel, Kothari & Weber, 2006; Armstrong et
al., 2010). Financial analysts act as information intermediaries in the capital markets.
They gather and interpret information from public and private sources, assess the current
financial performance of firms they are following, and prepare reports relating to the
prospects of these firms. Their reports comprise earnings forecast, recommendations to
either buy, sell or hold for shares and bonds, as well as a price target (Healy & Palepu,
2001; Asquith, Mikhail & Au, 2005).
Prior studies indicate that analyst reports are informative, that is, producing a stock-price
reaction (Givoly & Lakonishok, 1979; Lys & Sohn, 1990; Francis & Soffer, 1997) and
they complements financial reports informativeness (Frankel et al., 2006). Frankel and
Li (2004) reveal that firms with less informative financial statements are more likely to
have higher analyst coverage, suggesting that analyst following is a substitute for value
relevance of financial statements. Analysts‟ earnings forecasts are found to be more
superior than forecasts that are generated by time-series models (Fried & Givoly, 1982;
116
Bhushan, 1989b). Consequently, financial analysts convey more timely and new
information (Healy & Palepu, 2001; Asquith et al., 2005), thereby increasing the
informational efficiency of the capital markets (Moyer, Chatfield & Sisneros, 1989;
Frankel et al., 2006). Apart from providing information, financial analysts also act as a
monitoring mechanism to mitigate agency costs (Jensen & Meckling, 1976) and this is
empirically evidenced by Moyer et al. (1989) and Chung and Jo (1996).
Analysts gather and analyse both private and public information and consequently their
roles have a direct impact on the extent of information asymmetry faced by firms
(Armstrong et al., 2012). Increased analyst following is shown to be associated with
lower information asymmetry between managers and shareholders (Frankel & Li, 2004).
This is because analyst are increasing the speed of diffusion of firm-specific information
across market participants when they extend their coverage of firms (Hong et al., 2000).
However, Piotroski and Roulstone (2004) exemplify that firms covered by more analysts
incorporate greater industry and market-level information (instead of firm-specific
information) into stock prices as the analysts possess the necessary expertise and
industry affiliation to better analyse and distribute common industry information.
Share prices in firms with wider analyst following reflect future earnings more rapidly
than those firms neglected by analysts, thereby facilitating an earlier resource
deployment from less productive uses (Ayers & Freeman, 2003). This signifies that
stock prices of firms followed by a higher number of analysts will adjust more promptly
117
to new macroeconomic news, consistent with the study by Brennan, Jegadeesh and
Swaminathan (1993). Hong et al. (2000) found that stock prices adjust much more
sluggishly to bad firm-specific information for thinly followed firms. Similarly, widely
followed firms will portray smaller stock price surprises by firms‟ earnings
announcement, as compared to firms followed by fewer analysts (Dempsey, 1989).
Brennan and Subrahmanyam (1995) and Brennan and Tamarowski (2000) discover that
analyst following is negatively associated with adverse selection costs as measured
using the Kyle (1985) model. This suggests that high analyst coverage lowers
information asymmetry and improves stock liquidity.
In general, the intensity of analyst activities (analyst following) is negatively associated
with information asymmetry because information travels faster across the investing
public for stocks with higher analyst coverage. Nevertheless, prior empirical studies
show that the private information measure, for example, 1-R2 value used by Chen et al.
(2007) is negatively associated with analyst coverage. Bakke and Whited (2010) and
Frésard (2012) use analyst following to represent public information when they examine
whether private information in stock prices determine managerial decisions on corporate
investment or corporate cash savings respectively.
In Chen et al. (2007), high analyst coverage has a negative effect on investment
sensitivity to stock price. They explain that a large proportion of information produced
118
by analysts is obtained from managers. Being fully alert of this information, managers
do not change their investment when the information is reflected in the stock prices,
resulting in lower sensitivity of investment-to-stock prices. According to Chen et al.
(2007), the information released by analysts may be able to improve stock price
informativeness as more managerial information is incorporated into stock prices.
Analysts‟ reports, however, have lower tendency to affect managerial decisions as this
information was already factored in managers‟ past investments decisions. Frésard
(2012) also agrees that the content of the information analysts release is unlikely to be
new to managers as most of the information produced by analysts is mainly derived
from firm managers (Agrawal et al., 2006). As such, less managerial learning is
expected when analysts generate information about a firm‟s prospects.
Easley et al. (1998) demonstrate that analysts do not seem to generate new private
information but introduce more uninformed or noise trading to the stocks. This has
worsened the private information content in stock prices, thereby reducing the
sensitivity of investment-to-price as reported in Chen et al. (2007). Easley et al. (1998)
conclude that analysts rely more on public (rather than private) information for their
recommendations to investors to either buy, sell or hold firms‟ shares.
It is probable that firm managers with low analyst following are able to learn new firm-
specific private information from the stock prices and are therefore more enthusiastic in
adjusting their corporate expenditure in the subsequent year in response to a current
119
year‟s change in stock price informativeness. Therefore, it is expected that the
relationship between a current year‟s stock price informativeness and corporate
expenditure of the subsequent year is dependent on analyst following.
The following hypothesis is examined in an alternative form:
H2b: The negative relationship between a current year‟s stock price informativeness and
the subsequent year‟s corporate expenditure is likely to be stronger for firms with low
analyst following, ceteris paribus.
3.3.3 Bid-ask Spreads
In an imperfect information environment, the less informed investors “price-protect”
themselves against exploitation by the informed traders. Observable market liquidity
measures are used to ascertain the perceived level of information asymmetry that market
participants deal with (Lev, 1988). One of them is bid-ask spreads which directly
quantify the price protection that uninformed investors require as compensation for the
information risk when trading with the informed market participants (Welker, 1995).
The information asymmetry motive for bid-ask spreads originates from Bagehot (1971)
and was formally analysed by Copeland and Galai (1983). Bagehot (1971) points out
120
that a market maker23
will lose generally when trades with the better informed investors
as the latter possess more firm-specific information about the firms‟ true value
(indication of adverse selection problem). Further, the informed investors will only trade
with the market maker when the quoted prices are favourable to them (Lev, 1988). As
such, the market maker needs to recover the losses suffered and this is done through
gains obtained in trades with liquidity traders. Liquidity traders do not have any
informational advantage about firm values but need to trade to fulfil immediate liquidity
requirement. The gains to the market maker are achieved by setting an appropriate bid-
ask spread. The optimal behaviour of the market maker is modelled by Glosten and
Milgrom (1985) and their study shows that bid-ask spreads arise from adverse selection
problem. Larger bid-ask spreads represent more severe information asymmetry (Lev,
1988). Wider bid-ask spreads also signify higher transaction costs incurred in share
trading (Demsetz, 1968; Amihud & Mendelson, 1980) and is associated with lower
trading volumes, hence reducing market liquidity (Hamilton, 1978; Karpoff, 1986).
Bid-ask spreads are extensively used in the past studies to capture the information
environment of firms especially related to disclosure literature. For example, Welker
(1995) finds a negative association between financial analysts‟ disclosure ranking and
23 Market maker, or known as market specialist, is the exchange specialist (for listed securities) or the
over-the-counter dealer (for unlisted securities) who acts as a middleman by holding inventories and
facilitating trade. The market maker match buyers and sellers whose orders fail to arrive concurrently,
thus providing liquidity to the capital market (Bagehot, 1971; Glosten & Milgrom, 1985).
121
firms‟ bid-ask spreads while Healy, Hutton and Palepu (1999) show that increased
disclosure is associated with reduced bid-ask spreads. While these studies focus on US
firms that are highly committed in financial disclosure, Leuz and Verrecchia (2000)
examine German firms that have changed from local accounting standards to either
International Accounting Standard (IAS) or US Generally Accepted Accounting
Principles (GAAP) for their financial reporting. Their empirical results show that firms
who are committed to a higher disclosure standard exhibit lower bid-ask spreads,
indicating that the switch have successfully lessened information asymmetry. Petersen
and Plenborg (2006) also find that voluntary disclosure is negatively associated with
information asymmetry (represented by bid-ask spreads) in firms listed on the
Copenhagen Stock Exchange. The findings of the above studies indicate that disclosure
policy plays a pivotal role in reducing information asymmetry.
Bid-ask spreads are also applied in the field of corporate governance. Richardson (2000)
notes a positive relationship between bid-ask spreads and earnings management. The
imperfect information environment does not facilitate adequate monitoring of
management‟s action due to a lack of funds and information deficiency among outside
stakeholders. This allows managers to manage accruals and earnings for their personal
benefits. By contrast, Chung, Elder and Kim (2010) exemplify that corporate
governance increases financial and operational transparency, thereby reducing
information asymmetry between managers and shareholders as well as among investors.
122
Consequently, firms with higher level of corporate governance are found to have smaller
bid-ask spreads representing lower information asymmetry.
Large bid-ask spreads seem to represent higher information asymmetry when linked to
disclosure of public information (Welker, 1995; Richardson, 2000). Nonetheless, it is
crucial to comprehend the speed of diffusion of firm-specific information by examining
the connection between bid-ask spreads and idiosyncratic volatility. Spiegel and Wang
(2005) explain how idiosyncratic volatility connects with bid-ask spreads by looking at
their respective relationships with stock returns. Bid-ask spreads are found to be
positively correlated with stock returns (Amihud & Mendelson, 1986, 1989; Amihud,
2002) while a positive relationship is noted between idiosyncratic volatility and stock
returns (Lehmann, 1990; Goyal & Santa-Clara, 2003). Spiegel and Wang (2005) further
provide direct empirical evidence that idiosyncratic volatility is positively correlated
with bid-ask spreads.
Chan et al. (2013) also document a negative association between stock price
synchronicity (inverse of idiosyncratic volatility) and bid-ask spreads, an illiquidity
measure. They clarify when stock prices are more correlated with the market or industry,
market makers depend more on the information observed from the market movement24
to lessen the adverse selection risks of liquidity traders and improve market liquidity.
24 It is easier for market makers (market specialists) to observe market-wide information than firm-
specific information (Chan et al., 2013)
123
This implies that bid-ask spreads increase as idiosyncratic volatility improves. Therefore,
it is argued that managers of firms with large bid-ask spreads are accessible to more
firm-specific information and thus opportunities are higher for managerial learning to
improve in them making corporate decisions.
In addition, bid-ask spreads are shown to be higher for small firms due to lower
probability to find a party to trade, hence inventory and processing costs are higher for
the market makers for these firms (Stoll, 2000). Therefore, the rationale for applying the
learning hypothesis for small firms can also be extended to firms with high bid-ask
spreads.
As such, this study predicts that when firms experience high bid-ask spreads, their
managers will be able to learn more from private information revealed by the capital
markets, making them react more vigorously by altering corporate expenditure in the
subsequent year when stock price informativeness of a current year changes.
The above reasoning leads to the following hypothesis:
H2c: The negative relationship between a current year‟s stock price informativeness and
the subsequent year‟s corporate expenditure is more likely to be stronger for firms with
high bid-ask spreads, ceteris paribus.
124
3.4 Research Model
The research model of the current study is depicted in Figure 3.1.
Figure 3.1 Research Model
t= current year
t+1 = subsequent year
H1= Hypothesis 1
H2a= Hypothesis 2a
H2b= Hypothesis 2b
H2c= Hypothesis 2c
H2c
Bid-ask spreads
H2b
Analyst following
H2a
Firm
size
Corporate
Expenditure
(t+1)
H1
Stock Price
Informativeness
(t)
125
The research model displayed in Figure 3.1 highlights the association between a current
year‟s stock price informativeness and corporate expenditure of the subsequent year by
adopting a lead-lag approach. The learning theory suggests that firm managers apply the
new firm-specific information learned from the stock markets to improve efficiency of
their corporate decisions (Luo, 2005; Chen et al., 2007; Frésard, 2012). It is argued in
this study that stock price informativeness induces managers to initiate changes to their
corporate expenditure in three specific areas, namely, R&D expenditure, CAPEX and
SGA costs. When stock price informativeness is at a low level, firm managers will
maintain a high level of corporate expenditure by conveying positive signals about firm‟
future performance to the stock markets in view of the expected benefits deriving from
corporate expenditure. Conversely, a high level of stock price informativeness indicates
that stock prices are reflecting positive information about firms‟ future earnings and
thereby reflecting better allocation of firms‟ scarce resources. Relatively, their managers
need not incur high level of corporate expenditure. Managers learn valuable information
from the stock market and will respond by maintaining an appropriate level of corporate
expenditure. Consequently, Hypothesis 1 postulates that a current year‟s stock price
informativeness is negatively associated with corporate expenditure of the subsequent
year.
Further, the research model exhibited in Figure 3.1 illustrates how the relationship
between a current year‟s stock price informativeness and the subsequent year‟s
126
corporate expenditure is dependent on information asymmetry. Three proxies of
information asymmetry, namely, firm size, analyst following and bid-ask spreads are
used in this study. Large firms and firms with high analyst following as well as firms
with low bid-ask spreads are associated with low information asymmetry as information
flows faster in these firms. However, lesser managerial learning is expected in these
firms. This is because the information generated by large firms though public
announcements and financial disclosure is already utilised by their managers in past
investment decisions while information produced by analysts is mainly sourced from
managers or is already considered in managers‟ corporate decisions. Applying ideas
from the learning theory, the information produced by large firms, firms with high
analyst following and firms with low bid-ask spreads discourages managerial learning
and hence does not have any impact on firms‟ corporate expenditure decision.
Conversely, empirical findings show that more private information is produced for small
firms (Chen et al., 2007; Bakke & Whited, 2010), firms with low analyst following
(Chen et al., 2007) and firms with high bid-ask spreads (Chan et al., 2013). It is
predicted that the higher extent of managerial learning in these firms motivates their
managers to learn quicker and react more “aggressively” in making changes to firms‟
corporate expenditure such as R&D expenditure, CAPEX and SGA costs. Consequently,
Hypothesis 2a posits that the association between a current year‟s stock price
informativeness and the subsequent year‟s corporate expenditure is likely to be stronger
in small firms while Hypothesis 2b predicts that the association between a current year‟s
stock price informativeness and the subsequent year‟s corporate expenditure is likely to
127
be stronger in firms with low analyst following. Hypothesis 2c hypothesizes that the
association between a current year‟s stock price informativeness and the subsequent
year‟s corporate expenditure is likely to be stronger in firms with high bid-ask spreads.
3.5 Chapter Summary
This chapter provides an overview of the empirical literature and the theoretical
framework applied for the current study. The learning theory is used to explain the
relationship between a current year‟s stock price informativeness and the subsequent
year‟s corporate expenditure, while the information asymmetry theory is applied to
understand whether the association between a current year‟s stock price informativeness
and corporate expenditure of the subsequent year is dependent on firm size, analyst
following and bid-ask spreads. As a result, four hypotheses (H1, H2a, H2b and H2c) are
formulated. The research model is then presented to illustrate the hypotheses developed.
The next chapter (Chapter 4) evaluates and outlines the methodology deployed in this
study to examine the hypotheses developed.
128
CHAPTER 4
RESEARCH METHODOLOGY
4.1 Introduction
The preceding chapter (Chapter 3) provides the theoretical framework of the study. The
learning theory is applied to examine the association between a current year‟s stock
price informativeness and the subsequent year‟s corporate expenditure. The information
asymmetry theory is applied to determine whether this association is dependent on
information asymmetry. Four hypotheses are developed and illustrated using a research
model.
This chapter (Chapter 4) describes in detail the research methodology adopted for this
study and outlines procedures that are employed to examine the research problems.
Section 4.2 elaborates on the research paradigm adopted in this study. This is followed
by a description of the principal population data (Section 4.3), outlining sources of
secondary data (Section 4.4), explaining variables measurement (Section 4.5), model
specification (Section 4.6) and selection procedure for choosing the sample (Section 4.7).
Section 4.8 provides an overview of the statistical methodology employed in the current
study and a summary of this chapter is provided in Section 4.9.
129
4.2 Research Paradigm
Research paradigm (also referred to as a philosophical perspective) is an approach to
enhance knowledge by adopting certain theoretical assumptions, research goals and
research methods (Kuhn, 1962). Research is conducted based on researchers‟ own
philosophical stances which are largely dependent upon their personal backgrounds,
histories, cultural contexts and perceptions. This explains why there could be different
answers even when there are similar research questions.
Research paradigms are categorized into different groups, for example, positivism,
constructivism, interpretivism and critical theory, among others (Crotty, 1998, p. 16).
According to Guba (1990) and Ponterotto (2005), these paradigms differ from the
following perspectives:
a) ontology belief which deals with the nature of “reality” that is “how things really
are” and “how things work”,
b) epistemology, which is concerned with how knowledge is created and the
relationship between research participants and the researcher,
c) methodology, which is the process and procedures adopted by researchers in
finding out knowledge, and
d) goal of research.
The philosophical perspective applied to address the research questions formulated in
this study is positivism. The choice of this perspective is derived after considering its
130
ontological, epistemological and methodological standpoints. The ultimate aim of this
research is to assess how firm-level corporate expenditure responds to stock price
informativeness. This study also investigates whether information asymmetry plays a
role in the relationship between stock price informativeness and corporate expenditure.
Positivism is a term coined by Auguste Comte (1798-1857) through his work “Societe
Positiviste” in 1848 (Crotty, 1998, p. 19). Emphasizing on “hypothetico-deductive”
method (Crotty, 1998, p. 32) , this is the main philosophical position in management
studies that assumes that knowledge of the world is obtained through applying scientific
approaches (Eriksson & Kovalainen, 2008, p. 18).
4.3 Population Data
The target population is US public listed companies (PLCs) for the years 2003 to 2009.
Data prior to 2003 are excluded to minimise any possible impact arising from the
Sarbanes-Oxley Act executed in year 2002. This study adopts a lead-lag approach, thus
the data used for the dependent variable, i.e., corporate expenditure are for the years
2004 to 2010 while the data for independent variable being the stock price
informativeness as well as the control variables are for the years 2003 to 2009. To
qualify as a sample for the study, a company must possess a complete set of data in
respect of the dependent, independent and control variables for all the relevant years
covered.
131
4.4 Sources of Secondary Data
Secondary data is collected from numerous sources, namely, Compustat database for
financial data, The Centre for Research in Security Price (CRSP) database for stock
market data, Risk Metrics database for Gompers‟s Governance index, Execucomp
database for directors‟ data and Institutional Brokers‟ Estimate System (I/B/E/S) for
analysts‟ information.
4.5 Variables Measurement
This item outlines how the dependent variable (corporate expenditure), independent
variable (stock price informativeness), proxies for information asymmetry and control
variables used in this study are measured.
4.5.1 Dependent Variable
In this study, the dependent variable, corporate expenditure is measured by using the
following three variables, R&D expenditure, CAPEX and SGA costs:
a) Research and development expenditure
Research and development expenditure is measured using the natural logarithm of
annual R&D costs-to-assets ratio. This ratio scaled by book value of total assets is
commonly used particularly in the strategic management literature (Mizik & Jacobson,
132
2003; Eberhart et al., 2004; Kor, 2006; McAlister et al., 2007; Eberhart et al., 2008; Luo
& de Jong, 2012). According to Eberhart et al. (2008), the ratio of R&D costs/total
assets is more superior than the ratio of R&D costs/market value of equity as the latter
may provide a misleading impression of R&D expenditure caused by the fluctuating
nature of the market value of equity. Kor (2006) is of the view that some firms may not
have sales revenue in their early years of product development, hence he prefers to
standardize R&D investment by total assets. Therefore, R&D costs/total asset ratio is
used in this study because the book value of total assets does not vary much on an
annual basis, hence, it is found to be statistically more stable.
b) Capital expenditure
In this study, CAPEX is represented by the natural logarithm of annual capital
expenditure scaled by total assets. Scaling the annual capital expenditure by total assets
is broadly used in extant literature (Strong & Meyer, 1990; Carpenter & Guariglia, 2008;
Inci et al., 2009).
c) Selling, general and administrative costs
Selling, general and administrative costs are measured by the natural logarithm of the
SGA costs of the current year deflated by total assets. The ratio of SGA costs/total assets
is used by Banker et al. (2006, 2011b) in their studies of value relevance of SGA
expenditure. Most research in the area of SGA costs standardize SGA expenditure by
total sales to examine its asymmetric cost behaviour (Anderson et al., 2003; Anderson et
133
al., 2007; Dalla Via & Perego, Forthcoming) and information contents (Baumgarten et
al., 2010; Janakiraman, 2010). Nevertheless, the ratio of SGA costs/total assets is
adopted in the current study as the book value of total assets is more enduring when
compared to fluctuating sales.
4.5.2 Independent Variable
The independent variable, that is, stock price informativeness is represented by
idiosyncratic volatility. Following Ferreira and Laux (2007) and Gul et al. (2011b),
idiosyncratic volatility in this study is measured using a regression projection of daily
excess stock returns for each firm i for every fiscal year on the returns of the market
index using the CAPM model:
(4.1)
where:
is daily excess stock returns for firm i and is the daily value-weighted excess
stock return of the market portfolio. Both coefficients and are estimated for each
financial year by employing regression analysis. This model assumes that all systematic
risk is captured by the coefficient while the variance of represents idiosyncratic
volatility (unsystematic risk).
134
The value of 1- is obtained from the regression (4.1), where
is the coefficient of
determination of firm i in year t. It represents the relative idiosyncratic volatility, i.e., the
ratio of idiosyncratic variance to total volatility for each firm-year t and it is the
proportion of volatility that is not explained by systematic components (Ferreira & Laux,
2007). As 1- is bound within the intervals [0,1], a logistic transformation is done on
the ratio of (1-R2)/R
2 following Morck et al. (2000) and Ferreira and Laux (2007) to
generate the variable idiosyncratic volatility, Ψ with a more normal distribution.
Therefore, idiosyncratic volatility Ψi,t is formally defined as:
Ψi,t = Ln
(4.2)
where:
Ψi,t idiosyncratic volatility measures the firm-specific stock return variation relative to
market-wide variation and is the coefficient of determination of firm i in year t.
Idiosyncratic volatility is higher when firms‟ stock return is less correlated with market
returns, indicating a more informative stock price. The following variations to the above
model are examined in the additional tests to verify the robustness of idiosyncratic
volatility measure:
135
a) Use different market index.
The equally-weighted market return is used to replace value-weighted market return to
generate a different measure of idiosyncratic volatility (Gul et al., 2011a).
b) Use Fama & French three-factor model (Fama & French, 1993, 1995, 1996)
Following Ferreira et al. (2011) and Gul et al. (2011b), the annual idiosyncratic
volatility is estimated by using the Fama-French three-factor model that is by obtaining
the value of 1-R2 from the regression for each firm year :
(4.3)
where:
is the daily excess return of stock i in day t, is the daily value-weighted excess
market return, is the small-minus-big size factor return, and is the high-
minus-low book-to-market factor return. The daily returns for the small-minus-big
( and high-minus-low ( ) factors are drawn from French‟s website at
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_library.html.
c) Use Brockman and Yan‟s (2009) model
In Brockman and Yan (2009), the idiosyncratic volatility is estimated by controlling
market and industry factors. This is done by regressing firms‟ daily return on
contemporaneous and lagged daily market return, as well as contemporaneous and
136
lagged daily industry return for each firm-year observation, as indicated in Equation 4.4.
Such an approach was adopted by Gul et al. (2010) and Gul et al. (2011a).
(4.4)
where:
is the daily excess return of stock i in day t, is the contemporaneous daily value-
weighted market return, is the lagged daily value-weighted market return,
is the contemporaneous daily industry return and is the lagged daily industry
return.
The industry return for a specific day is created using all firms with the same two-digit
Standard Industrial Classification (SIC) codes. Lagged market and industry return is
included in the regression to control for informed trading that can affect the timing of
incorporation of the market and industry information into stock prices (Piotroski &
Roulstone, 2004; Brockman & Yan, 2009).
4.5.3 Proxies of Information Asymmetry
Prior studies have identified a number of proxies to measure information asymmetry
(Atiase, 1985; Aboody & Lev, 2000; Armstrong et al., 2010; Chung et al., 2010). This
study selects three proxies of information asymmetry, namely, firm size, analyst
following and bid-ask spreads and each of them are measured as follows:
137
a) Firm size
Following Beaver (1968), this study uses the book value of total assets to represent firm
size as one of the proxies for the amount of information available in a firm. Many
empirical studies on information asymmetry tend to use other measures as the proxy for
firm size such as market capitalization (Atiase, 1985; Freeman, 1987) or the market in
which firm shares are traded, for example, over-the-counter versus New York Stock
Exchange (NYSE) (Grant, 1980). The value of total assets portrays more steady patterns
over the years as opposed to market value of equity, thus the former is preferred in this
study. Higher book value of total assets indicates larger firm size.
b) Analyst following
In the empirical literature of information asymmetry, analyst following is represented by
the number of analysts that follow a firm (Piotroski & Roulstone, 2004; Frankel et al.,
2006; Armstrong et al., 2010).
c) Bid-ask spreads
Bid-ask spread is computed as the mean of difference between ask price and bid price
for each stock at the close of trade on the last trading day of the year, scaled by the
average of ask and bid price (Welker, 1995; Richardson, 2000; Chung et al., 2010).
Following Chung et al. (2010), the quoted bid ask spread for a company i at time t is
calculated as follows:
138
(4.5)
where:
is the bid-ask spread for stock i at time t, is the ask price for stock i at
time t, and is the bid price for stock i at time t.
Bid-ask spread for each firm is firstly calculated at the end of the trading day of each
month and then the average bid-ask spread for the year will be computed for each firm.
4.5.4 Control Variables
Control variables are included in the regression. It is preferred econometrically to
include control variables whenever possible (Durnev et al., 2004). In this study, a
similar set of control variables are used for each of the corporate expenditures examined
as these factors generally influence corporate expenditure decisions. Some control
variables that are specific to certain corporate expenditure, such as the variables free
cash flow and employee intensity are added in examining CAPEX and SGA costs
respectively.
The control variables applied are categorized into firm characteristics and corporate
governance control variables. Each of them are outlined and measured as follows:
139
4.5.4.1 Firm Characteristics Control Variables
There are 20 firm characteristics that may influence corporate expenditure decisions and
each of them are delineated below.
a) Firm size
Firm size (SIZE) is an important determinant for all corporate expenditure and it is
expected that as a firm grows, expenditure on R&D, capital projects and SGA increases
(Rothwell, 1984; Vogt, 1994; Banker et al., 2011b). It is measured in this study as the
natural logarithm of total assets and its squared value at the end of the fiscal year (Coles,
Lemmon & Meschke, 2012). Total assets are commonly used to represent firm size in
the empirical research of the learning theory (Bakke & Whited, 2010; Frésard, 2012)
and of stock price informativeness (Gul et al., 2010; Gul et al., 2011a).
b) Analyst following
Analyst following (ANALYST) is measured as the number of analysts following a firm
(Chen et al., 2007; Ferreira et al., 2011). Analyst following is strongly related with firm
size (Bhushan, 1989a), thus firms with higher analyst following is larger and thus are
more financially feasible to invest in R&D initiatives, CAPEX and SGA expenditure. A
positive association between analyst following and corporate expenditure is predicted.
140
c) Firm age
Firm age (AGE) is measured as the natural logarithm of the number of years since the
firm‟s stock is exchange-listed (Coles, Daniel & Naveen, 2008; Ferreira et al., 2011).
Haynes and Hillman (2010) find firm age is positively related to changes in corporate
expenditure. A positive association between firm age and corporate expenditure is
expected as older firms are normally bigger and financially stronger to invest more in
R&D costs, CAPEX and SGA expenditure.
d) Volatility of return on equity
The volatility of return on equity (StdROE) representing business riskiness is calculated
as the standard deviation of annual return on equity (ROE) over the last three years.25
ROE is measured as earnings before extraordinary items divided by the book value of
equity at year end. Banker et al. (2011b) suggest that firms operating under an unstable
environment are more likely to increase their SGA costs in view of its future value-
creation ability. Applying the same rationale, R&D investment contributes substantially
to productivity and its benefit can last for years (Lev, 1999). Therefore, a positive
association is expected between StdROE with R&D expenditure and SGA costs
respectively. However, the relationship sign between StdROE and CAPEX cannot be
predicted because the contribution of CAPEX to future economic value is dependent
25 Volatility of ROE is represented by variance of annual ROE over the last three years in Gul et al.
(2011b). This study uses standard deviation of annual ROE instead.
141
upon whether pecking order model or free cash flow hypothesis prevail in the
companies examined (Vogt, 1994).
e) Cash flow volatility
Cash flow volatility (StdCF) is measured as the standard deviation of cash flow for the
past five years (Harford, Mansi & Maxwell, 2008). This is a second proxy of uncertain
business condition. Similar to StdROE, firms operating under ambiguous cash flow
positions are more likely to invest more in corporate expenditure such as R&D
expenditure and SGA costs to derive more future benefits, therefore a positive
relationship is expected. The predicted sign for the relationship between StdCF and
CAPEX, however, is uncertain depending whether pecking order model or free cash
flow hypothesis exists in the companies examined (Vogt, 1994).
f) Stock return volatility
Stock return volatility (StdRET) is measured by the average standard deviation of daily
stock returns during the fiscal year (Demsetz & Lehn, 1985; Gul et al., 2011b). It
proxies for an ambiguous information environment about firms facing investors in the
capital markets (Bhagat & Bolton, 2008) and “noise” in evaluating managers‟ action
(Gillan, Hartzell & Starks, 2003). Intuitively, when stock return is volatile indicating
higher investors‟ uncertainty on firms‟ information, managers may not be able to obtain
the appropriate feedback from the stock markets and delay in making investment in
R&D expenditure, CAPEX and SGA costs, and vice versa. This reflects a negative
142
association between StdRET and corporate expenditure. However, the StdRET measures
the extent of unstable business environment and is predicted to be positively associated
with corporate expenditure as higher level of R&D expenditure and SGA costs will be
incurred to reap the benefits. As such, the net effect of StdRET on corporate expenditure
is unclear.
g) Market-to-book ratio
The market-to-book ratio (MB) measures firms‟ investment opportunity sets or their
growth prospects, given by the ratio of market capitalization to the book value of
shareholders‟ funds at balance sheet date (Linck, Netter & Yang, 2008). Growth
opportunities are found to be positively related to SGA costs (Banker et al., 2011b), thus,
a positive relationship is predicted between market-to-book ratio and corporate
expenditure.
h) Return on assets
Return on assets (ROA) is the earnings before extraordinary items deflated by total
assets at the end of each financial year (Ferreira et al., 2011). Gordon and Iyengar (1996)
highlight that return on investment is positively associated with CAPEX. Return on
investment is closely related to ROA, hence a positive relationship is predicted between
ROA and CAPEX. Since R&D expenditure and SGA costs are expensed off to the
current year income statements, the earnings figures are negatively affected. As ROA is
143
derived from current year‟s earnings, it is expected that the former is negatively
associated with both R&D expenditure and SGA costs.
i) Working capital ratio
Following Finkelstein and Hambrick (1990), working capital (WC) ratio represents the
degree of availability of immediate resources, referred to as “slack”. It is calculated by
dividing working capital with sales. “Slack” is spare resource that shields the companies
in meeting short-term needs and opportunities (Bourgeois, 1981). Finkelstein and
Hambrick (1990) document a negative association between working capital ratio and a
composite index measuring strategic persistence.26
This indicates that firms with higher
slack will have a lower tendency to change their corporate costs. In this study, a
negative association between working capital ratio and corporate expenditure is
expected as any immediate needs can be fulfilled by slack, hence reducing any need to
incur immediate costs especially in the area of SGA costs.
j) Dividend dummy
Annual dividend dummy (DIV) equals to “one” if firm pays dividend for each fiscal
year, and “zero” otherwise (Gul et al., 2011b). Vogt (1994) suggests managers make
low dividend payout in order for firms to preserve cash flows for CAPEX. The same
26 The composite index of strategic persistence is a summation of variance score of six cost indicators,
including R&D expenditure, CAPEX and SGA costs. A higher strategic persistence index indicates that
firms are persistent in adopting their corporate costs structure.
144
rationale could be applied to R&D expenditure and SGA costs. Therefore, a negative
relationship between dividend dummy and corporate expenditure is predicted.
k) Merger dummy
The merger dummy (MERGER) equals to “one” if there is any merger and acquisition
activity in year t and “zero” otherwise (Gul et al., 2011b). Intuitively, if merger and
acquisition takes place in a firm during the year, CAPEX is expected to be reduced as
the former is itself a form of capital investment. Nevertheless, it is difficult to predict the
SGA expenditure level when merger and acquisition activities occur. By merging or
acquiring other business units, firms are expected to incur higher SGA costs. Managers,
however, may want to consolidate their business operations by removing some
redundancies in human resources or office space, thereby reducing SGA expenditure.
Consequently, the net effect of merger and acquisition activity on SGA expenditure is
unclear. The likelihood for managers to undertake more risky R&D projects when
merger and acquisition activity takes place during the year is dependent upon the risk-
taking behaviour of managers and funding availability. Judging from this analysis, the
direction of the association between merger/acquisition activities and R&D expenditure
is yet to be ascertained.
l) Restructuring dummy
Restructuring dummy (RESTRUCT) is equals to “one” if there is any restructuring in
year t and “zero” otherwise. As in the case of merger dummy, it is predicted that firm
145
managers will reduce CAPEX. The tendency for firms undergoing restructuring is high
in taking up more risky R&D investment and incur more SGA costs, for example,
expenditure in human resources and marketing. The restructuring dummy is predicted to
be positively associated with R&D and SGA costs separately.
m) Loss dummy
Loss dummy (LOSS) equals to “one” if firm is in a loss situation in the current year and
“zero” otherwise (Gul et al., 2010). This is to isolate the effect of loss-making
companies as they may make different sets of management decisions when compared to
profitable firms. R&D expenditure is generally a strategy of high-risk high-return that
provides future gains to shareholders. Firms investing in R&D projects for long-term
growth may not have sales in the early years of development (Kor, 2006), resulting in
them incurring loss. Furthermore, firms that emphasize strategic control encourage long-
term risk taking and evaluate long-term performance by rewarding innovation (Shadab,
2008). Their managers are more likely to support R&D activities even though the firms
are incurring loss, especially if they are well represented in the board of directors
(Baysinger et al., 1991). Consequently, loss dummy is expected to be positively
associated with R&D expenditure.
In a study covering 40 countries, earnings are found positively associated with CAPEX
regardless of the form of legal systems and the extent of financial development (Inci et
146
al., 2009). In view of the scarcity of internally generated cash flow, loss dummy is
predicted to be negatively related to CAPEX.
Uncertainty arises in predicting the relationship between loss dummy and SGA costs.
Loss-making firms may adopt cost-cutting strategy, hence reducing SGA expenditure. In
view of the value-creation ability of SGA expenditure, loss-making firms may in turn
invest in training and marketing costs, thereby boosting SGA costs level. The predicted
sign for the relationship between loss dummy and SGA costs is yet to be ascertained due
to the contrasting views.
n) Leverage
Leverage is measured as total debts divided by total assets. An inverse relationship is
predicted between leverage and R&D investment as managers are discouraged from
investing in R&D initiatives so as to increase current cash flows to service their debts
(Barker & Mueller, 2002). The same justification can be extended to CAPEX and SGA
expenditure, thus a negative association between leverage and corporate expenditure is
expected.
o) Other corporate expenditure
Due to resource constraints, firms that invest more in tangible assets (CAPEX) tend to
spend less on intangibles (R&D expenditure and SGA costs) (Banker et al., 2011b). A
147
negative association is predicted between CAPEX and R&D expenditure as well as
between CAPEX and SGA costs.
p) Diversification
Diversification is determined by using an entropy27
measure of a firm‟s sales share in
different business sectors (DVS_BIZ) or geographic areas (DVS_GEO) (Palepu, 1985;
Wiersema & Bowen, 2008). The extent of diversification is negatively related with
R&D expenditure in diversified firms as suggested by Baysinger and Hoskisson (1989).
This is because highly diversified firms apply strict financial control (Gupta, 1987) and
their managers are more likely to avoid risky R&D projects to meet short-term financial
performance goals. A negative association between diversification and R&D
expenditure is therefore predicted.
Banker et al. (2011b) show that firms operating in a competitive industry tend to invest
more in SGA expenditure. High diversification level signifies more intense competition,
hence diversification is predicted to be positively connected to SGA costs. Similarly,
more intense competition could imply higher needs for capital expenditure, thus a
positive association between diversification and CAPEX is expected.
27 Entropy measure of diversification = ∑
, being the share of the ith segment in the total
sales of the firm.
148
q) Year and Industry dummies
Year dummies are included in the regression due to divergent business environments
and market conditions for each year. Industry membership determines the level of firms‟
R&D investments due to varying extent of scientific knowledge in the field firms
operate (Baysinger & Hoskisson, 1989). Similarly, different industries have different
needs for CAPEX and SGA costs and it is therefore important to control for industry
membership in the regression. Industry dummies are measured by one-digit SIC code in
this study.
r) Free cash flow
Free cash flow (FCF) is an important determinant for CAPEX, thus, it is included
specifically to examine the relationship between stock price informativeness and
CAPEX. Following Gul and Tsui (1998) and Gul (2001), FCF is measured by operating
income before depreciation minus interest expense, taxes, preferred dividend and
ordinary dividend, and scaled by total assets. The free cash flow hypothesis suggests
that managers prefer to over-invest firms‟ free cash flow wastefully on capital projects
for empire building as opposed to paying it out as dividends and debt-financed share
repurchase (Jensen, 1986). Strong and Meyer (1990) and Devereux and Schiantarelli
(1990) found a significant positive relationship between residual cash flows and
discretionary capital investment. Therefore, if free cash flow hypothesis prevails, a
positive relationship is predicted between free cash flow and CAPEX.
149
s) Employee intensity
Employee intensity (EMP) influences SGA costs because firms with a higher number of
employees are more likely to invest in human capital costs such as payroll and training
costs (Banker et al., 2011b; Chen et al., 2012b). It is frequently measured as the number
of employees scaled by sales. This variable is included in the regression model to
examine the association between stock price informativeness and SGA costs. However,
due to the fluctuating nature of sales revenue, EMP is calculated as the number of
employees divided by total assets in this study. A positive association is expected
between employee intensity and SGA costs.
4.5.4.2 Corporate Governance Control Variables
Corporate governance factors that are included as control variables are audit quality,
earnings management and use of a Governance index. An additional variable that is used
in the robustness test is the variable percentage of independent directors.
a) Audit quality
Auditing is found to ease information asymmetry between insiders and investors by
improving the quality of information presented by financial statements (Dopuch &
Simunic, 1982). It improves transparency and is essential to control the effect of audit
quality as a proxy for corporate governance. Gul et al. (2010) suggest that appointment
150
of high-quality auditors (proxied by “Big-4 auditors”) in the Chinese market helps to
facilitate a better flow of firm-specific information to the market. However, “Big-4
auditors” is not a suitable proxy for audit quality in the US context as nearly all listed
firms in developed markets such as the NYSE engage Big-4 auditors. In Gul, Fung and
Jaggi (2009), an industry-specialist auditor has a higher likelihood to detect irregularities
and fraud. These auditors are shown to provide audit services of higher quality even if
there is deficiency of client-specific knowledge due to short audit tenure. Therefore, in
this study, audit quality (AUDIT) is represented by an industry-specialist auditor who
possesses the highest share of clients‟ total assets in the two-digit SIC industry code.
Audit quality is predicted to be positively linked to corporate expenditure.
b) Earnings management
Following Jones (1991) model, earnings management (EM) is measured by the value of
firm-specific residuals from an industry regression of total accruals on the reciprocals of
total assets, revenue growth and fixed assets. Agency theory suggests that managers are
risk averse and emphasize on meeting short-term performance goals, hence may manage
earnings and accruals for personal gain (Jensen & Meckling, 1976). Therefore, EM may
lead to lower R&D expenditure and SGA costs to show higher earnings in the current
year. Moreover, self-interested managers are reluctant to invest in both R&D and capital
projects as these investments do not yield short-term returns. Thus, EM is predicted to
be negatively linked to corporate expenditure.
151
c) Governance index
A Governance index, GINDEX was developed by Gompers, Ishii and Metrick (2003)
and it measures shareholder rights in each firm from 24 distinct corporate governance
provisions. It covers five specific areas, namely, tactics for delaying hostile bidders,
voting rights, protection of directors and officers, other takeover defences as well as
state laws. The GINDEX assigned to firms range from five (the strongest shareholder
rights) to 14 (the weakest shareholder rights), indicating that corporate governance level
deteriorates as GINDEX increases. Therefore, it is predicted that GINDEX is negatively
associated with corporate expenditure as better corporate governance fosters greater
investment in R&D projects, CAPEX and SGA costs.
d) Percentage of independent directors
Percentage of independent directors (INDDIRPCT) is measured by the independent non-
executive directors as a percentage of all directors on board. It is an alternative corporate
governance variable used in this study as a robustness check. Independent board of
directors play a fundamental corporate governance role in disciplining managerial
actions. Their main priority is to advocate shareholders‟ wealth (Jensen & Meckling,
1976). Baysinger and Hoskisson (1990) suggest firms with outsider-dominated board of
directors tend to incur lower R&D expenditure if they emphasize financial controls.
Thus, a negative relationship is predicted between independent director‟s percentage and
R&D expenditure.
152
On the other hand, the expected association between percentage of independent directors
and CAPEX is dependent upon whether market value maximisation hypothesis or size
maximisation hypothesis prevails in firms (McConnell & Muscarella, 1985). If firm
managers invest in CAPEX to maximise firm value for the benefit of the shareholders,
then the CAPEX decisions will be supported by the independent directors. Hence, a
positive association between independent directors and CAPEX is expected. In
accordance with size maximisation hypothesis, firm managers could, however, over-
invest for empire building (Jensen, 1986). The independent directors, in view of their
assigned role to uphold investors‟ wealth, would not favour the CAPEX decisions by
management. A negative relationship between independent directors and CAPEX is then
expected. As such, the net effect of the relationship between independent directors and
CAPEX cannot be predicted.
A positive association is predicted between percentage of independent directors and
SGA costs in light of the ability of SGA expenditure in creating future value.
4.6 Model Specification
This section delineates the model specification for the main model in examining the
hypotheses developed as well as for the robustness tests conducted.
153
4.6.1 Main Model
The first hypothesis (H1) of the current study examines whether current year‟s stock
price informativeness motivates firms to change corporate expenditure in the subsequent
year. This can be empirically tested using a regression indicated in Equation 4.6.
∑ ∑ ∑
(4.6)
where:
represents corporate expenditure for firm i of the subsequent year, t+1. Three
proxies of corporate expenditures are employed in this study, namely, R&D expenditure,
CAPEX and SGA costs in this study. The coefficient is the intercept while
coefficient is the coefficient of interest. The variable represents idiosyncratic
volatility for firm i of the current year, t and denotes a set of control variables.
Year and Industry dummies are included in the model while represents unspecified
random factors.
This study adopts a lead-lag approach as recent work in finance and strategic
management show that the Efficient Market Hypothesis (Fama, 1970) is not always
reliable as the market takes time to incorporate public information (Eberhart et al., 2004).
Furthermore, the lead-lag structure can reduce reverse causality concern, as it specifies
154
the impact‟s direction from stock price informativeness to corporate expenditure, and
not in a reverse situation.
Hypothesis 1 (H1) of this study posits that the stock price informativeness of the current
year is negatively associated with the subsequent year‟s corporate expenditure.
Therefore, the sign for the coefficient for idiosyncratic volatility, represented by , is
predicted to be negative.
This study also examines whether the relationship between stock price informativeness
and corporate expenditure is dependent on information asymmetry. Three proxies of
information asymmetry, namely, firm size, analyst following and bid-ask spreads are
employed separately. Two sub-samples are firstly segregated based on the median value
of measurement for firm size (natural logarithm of total assets), analyst following
(number of analysts that follow a firm) and bid-ask spreads (value of bid-ask spreads)
respectively. The regression model presented in Equation 4.6 is then applied. The
significance levels of coefficient for the two sub-samples of each of the proxy of
information asymmetry are compared to determine which sub-sample reflects a stronger
relationship between stock price informativeness and corporate expenditure.
For example, in the case of firm size proxy, the full sample is split to two sub-samples
based on the value of firm‟s natural logarithm of total assets. The sub-sample with value
of the natural logarithm of total assets above the median value of the full sample is
155
considered as large firms, the others are small firms. As Hypothesis 2a conjectures that
the relationship between a current year‟s stock price informativeness and the subsequent
year‟s corporate expenditure is likely to be stronger for small firms, the significance
level of coefficient for small firms is predicted to be greater than the significance
level of coefficient for large firms. A similar interpretation applies to the other two
proxies of information asymmetry by looking at the significance level of coefficient
of their respective sub-samples. Hypothesis 2b conjectures that the relationship between
a current year‟s stock price informativeness and the subsequent year‟s corporate
expenditure is likely to be stronger for firms with low analyst following. Therefore, the
significance level of coefficient for firms with low analyst following is predicted to
be greater than that of firms with high analyst following. Hypothesis 2c predicts that the
relationship between a current year‟s stock price informativeness and the subsequent
year‟s corporate expenditure is likely to be stronger for firms with high bid-ask spreads.
Hence, the significance level of for firms with high bid-ask spreads is expected to be
greater than that of firms with low bid-ask spreads.
4.6.2 Robustness Tests
Endogeneity problems are prevalent in empirical research in the areas of accounting and
finance. In order to tackle the potential endogeneity issue, a lead-lag approach is
adopted in this study while a change model as well as a two-stage least squares (2SLS)
156
regression are conducted. The model specifications of the change model and 2SLS
regression are outlined in items 4.6.2.1 and 4.6.2.2 respectively.
4.6.2.1 Change Model
In this study, „change model‟ examines the changes to firms‟ corporate expenditure
following changes in stock price informativeness. Consistent with the main model
presented in Equation 4.6 outlined in item 4.6.1, the change model follows a lead-lag
structure. It deals with the problem of reverse causality as the direction of impact is
specified from current year‟s stock price informativeness to subsequent year‟s corporate
expenditure.
The change model can be empirically tested using the regression indicated in Equation
4.7.
∑ ∑ ∑
(4.7)
where:
is changes in corporate expenditure for firm i from year t to year t+1. The
coefficient is the intercept while the coefficient is the coefficient of interest.
is changes in idiosyncratic volatility from year t-1 to year t and is
157
changes in a set of control variables (excluded dummy variables) from year t-1 to year t.
Year and Industry dummies are included in the model while is unspecific random
factors.
The coefficient captures the difference in corporate expenditure one year after the
changes in idiosyncratic volatility and the sign of indicates whether corporate
expenditure increases or decreases following the changes in idiosyncratic volatility. The
sign of the coefficient is predicted to be negative as Hypothesis 1 of this study
conjectures that a current year‟s stock price informativeness is negatively associated
with the subsequent year‟s corporate expenditure.
The change model is able to display how fast managers react to changes in idiosyncratic
volatility when making their corporate expenditure decisions as idiosyncratic volatility
strengthens or weakens. As such, the full sample is separated into two sub-samples of
observations with increasing and decreasing idiosyncratic volatilities. Analyses are then
performed by applying the regression model presented in Equation 4.7.
4.6.2.2 Two-Stage Least Squares Regression
It is possible that corporate expenditure and stock price informativeness are
endogenously determined as firms with high level of spending in corporate expenditure
could have greater stock price informativeness. The instrument variable estimation
158
method is applied using a 2SLS regression to mitigate the econometrical problems
arising when the outcome variable (corporate expenditure) and the regressor (stock price
informativeness) are endogenous (Larcker & Rusticus, 2010). Endogeneity arises when
the independent variable (idiosyncratic volatility) is correlated with the residual term
(error term) and leads to inconsistent regression estimates (Lev & Sougiannis, 1996). An
appropriate instrument variable is chosen as a substitute for the independent variable
(idiosyncratic volatility) of regression indicated in Equation 4.6 as shown in item 4.6.1.
It would be a variable which is correlated with the original explanatory variable
(idiosyncratic volatility) but unrelated with the error term in the regression (Lev &
Sougiannis, 1996; Wooldridge, 2009, pp. 508-509).
It is often difficult to find a suitable instrument variable that is exogenous. Larcker and
Rusticus (2010) suggest that several instrument variables frequently used in the
accounting literature are not suitable, for instance, industry averages, ranked
endogenous regressors, or lagged endogenous regressors. Hamermesh (2000) opines
that a superior instrument should be beyond the control of the decision-makers and is
able to portray the behaviour of the population.
This study follows the Ferreira and Laux (2007) model by selecting all variables used as
instrument variables. This is because their study concludes that idiosyncratic volatility is
significantly correlated with the Gompers et al. (2003) governance index. The control
variables used in their model are: returns on equity, variance of return on equity,
159
leverage, market-to-book ratio, market capitalization, a dividend dummy, firm age and a
diversification dummy.
In the stage one regression, a predicted value of idiosyncratic volatility ( ) is
obtained by following the Ferreira and Laux (2007) model, that is, by regressing firms‟
idiosyncratic volatility, on the Gompers Governance index (GINDEX) and several
other explanatory variables for each firm-year as presented in Equation 4.8.
∑ ∑ (4.8)
where:
represents idiosyncratic volatility of the current year, t and GINDEX is the
governance index developed by Gompers et al. (2003) in the previous year, t-1. The
control variables included are ROE (return on equity), VROE (variance of return on
equity), LEV (leverage), MB (market-to-book ratio), (market capitalization), DIV
(dividend dummy), AGE (firm age) and DIVER (diversification dummy). Year and
Industry dummies are included in the model while is the error term. The symbol i and
t denote firms and yearly time index respectively.
160
In the second stage regression, Equation 4.9 is estimated using the fitted value of
idiosyncratic volatility, as a substitute for the actual value of firms‟ actual
value of idiosyncratic volatility, as follows:
∑ ∑ ∑
(4.9)
where:
represents corporate expenditure of firm i for the subsequent year, t+1 and
three proxies of corporate expenditures are employed in this study: R&D expenditure,
CAPEX and SGA costs. The coefficient is the intercept while coefficient is the
coeffient of interest. Variable is the fitted value of idiosyncratic volatility
and denotes a set of control variables. Year and Industry dummies are
included in the model and represents unspecified random factors.
It is crucial to impose exclusion restrictions on the model, which means that the
equation of the first and the second stage models generally contain different exogenous
variables (Wooldridge, 2009, p. 521). As such, several variables appearing in the first
stage model in Equation 4.8 such as firm age (AGE), leverage (LEV) and dividend
dummy (DIV) are excluded in the second stage regression in Equation 4.9. The variable
StdROE which is measured by standard deviation of ROE is also not applied as a
control variable in Equation 4.9 as it is closely related to another measure of volatility of
return on earnings, VROE represented by variance of ROE. Moreover, the variable
161
market-to-book ratio (MB) used in stage one model in Equation 4.8 is substituted by the
variable earning-to-price ratio (EP) in Equation 4.9. The variable GINDEX is not
suitable to serve as a corporate governance variable in Equation 4.9 as it is the
independent variable used in Equation 4.8 (stage one regression model), hence GINDEX
is replaced by variable percentage of independent directors (INDDIRPCT) in the stage
two regression.
Table 4.1 presents the definitions of variables used in this study and the sources of the
data. The variables are displayed across five panels: panels A to E define the dependent
variable (corporate expenditure variables), independent variable (stock price
informativeness variable), variables representing information asymmetry, control
variables and variables used for the two-stage least squares regression respectively.
162
Table 4.1 Definition of Variables
Variable Represented by Definition Source
of data
Panel A Dependent Variable - Corporate Expenditure Variables
Research and development
expenditure (value)
R&D (value) Research and development expenditure measured in US
dollar value
Compustat
Research and development
expenditure
R&D Natural logarithm of annual research and development
expenditure scaled by total assets
Compustat
Capital expenditure (value) CAPEX (value) Capital expenditure measured in US dollar value
Compustat
Capital expenditure CAPEX Natural logarithm of annual capital expenditure scaled by
total assets
Compustat
Selling, general and
administrative costs (value)
SGA(value) Selling, general and administrative costs measured in US
dollar value
Compustat
Selling, general and
administrative costs
SGA Natural logarithm of annual selling, general and
administrative costs scaled by total assets
Compustat
Panel B Independent Variable – Stock Price Informativeness Variables
Logistic relative idiosyncratic
volatility
ψ Annual logistic transformed relative idiosyncratic volatility
estimated from the market model
CRSP
Relative idiosyncratic volatility 1-R2
Annual relative idiosyncratic volatility given by the ratio of
idiosyncratic variances to total variance
CRSP
163
Table 4.1 Definition of Variables (Continued)
Variable Represented by Definition Source
of data
Panel C Information Asymmetry Variable
Total assets AT Firm‟s total assets at the end of fiscal year in US dollar value
Compustat
Firm size SIZE Natural logarithm of total assets at the end of the fiscal year
Compustat
Analyst following ANALYST
Number of analysts that follow a firm I/B/E/S
Bid-ask spreads BIDASK Annual average bid ask spreads measured as the mean of
difference between closing ask and bid price, scaled by the
average of ask and bid price.
CRSP
Panel D Control Variables
Firm size SIZE Natural logarithm of total assets at the end of the fiscal year Compustat
Analyst following
ANALYST Number of analyst that follow a firm I/B/E/S
Firm age (years) FIRM AGE Number of years that the firm reported assets on Compustat
Compustat
Firm age (log) AGE
Natural logarithm of number of years that the firm reported
assets on Compustat
Compustat
Volatility of return on equity
StdROE
Standard deviation of a firm‟s annual return on equity over
the last three years
Compustat
164
Table 4.1 Definition of Variables (Continued)
Variable Represented by Definition Source
of data
Cash flow volatility StdCF
Standard deviation of cash flow for the past five years
Compustat
Stock return volatility StdRET
Standard deviation of daily stock return over the past one
year
CRSP
Market-to-book ratio MB
Total market value of equity divided by book value of
shareholder funds at the end of fiscal year
Compustat
Return on assets ROA
Earnings before extraordinary items over total assets at the
end of fiscal year
Compustat
Working capital ratio WC
Working capital divided by sales
Compustat
Dividend dummy DIV
Annual dummy variable equals to “1” if firm pays dividend
during the fiscal year and “0” otherwise
Compustat
Merger dummy
MERGER
Dummy variable equals to “1” if there is merger or
acquisition activities during the fiscal year and “0” otherwise
Compustat
Restructuring dummy RESTRUCT
Dummy variable equals to “1” if there is restructuring
activities during the fiscal year and “0” otherwise
Compustat
Loss dummy LOSS
Dummy variable equals to “1” if firm is in loss financial
position during the fiscal year and “0” otherwise
Compustat
Leverage
LEV Total debts divided by total assets Compustat
165
Table 4.1 Definition of Variables (Continued)
Variable Represented by Definition Source
of data
Diversification (business) DVS_BIZ Entropy measure of a firm‟s sales share in different lines of
business
Compustat
Diversification (geography)
DVS_GEO Entropy measure of a firm‟s sales share in different
geographic areas
Compustat
Free cash flow FCF
The operating income before depreciation minus interest
expenses, taxes, preferred dividend and ordinary dividend
and divided by total assets
Compustat
Employee intensity EMP Number of employees divided by total assets Compustat
Audit quality AUDIT Dummy variable equals to “1” if a firm is audited by an
industry-specialist auditor and “0” otherwise. Industry-
specialist auditor possesses the highest share of clients‟ total
assets in the two-digit SIC industry code.
Compustat
Earnings management EM
Firm-specific residuals from an industry regression of total
accruals on the reciprocals of total assets, revenue growth and
fixed assets using the Jones (1991) model
Compustat
Governance index GINDEX Gompers et al. (2003) index based on 24 corporate
governance provisions
Risk Metrics
166
Table 4.1 Definition of Variables (Continued)
Variable Represented by Definition Source
of data
Panel E Two-stage Least Squares Regression Variable
Predicted value for idiosyncratic
volatility Fitted value for idiosyncratic volatility estimated based on the
Ferreira and Laux (2007) model
CRSP &
Compustat
Return on equity ROE Earnings before extraordinary items divided by equity
Compustat
Variance of return on equity VROE Sample variance of annual returns on equity over the last
three years
Compustat
Market capitalization MCAP Market value of firms‟ equity at the end of fiscal year Compustat
Diversification dummy DIVER Internal diversification dummy equals to “1” if there is
internal diversification and “0” otherwise
Compustat
Earnings-to-price ratio EP Earnings per share for firms at the end of the fiscal year,
scaled by the closing market price of firms‟ outstanding
shares nine months prior to its balance sheet date
Compustat &
CRSP
Percentage of independent
directors
INDDIRPCT Independent non-executive director as a percentage of all
directors on board
Execucomp
167
4.7 Sample Selection Procedure
Table 4.2 outlines the sample selection procedure to arrive at the final sample for each
of the corporate expenditure.
Table 4.2 Sample Selection Procedure
Description Firm-year
observations
Number of observations with idiosyncratic volatility
43,257
(-) financial institution (SIC code between 6000 and 6999) and
utilities (SIC code between 4900 and 4999)
(15,872)
Initial Sample
27,385
(-) observations without control variables
(10,765)
Number of observations with idiosyncratic volatility and control
variables (Years 2003-2011)
16,620
After deleting missing value for each of the corporate expenditure:
Final Sample for H1, H2a (firm size) and H2b (analyst following)
Research & development expenditure
Capital expenditure
Selling, general and administrative costs
Sample size
8,513
15,443
14,318
After deleting missing value for bid ask spreads:
Final sample for H2c (bid-ask spreads)
Research & development expenditure
Capital expenditure
Selling, general and administrative costs
8,510
15,439
14,315
168
The annual idiosyncratic volatility is estimated using daily returns of US PLCs from
CRSP database for the years 2003 to 2009. Data prior to 2003 are excluded to minimise
any possible impact arising from the Sarbanes-Oxley Act executed in year 2002. After
excluding firms in the financial institutions (SIC code between 6000 and 6999) and
utilities (SIC code between 4900 and 4999) as these two sectors are regulated in nature,
an initial sample of 27,385 firm-year observations is obtained. This sample is then
reduced to 16,620 firm-year observations after merging with both Compustat (financial
data) and I/B/E/S database (analyst data), followed by removing observations without
control variables. The sample size is further reduced due to missing values for corporate
expenditure and the requirement to use lead value (t+1) of corporate expenditure. The
final sample for the study ranges from 8,513 (R&D expenditure) to 15,443 (CAPEX)
observations. This final sample is used for the examination of H1 and H2a (using firm
size as the proxy of information asymmetry) and H2b (using analyst following as the
proxy of information asymmetry).
A slightly smaller sample is obtained to investigate H2c when bid-ask spreads are used
as the third proxy of information asymmetry. The standard data filtering procedure
identified in the microstructure literature are employed to “clean” the data of trade and
quotes for errors and outliers (Huang & Stoll, 1996; Chung et al., 2010). This filtering
process would remove:
169
i) quotes if either the bid or ask price is negative,
ii) quotes if the bid-ask spread is more than USD4 or negative, and
iii) trades when the price or volume is negative.
4.8 Statistical Analyses
The following sections describe data screening, data analyses and data validation that is
performed in this study. Computer software SAS Version 9.3 is employed in this study
to facilitate data screening and data analysis process.
4.8.1 Data Screening
Data collected from a variety of databases is firstly reviewed for missing values, shape
of the data distribution and determining outliers. This process improves the chances of
data correctness by detecting and rectifying any possible apparent errors (O'Rourke,
Hatcher & Stepanski, 2009, p. 90).
Missing data occurs when the valid value of an observation of either the dependent
variable or any of the independent variables are not available. Observations with
missing information that cannot be subjected to data analysis are excluded in the
multiple regression analyses. Missing data reduces the sample size available from the
population and may result in less accurate estimation in the data analysis, but it does not
170
violate the random sampling assumption if the observation is missing at random
(Wooldridge, 2009, p. 332).
The shape of the data distribution depicts the extent to which the sample distribution
deviates from the normality by observing its skewness and kurtosis. It is preferred to
have data that is normally distributed28
to avoid inaccurate statistical inferences and
biased correlation coefficients (O'Rourke et al., 2009, p. 100). Hence, a distribution that
is not too skewed and without extreme kurtosis is favoured (Field, 2009, p. 138).
Skewness and kurtosis of a distribution can be inferred using a histogram or univariate
analysis. Skewness measures symmetric (or asymmetric) distribution by viewing its
long “tail” on either left or right side of the distribution. A skewness value of zero
indicates a symmetric distribution of the mean, a positive skewness value is related to a
right-skewed distribution while a negative skewed value shows a left-skewed
distribution (Cody, 2011, p. 28). Kurtosis, on the other hand, ascertains whether a
distribution is either taller and leaner or flatter than a normal distribution. A kurtosis
value of zero indicates a normal distribution, a positive value for kurtosis shows that the
distribution is too peaked (tall and lean) while a negative value depicts a flat distribution
(O'Rourke et al., 2009, p. 94). Univariate analysis such as use of Shapiro-Wilk statistics
can be used to examine the normality of the distribution (O'Rourke et al., 2009, p. 104).
28 A normal distribution is symmetrical and bell shaped distribution of values (O'Rourke et al., 2009, p.
99).
171
If a data is not normally distributed, a logarithm transformation is usually performed to
produce a distribution that is closer to normal (Wooldridge, 2009, p. 119).
A departure from normality can be caused by outliers too (O'Rourke et al., 2009, p. 102).
Outliers are extreme values that diverge significantly from the other values in the
distribution. The problem of outliers can be due to error in data entry or when one or
several members of the population behave differently from the rest of a small population.
The marked difference of outliers from the other observations may substantially affect
the outcome of the regression especially in small datasets (Wooldridge, 2009, p. 325).
Outliers can be identified by observing the extreme values of the variables analysed that
fall at the outer range (the highest or the lowest) of the distribution from a univariate
analysis. The outliers can also be viewed graphically by using a probability plot or box
plot (Cody, 2011, p. 37). Wooldridge (2009, p. 325) opines that whether to keep or to
drop outliers in a regression analysis is a difficult decision to make. The author proceeds
to suggest that certain function forms, for example, logarithm transformation of most
economic variables, significantly reduce the range of data and therefore are less
sensitive to outliers. Following empirical research in accounting and finance, all
variables are winsorised at the bottom and top one per cent levels in this study to
minimise outlier effects and spurious inferences.
172
4.8.2 Data Analyses
The screened data is then analysed using univariate and multivariate tests. Additional
tests are conducted to verify the robustness of the main model specified in item 4.6.1.
4.8.2.1 Univariate Tests
Descriptive statistics and Pearson correlations are used to obtain a basic understanding
of the data collected. Graphs are presented to illustrate the trends or associations among
the key variables in Figures 5.1 to 5.7 displayed in Chapter 5.
4.8.2.2 Multivariate Tests
This study employs multiple linear regressions using ordinary least squares (OLS)
estimation method to examine the hypotheses developed. OLS attempts to fit a
regression line through the observations in the sample to obtain the intercept and slope
estimates where the sum of squared residuals is the lowest. A t-test is used to determine
whether an alternative hypothesis, H1 can be supported. A p-value for an observed value
of t statistics represents the smallest significance level at which the alternative
hypothesis would be supported. The p-value of the t-test will be compared to the pre-
determined α-value and if the p-value is smaller than the α-value chosen, the alternative
hypothesis is supported (Wooldridge, 2009, pp. 120-124).
173
This study has fulfilled six assumptions required to obtain unbiased and efficient OLS
estimators. According to Wooldridge (2009, pp. 84-102), the six assumptions are
outlined as follows:
a) Linearity: the relationship between independent variables and dependent variable
is assumed to be linear.
b) Random sampling: observations are randomly drawn from the population and are
representative of the population.
c) No perfect collinearity: No perfect correlation should exist among the independent
variables in a model.29
Variables with a correlation value of 0.80 and above are
considered to be highly correlated while variance inflation factor (VIF) above cut-
off threshold of 10 signals multicollinearity problem (Field, 2009, p. 224).30
d) Zero conditional mean: the error term is unrelated to the independent variable(s).
e) Homoskedasticity: the variances of the error term are constant for any value of the
independent variables.
f) Normality: The error terms are assumed to be normally distributed.
It is important to measure the goodness-of-fit of a regression model. The coefficient of
determination, R2 of the regression is commonly used to assess how well the
29 Wooldridge (2009, pp. 98-99), however, comments that high correlations among control variables do
not affect the relationship between the main variables.
30 Wooldridge (2009, p. 99) warns that these cut-off value are arbitrarily determined and must be applied
with care.
174
independent variable (idiosyncratic volatility) explains the dependent variable (corporate
expenditure) in an OLS regression. The value of the R2
of a regression model sums up
how well the OLS regression fits the data, i.e., the percentage of the sample variance in
the dependent variable that can be explained by the regression model. The range of R2
value is between zero and one where higher value of R2 indicates a better fit of the OLS
regression model to the data (Wooldridge, 2009). The second measure of goodness-of-
fit generally used is the F statistic which determines the overall significance of a
regression model. When F statistic is large and significant (as the p-value is very small),
the null hypothesis is rejected and the regression model is concluded to be statistically
significant (O'Rourke et al., 2009, p. 232).
In this study, H1 is stated in an alternative form and it hypothesizes that the stock price
informativeness of a current year is negatively associated with corporate expenditure of
the subsequent year, while holding other variables constant. If the p-value of the t-test
generated from the computer statistical software is smaller than the α-value chosen, say
5%, then H1 is supported. This indicates that an association between a current year‟s
stock price informativeness and the subsequent year‟s corporate expenditure is
significant at 95% confidence level.
Hypothesis 2a conjectures that the relationship between a current year‟s stock price
informativeness and the subsequent year‟s corporate expenditure is likely to be stronger
for small firms. Multiple linear regressions are carried out on each group of the firm size
175
to examine whether the relationship examined is stronger for large or small firms.
Similar procedures are applied to examine H2b for analyst following and H2c for bid-
ask spreads.
Other challenges faced by accounting and finance researchers are the cross-sectional and
time series correlation prevalent in panel data sets (data sets that contain observations of
multiple firms in multiple years). Time series dependence occurs when the residuals of a
firm may be correlated across years for that firm resulting in unobserved firm effect, for
example, the observation of firm A in year t can be correlated with that of this firm
(Firm A) in year t+1. Cross-sectional correlation indicates the residuals of a year may be
correlated across varying firms giving rise to time effect, for example, the observation of
firm A in year t can be correlated with that of firm B in the same year (Petersen, 2009;
Gow, Ormazabal & Taylor, 2010). In econometrics and finance research, common
methods used to address these concerns are Fama and MacBeth (1973) procedure, the
Newey and West (1987) procedure and one way cluster-robust standard errors (Petersen,
2009; Gow et al., 2010). Fama-Macbeth (1973) procedure deals with the issue of
possible correlation in the cross-sectional error structure. It estimates cross-sectional
regressions for each period and inferences are made based on the mean and standard
deviation of the estimated coefficients. The Newey and West (1987) procedure is used
to mitigate both heteroskedasticity and time series autocorrelation. The one-way cluster-
robust standard errors, also known as the Huber-White standard errors or Rogers
standard errors, was proposed by White (1984) to adjust White‟s (1980) standard
176
errors31
to account for possible correlation within a cluster. The one-way cluster-robust
standard errors method performs clustering of standard errors along a cross-sectional
dimension, for example, in relation to firms, industries or countries. Alternatively, the
one-way cluster-robust standard errors method can be performed along a time-series
dimension such as year. However, Petersen (2009) opines that these three methods,
being the Fama and MacBeth (1973) procedure, the Newey and West (1987) procedure
and the one way cluster-robust standard errors consider either cross-sectional or time-
series correlation separately, but not simultaneously. Thompson (2011) criticises that the
standard errors produced by the one-way clustering method do not adjust correctly for
simultaneous correlation across both firms and time span.
The econometrics literature demonstrates that the two-way cluster-robust standard errors
method is robust to both time-series and cross-sectional correlation (Petersen, 2009;
Cameron, Gelbach & Miller, 2011; Thompson, 2011). Petersen (2009) found the
coefficient estimated using the two-way cluster-robust standard error method account
correctly for the presence of both time and firm effect in panel data sets, thus generating
unbiased standard errors when there are sufficient number of clusters in each dimension.
Unbiased standard error is crucial for research using standard hypothesis testing to
31 The White‟s (1980) variance correction method are used to mitigate heteroskedasticity problem in the regressions.
Heteroskedasticity occurs when the variance of the error term is not constant.
177
arrive at correct statistical inferences as any moderate changes in standard errors can
have a significant impact on statistical inferences (Cameron et al., 2011).
According to Cameron et al. (2011) and Thompson (2011), the covariance estimator is
equal to the estimator that clusters by firm plus the estimator that clusters by time, and
minus the heteroskedasticity-robust OLS variance matrix (White, 1980). The standard
errors can be calculated using any statistical package with a clustering command, such
as SAS or Stata.
Gow et al. (2010) evaluate the appropriateness of other approaches applied in empirical
accounting research such as the Fama-Macbeth-i, Z2 statistics and Newey-West
corrected Fama-Macbeth statistics. Fama-Macbeth-i is a modification of the Fama and
MacBeth (1973) procedure which involves estimation of time-series regressions for each
firm and makes inferences based on the mean and standard deviation of the coefficients.
Z2 statistics is firstly used in Barth (1994) to adjust for cross-sectional and serial
correlation. The Newey-West corrected Fama-Macbeth (FM-NW) procedure is used to
modify the Fama and MacBeth (1973) approach. It applies the Newey and West (1987)
procedure to the time-series of coefficient estimates and adjusts the standard errors to
mitigate time series autocorrelation. Gow et al. (2010) find that these approaches
examined do not correct for both cross-sectional and time-series correlation, leading to
misspecified test statistics and materially varying research inferences. They conclude
178
that the two-way cluster-robust standard errors method is required to generate well-
founded research conclusions particularly in the area of corporate finance.
In this study, the White‟s (1980) variance correction method is used to avoid the
hetereoskedasticity inherent in the regressions. Statistical tests are also carried out based
on the Petersen (2009) clustering method to control for clustered standard errors by both
firm and year, hence cross-sectional and time-series correlation are accounted for. These
tests are conducted using SAS, a standard econometric software package. Two other
procedures are also conducted as additional test to verify the robustness of statistical
tests conducted, namely:
a) The Fama and MacBeth (1973) procedure to control for the possible correlation
in the cross-sectional error structure; and
b) The Newey-West corrected Fama-Macbeth (FM-NW) procedure to mitigate time
series autocorrelation.
4.8.2.3 Additional Tests
The following additional tests are conducted to examine the robustness of the model
specified in this study:
a) using different measures of idiosyncratic volatility generated using methods
outlined in item 4.5.2, namely,
179
(i) different market index,
(ii) Fama & French three factor model (Fama & French, 1993, 1995, 1996), and
(iii) Brockman and Yan‟s (2009) model,
b) exclude 2008 year data to examine whether the association between current year‟s
stock price informativeness and subsequent year‟s corporate expenditure is driven
by the global financial crisis
4.9 Chapter Summary
This chapter (Chapter 4) outlines the research methodology used in this study. A
quantitative research method is adopted within the positivism paradigm. This chapter
describes the principal population data as well as sources of the secondary data. The
measurement of variables are then elaborated, followed by a detailed overview of the
model specification and sample selection procedure undertaken to examine four
hypotheses developed in Chapter 3. Finally, the statistical analysis conducted in this
study is delineated.
The next chapter, Chapter 5 presents the findings followed by a discussion of results of
the current study.
180
CHAPTER 5
RESEARCH FINDINGS AND DISCUSSION
5.1 Introduction
The previous chapter (Chapter 4) presents the research methodology employed in this
study. The research paradigm adopted in this study is firstly elaborated, followed by a
description of the principal population data, sources of secondary data and variables
measurement. The chapter then presents an overview of the model specification, sample
selection procedure as well as the statistical methodology conducted for this study.
This chapter (Chapter 5) provides the research findings followed by a discussion of the
results. After the introductory note, Section 5.2 describes the univariate results while
Section 5.3 presents and discusses the multivariate results with regards to the association
between stock price informativeness and corporate expenditure. Section 5.4 exhibits
how this association observed between stock price informativeness and corporate
expenditure is dependent on information asymmetry, proxied by firm size, analyst
following and bid-ask spreads. Section 5.5 summarises the results of additional tests
conducted in verifying the robustness of the model applied. A summary of this chapter
is presented in Section 5.6.
181
5.2 Univariate Results
Univariate results are reported in the following manner:
a) use of descriptive statistics of the full sample to explain corporate expenditure
which are represented by R&D, CAPEX and SGA,
b) explain the Pearson correlations between variables used in the study, and
c) provide descriptive statistics of each corporate expenditure by segregating the
full sample of the study into two sub-groups using three proxies of information
asymmetry, namely, firm size, analyst following and bid-ask spreads.
5.2.1 Descriptive Statistics of Corporate Expenditure
Table 5.1 presents the descriptive statistics of each of the corporate expenditure in US
dollars (USD) for the sample of this study for the years 2004 to 2010. The descriptive
statistics are presented for each individual year as well as for the full sample.
182
Table 5.1 US Corporate Expenditure of Years 2004 - 2010 (in USD million)
Year N Mean Lower
Quartile
Median Upper
Quartile
Standard
Deviation
Research & Development Expenditure
2004 1,238 126.42 7.26 21.08 61.00 545.93
2005 1,249 132.51 7.30 21.84 61.27 548.43
2006 1,207 161.29 8.09 24.80 68.30 639.91
2007 1,210 166.39 8.53 26.01 75.70 637.13
2008 1,193 178.68 8.74 26.68 81.82 681.18
2009 1,211 156.98 8.04 24.69 71.36 625.77
2010 1,205 177.21 8.51 26.08 75.94 727.52
Full Sample 8,513
156.80
8.00
24.40
70.91
631.63
Capital Expenditure
2004 2,090 164.83 3.92 17.06 74.14 979.71
2005 2,139 191.41 4.08 18.92 85.94 1,042.10
2006 2,097 235.94 4.78 22.23 102.50 1,307.83
2007 2,098 257.09 4.98 23.11 113.46 1,159.64
2008 2,151 278.29 5.23 24.34 123.00 1,259.52
2009 2,171 200.95 3.88 17.68 85.86 1,021.77
2010 2,220 232.18 5.02 21.57 97.80 1,159.34
Full Sample 14,966
223.06
4.56
20.68
95.37
1,138.89
183
Table 5.1 US Corporate Expenditure of Years 2004-2010 (in USD million) (Continued)
Year N Mean Lower
Quartile
Median Upper
Quartile
Standard
Deviation
Selling, General & Administrative Costs
2004 1,944 566.34 41.03 103.44 306.18 2,183.05
2005 1,989 584.60 41.34 109.95 334.38 2,267.47
2006 1,952 657.58 45.94 124.78 362.61 2,524.58
2007 1,949 713.74 47.39 130.15 401.90 2,732.19
2008 2,004 740.49 48.28 135.92 423.21 2,847.01
2009 2,019 693.66 45.01 121.20 381.20 2,819.91
2010 2,067 735.33 45.85 128.20 417.00 2,934.21
Full Sample 13,924
670.99
44.89
120.51
370.88
2,633.85
184
Figure 5.1 depicts the movement of each of the three corporate expenditures during the
years 2004 to 2010.
Figure 5.1 Trend Analysis of US Corporate Expenditure from 2004-2010
The graphs presented in Figure 5.1 reveal that SGA costs have the highest monetary
value among the three corporate expenditures. R&D costs on average forms about 25
per cent of SGA costs. Trend analyses portray that the mean of R&D expenditure
increases from year 2004 to 2010 except in 2009 where firms experienced an average
decline of 12 per cent or USD 21.70 million. A similar trend is also found in CAPEX
and SGA costs. A general reduction of 28 per cent (USD 77.34 million) and six per cent
(USD 46.83 million) are registered in CAPEX and SGA costs respectively in year 2009.
-
100
200
300
400
500
600
700
800
2004 2005 2006 2007 2008 2009 2010
USD
mill
ion
Year
SGA Costs
CAPEX
R&D Costs
185
This reflects the global financial crisis that took place in the year 2008 has influenced
managerial decisions in curbing firms‟ corporate expenditure budgets and consequently
led to a lower actual investment in R&D costs, CAPEX and SGA costs of the
subsequent year. An additional test is carried out in item 5.5.2 by excluding data for the
year 2008 from the sample and the results show that the empirical evidence found in this
study remains robust and is not driven by the impact of the 2008 global financial crisis.
Tables 5.2, 5.3 and 5.4 provide descriptive statistics for all the variables used in the
analysis of US corporate expenditure in relation to:
(a) research and development expenditure;
(b) capital expenditure; and
(c) selling, general and administrative costs respectively.
The sample for each of the corporate expenditure is divided into two sub-samples, that is,
when idiosyncratic volatility is increasing and when it is decreasing. The descriptive
statistics of these two sub-groups are presented simultaneously with the full sample in
Tables 5.2 to 5.4.
186
Table 5.2 Descriptive Statistics – R&D Expenditure
Full Sample
Variable N Mean Lower
Quartile
Median Upper
Quartile
Std Dev
R&D t+1(Value) 8,513 $156.80 $8.00 $24.40 $70.91 $631.63
R&D t+1 8,513 -2.943 -3.780 -2.749 -2.017 1.383
Ψ 8,513 1.909 0.856 1.527 2.569 1.667
1-R2 8,513 0.797 0.702 0.822 0.929 0.158
AT (Value) 8,513 $3,649.84 $114.51 $379.72 $1,587.09 $15,481.43
SIZE 8,513 6.121 4.741 5.939 7.370 1.909
ANALYST 8,513 7.154 2.000 5.000 10.000 6.585
BIDASK 8,510 0.006 0.001 0.002 0.006 0.010
FIRM AGE (Years) 8,513 20.457 10.000 15.000 25.000 14.956
AGE 8,513 2.783 2.303 2.708 3.219 0.677
StdROE 8,513 0.769 0.030 0.074 0.215 9.994
StdCF 8,513 90.215 5.127 13.993 43.762 388.443
StdRET 8,513 0.034 0.022 0.031 0.042 0.019
MB 8,513 3.988 1.574 2.519 4.125 68.048
ROA 8,513 -0.061 -0.081 0.032 0.078 0.352
WC 8,513 16.693 0.185 0.378 0.783 675.765
DIV 8,513 0.296 0.000 0.000 1.000 0.456
MERGER 8,513 0.081 0.000 0.000 0.000 0.273
RESTRUCT 8,513 0.408 0.000 0.000 1.000 0.491
LOSS 8,513 0.374 0.000 0.000 1.000 0.484
LEV 8,513 0.442 0.231 0.404 0.586 0.314
AUDIT 8,513 0.248 0.000 0.000 0.000 0.432
EM 8,513 -0.006 -0.041 0.007 0.051 0.191
DVS_GEO 7,194 0.750 0.275 0.707 1.132 0.557
DVS_BIZ 6,471 0.380 0.000 0.000 0.687 0.554
GINDEX 3,033 9.136 7.000 9.000 11.000 2.496
Note: All variables are defined in Table 4.1. The variables R&D t+1 (Value) and AT (Value) are stated in
USD million.
187
Table 5.2 Descriptive Statistics – R&D Expenditure (Continued)
Increasing ψ Decreasing ψ
Variable N Mean Median Std Dev N Mean Median Std Dev
R&D t+1 (Value) 3,729 $175.63 $25.40 $683.62 4,784 $142.12 $23.61 $587.58
R&D t+1 3,729 -2.865 -2.670 1.359 4,784 -3.003 -2.808 1.398
Ψ 3,729 2.438 1.902 1.906 4,784 1.496 1.226 1.313
1-R2 3,729 0.846 0.870 0.133 4,784 0.758 0.773 0.164
AT (Value) 3,729 $3,843.45 $387.15 $16,209.85 4,784 $3,498.92 $375.27 $14,888.92
SIZE 3,729 6.123 5.959 1.949 4,784 6.119 5.928 1.878
ANALYST 3,729 7.489 5.000 7.163 4,784 6.894 5.000 6.084
BIDASK 3,728 0.006 0.002 0.009 4,782 0.005 0.002 0.010
FIRM AGE
(Years)
3,729 20.431 15.000 14.825 4,784 20.476 15.000 15.059
AGE 3,729 2.788 2.708 0.665 4,784 2.779 2.708 0.686
StdROE 3,729 0.889 0.079 12.510 4,784 0.675 0.071 7.467
StdCF 3,729 90.139 14.399 401.265 4,784 90.275 13.810 378.189
StdRET 3,729 0.034 0.029 0.023 4,784 0.035 0.032 0.016
MB 3,729 3.382 2.565 20.709 4,784 4.461 2.482 88.915
ROA 3,729 -0.070 0.027 0.380 4,784 -0.054 0.037 0.329
WC 3,729 23.988 0.398 878.253 4,784 11.006 0.361 459.801
DIV 3,729 0.294 0.000 0.455 4,784 0.298 0.000 0.457
MERGER 3,729 0.089 0.000 0.284 4,784 0.075 0.000 0.263
RESTRUCT 3,729 0.421 0.000 0.494 4,784 0.397 0.000 0.489
LOSS 3,729 0.393 0.000 0.488 4,784 0.359 0.000 0.480
LEV 3,729 0.439 0.400 0.330 4,784 0.444 0.406 0.301
AUDIT 3,729 0.256 0.000 0.436 4,784 0.242 0.000 0.428
EM 3,729 -0.006 0.004 0.179 4,784 -0.006 0.008 0.200
DVS_GEO 3,151 0.755 0.708 0.555 4,043 0.746 0.705 0.559
DVS_BIZ 2,838 0.363 0.000 0.556 3,633 0.393 0.000 0.551
GINDEX 1,522 9.064 9.000 2.474 1,511 9.209 9.000 2.516
Note: All variables are defined in Table 4.1. The variables R&D t+1 (Value) and AT (Value) are stated in
USD million.
188
Table 5.3 Descriptive Statistics – Capital Expenditure
Full Sample
Variable N Mean Lower
Quartile
Median Upper
Quartile
Std Dev
CAPEXt+1 (Value) 14,966 $223.06 $4.56 $20.68 $95.37 $1,138.89
CAPEXt+1 14,966 -3.485 -4.113 -3.470 -2.816 1.042
Ψ 14,966 1.855 0.824 1.473 2.491 1.658
1-R2 14,966 0.791 0.695 0.814 0.923 0.158
AT (Value) 14,966 $3,634.68 $171.32 $571.69 $1,988.03 $13,760.20
SIZE 14,966 6.432 5.144 6.349 7.595 1.793
ANALYST 14,966 7.267 2.000 5.000 10.000 6.485
BIDASK 14,962 0.005 0.001 0.002 0.005 0.010
FIRM AGE (Years) 14,966 20.659 10.000 15.000 27.000 14.544
AGE 14,966 2.800 2.303 2.708 3.296 0.675
StdROE 14,966 0.924 0.023 0.056 0.150 52.873
StdCF 14,966 87.982 6.096 17.108 53.319 324.939
StdRET 14,966 0.033 0.021 0.029 0.040 0.017
MB 14,966 4.293 1.453 2.261 3.705 49.083
ROA 14,966 -0.009 -0.016 0.041 0.082 0.218
WC 14,966 9.955 0.093 0.229 0.495 509.067
DIV 14,966 0.365 0.000 0.000 1.000 0.482
MERGER 14,966 0.075 0.000 0.000 0.000 0.263
RESTRUCT 14,966 0.328 0.000 0.000 1.000 0.469
LOSS 14,966 0.291 0.000 0.000 1.000 0.454
LEV 14,966 0.454 0.284 0.454 0.605 0.213
AUDIT 14,966 0.257 0.000 0.000 1.000 0.437
EM 14,966 0.000 -0.036 0.007 0.049 0.141
FCF 14,966 0.039 0.029 0.075 0.117 0.201
DVS_GEO 12,208 0.574 0.000 0.523 0.984 0.560
DVS_BIZ 11,278 0.363 0.000 0.000 0.675 0.517
GINDEX 5,204 9.047 7.000 9.000 11.000 2.498
Note: All variables are defined in Table 4.1. The variables CAPEXt+1 (Value) and AT (Value) are
stated in USD million.
189
Table 5.3 Descriptive Statistics – Capital Expenditure (Continued)
Increasing ψ Decreasing ψ
Variable N Mean Median Std Dev N Mean Median Std Dev
CAPEXt+1 (Value) 6,313 $215.25 $19.81 $1,222.36 8,653 $228.77 $21.36 $1,073.94
CAPEXt+1 6,313 -3.520 -3.486 1.015 8,653 -3.459 -3.456 1.061
Ψ 6,313 2.401 1.861 1.913 8,653 1.457 1.202 1.306
1-R2 6,313 0.844 0.865 0.133 8,653 0.752 0.769 0.164
AT (Value) 6,313 $3,739.13 $568.77 $14,433.99 8,653 $3,558.48 $573.70 $13,247.35
SIZE 6,313 6.417 6.343 1.825 8,653 6.442 6.352 1.770
ANALYST 6,313 7.531 5.000 6.904 8,653 7.074 5.000 6.153
BIDASK 6,311 0.005 0.002 0.009 8,651 0.005 0.002 0.010
FIRM AGE
(Years)
6,313 20.816 15.000 14.558 8,653 20.545 15.000 14.534
AGE 6,313 2.812 2.708 0.666 8,653 2.791 2.708 0.681
StdROE 6,313 1.637 0.058 81.161 8,653 0.404 0.054 5.402
StdCF 6,313 86.257 17.161 333.291 8,653 89.240 17.098 318.720
StdRET 6,313 0.032 0.027 0.019 8,653 0.033 0.030 0.016
MB 6,313 3.975 2.314 15.629 8,653 4.525 2.228 63.156
ROA 6,313 -0.018 0.037 0.237 8,653 -0.001 0.044 0.203
WC 6,313 14.497 0.244 675.109 8,653 6.641 0.221 340.159
DIV 6,313 0.362 0.000 0.481 8,653 0.368 0.000 0.482
MERGER 6,313 0.082 0.000 0.274 8,653 0.070 0.000 0.255
RESTRUCT 6,313 0.348 0.000 0.476 8,653 0.313 0.000 0.464
LOSS 6,313 0.314 0.000 0.464 8,653 0.274 0.000 0.446
LEV 6,313 0.449 0.447 0.213 8,653 0.458 0.459 0.213
AUDIT 6,313 0.262 0.000 0.440 8,653 0.254 0.000 0.435
EM 6,313 -0.002 0.005 0.133 8,653 0.002 0.009 0.146
FCF 6,313 0.029 0.071 0.221 8,653 0.047 0.078 0.184
DVS_GEO 5,163 0.579 0.527 0.558 7,045 0.571 0.520 0.562
DVE_BIZ 4,795 0.354 0.000 0.523 6,483 0.370 0.000 0.513
GINDEX 2,565 9.002 9.000 2.496 2,639 9.091 9.000 2.500
Note: All variables are defined in Table 4.1. The variables CAPEXt+1 (Value) and AT (Value) are stated in
USD million.
190
Table 5.4 Descriptive Statistics – Selling, General and Administrative Costs
Full Sample
Variable N Mean Lower
Quartile
Median Upper
Quartile
Std Dev
SGAt+1 (Value) 13,780 $675.70 $45.19 $121.72 $374.94 $2,646.73
SGAt+1 13,780 -1.586 -2.082 -1.442 -0.942 0.961
Ψ 13,780 1.808 0.809 1.451 2.441 1.545
1-R2 13,780 0.788 0.692 0.810 0.920 0.158
AT (Value) 13,780 $3,686.18 $182.26 $593.86 $2,018.73 $14,148.17
SIZE 13,780 6.468 5.205 6.387 7.610 1.769
ANALYST 13,780 7.304 2.000 5.000 10.000 6.543
BIDASK 13,777 0.005 0.001 0.002 0.005 0.010
FIRM AGE (Years) 13,780 20.959 10.000 15.000 28.000 14.676
AGE 13,780 2.814 2.303 2.708 3.332 0.676
StdROE 13,780 0.489 0.023 0.054 0.142 8.691
StdCF 13,780 89.851 6.215 17.474 53.809 333.856
StdRET 13,780 0.032 0.021 0.028 0.040 0.017
MB 13,780 4.133 1.439 2.228 3.626 50.784
ROA 13,780 0.006 -0.008 0.044 0.084 0.184
WC 13,780 0.707 0.096 0.224 0.459 13.857
DIV 13,780 0.373 0.000 0.000 1.000 0.484
MERGER 13,780 0.077 0.000 0.000 0.000 0.267
RESTRUCT 13,780 0.340 0.000 0.000 1.000 0.474
LOSS 13,780 0.271 0.000 0.000 1.000 0.445
LEV 13,780 0.454 0.286 0.453 0.603 0.211
AUDIT 13,780 0.258 0.000 0.000 1.000 0.438
EM 13,780 0.001 -0.036 0.007 0.048 0.121
EMP 13,780 0.007 0.002 0.004 0.007 0.019
DVS_GEO 11,543 0.589 0.000 0.544 0.997 0.561
DVS_BIZ 10,327 0.377 0.000 0.000 0.685 0.522
GINDEX 4,922 9.081 7.000 9.000 11.000 2.512
Note: All variables are defined in Table 4.1. The variables SGAt+1 (Value) and AT (Value) are
stated in USD million.
191
Table 5.4 Descriptive Statistics – Selling, General and Administrative Costs
(Continued)
Increasing ψ Decreasing ψ
Variable N Mean Median Std Dev N Mean Median Std Dev
SGAt+1 (Value) 5,809 $741.49 $127.58 $2,790.27 7,971 $627.75 $118.15 $2,536.12
SGAt+1 5,809 -1.519 -1.397 0.919 7,971 -1.636 -1.473 0.988
ψ 5,809 2.315 1.826 1.720 7,971 1.438 1.186 1.284
1-R2 5,809 0.841 0.861 0.133 7,971 0.750 0.766 0.164
AT (Value) 5,809 $3,819.08 $597.61 $14,884.76 7,971 $3,589.34 $592.49 $13,586.36
SIZE 5,809 6.459 6.393 1.801 7,971 6.475 6.384 1.746
ANALYST 5,809 7.593 5.000 6.979 7,971 7.093 5.000 6.198
BIDASK 5,807 0.005 0.002 0.009 7,970 0.005 0.002 0.010
FIRM AGE
(Years)
5,809 21.111 15.000 14.695 7,971 20.849 15.000 14.662
AGE 5,809 2.825 2.708 0.668 7,971 2.806 2.708 0.681
StdROE 5,809 0.629 0.056 11.676 7,971 0.388 0.053 5.588
StdCF 5,809 88.558 17.508 344.571 7,971 90.794 17.429 325.843
StdRET 5,809 0.031 0.027 0.018 7,971 0.033 0.030 0.016
MB 5,809 3.733 2.277 13.915 7,971 4.424 2.197 65.707
ROA 5,809 -0.002 0.040 0.193 7,971 0.012 0.046 0.177
WC 5,809 0.724 0.237 8.467 7,971 0.695 0.217 16.725
DIV 5,809 0.370 0.000 0.483 7,971 0.374 0.000 0.484
MERGER 5,809 0.084 0.000 0.278 7,971 0.072 0.000 0.259
RESTRUCT 5,809 0.360 0.000 0.480 7,971 0.325 0.000 0.469
LOSS 5,809 0.292 0.000 0.455 7,971 0.256 0.000 0.436
LEV 5,809 0.448 0.445 0.211 7,971 0.458 0.460 0.211
AUDIT 5,809 0.264 0.000 0.441 7,971 0.254 0.000 0.435
EM 5,809 -0.001 0.004 0.128 7,971 0.003 0.009 0.116
EMP 5,809 0.007 0.004 0.020 7,971 0.007 0.004 0.018
DVS_GEO 4,890 0.595 0.554 0.558 6,653 0.584 0.538 0.564
DVS_BIZ 4,383 0.367 0.000 0.527 5,944 0.384 0.000 0.518
GINDEX 2,421 9.032 9.000 2.507 2,501 9.129 9.000 2.517
Note: All variables are defined in Table 4.1. The variables SGAt+1 (Value) and AT (Value) are stated in
USD million.
192
The descriptive statistics reported in Tables 5.2 to 5.4 suggest that the means of the
monetary values for all three corporate expenditures of the subsequent year are on
average higher than their median. This phenomenon reflects skewness of the distribution
on the right tail and therefore, a logarithm transformation is performed to “normalize”
the sample distribution (Wooldridge, 2009, p. 119). The mean values of relative
idiosyncratic volatility (1-R2) range from 0.788 (SGA costs) to 0.797 (R&D costs) while
its medians range from 0.810 (SGA costs) to 0.822 (R&D costs). The relative
idiosyncratic volatility (1-R2) of this study is computed using the same specification of
the market model used by Ferreira and Laux (2007), i.e., based on the correlation
between firms‟ daily stock return and return of the corresponding market. The relative
idiosyncratic volatility is higher when firms‟ stock returns do not move simultaneously
with the market, reflecting higher informativeness of stock prices. The value of 1-R2
computed in this study is lower when compared to the reported mean and median values
of 1-R2 by Ferreira and Laux‟s (2007),
32 that is, 0.854 and 0.907 respectively.
Nevertheless, the standard deviation of 1-R2 reported in this study, that is, 0.158 for each
of the corporate expenditure is comparable to 0.155 reported by Ferreira and Laux
(2007).
32 It is possible that the value of 1-R
2 is lower in this study compared to Ferreira and Laux (2007) because
of different sample size and years covered. Ferreira and Laux (2007) use a total of 161,691 observations
for the years 1990 to 2001 while the sample sizes of this study range from 8,513 (R&D costs) to 14,966
(CAPEX) for the years 2003 to 2009.
193
This study compares two sub-samples with opposing direction of the movement in
idiosyncratic volatility. The mean and median level of the relative idiosyncratic
volatility, 1-R2, is higher for the group that is experiencing improvement in idiosyncratic
volatility for all three proxies of corporate expenditure when compared to the group with
declining idiosyncratic volatility. For both R&D costs and CAPEX groups, the mean
value of market-to-book ratio (representing growth) is higher but the working capital
ratio (indicating “slack” resources) is lower for the sub-sample showing a deterioration
in idiosyncratic volatility. However, there are no significant differences noted between
these two sub-samples for all three types of corporate expenditure, in terms of their
monetary value of the subsequent year, firm size, analyst following, bid-ask spreads and
firm age.
5.2.2 Pearson Correlations
Tables 5.5 to 5.7 present the matrix of Pearson pair-wise correlations between all
variables used in the analysis for the three proxies of corporate expenditure, namely,
R&D expenditure, CAPEX and SGA costs respectively.
194
Table 5.5 Pearson Correlations – R&D Expenditure
Variable N (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
N 8,513 8,513 8,513 8,513 8,510 8,513 8,513 8,513 8,513 8,513 8,513
(1) R&Dt+1 8,513 1.000 0.247a
-0.439a
-0.050a
0.155a
-0.348a
0.030a
-0.142a
0.256a
0.017 -0.362a
(2) ψ 8,513 1.000 -0.591a
-0.380a
0.501a
-0.275a
0.018c
-0.152a
0.281a
-0.005 -0.282a
(3) SIZE 8,513 1.000 0.661a
-0.467a
0.472a
-0.024b
0.428a
-0.450a
-0.001 0.374a
(4) ANALYST 8,513 1.000 -0.346a
0.159a
-0.029a
0.334a
-0.292a
0.009 0.197a
(5) BIDASK 8,510 1.000 -0.155a
0.018 -0.095a
0.480a
-0.005 -0.286a
(6) AGE 8,513 1.000 0.009 0.223a
-0.283a
0.011 0.208a
(7) StdROE 8,513 1.000 -0.004 0.053a
0.008 -0.050a
(8) StdCF 8,513 1.000 -0.118a
0.002 0.072a
(9) StdRET 8,513 1.000 0.010 -0.391a
(10) MB 8,513 1.000 0.005
(11) ROA 8,513
1.000
Note: All variables are defined in Table 4.1. Superscripts a, b and c stand for statistical significance at a p-value of less than 1%, 5% and 10%
levels respectively.
195
Table 5.5 Pearson Correlations - R&D Expenditure (Continued)
Variable N (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22)
N 8,513 8,513 8,513 8,513 8,513 8,513 8,513 8,513 7,194 6,471 3,030
(1) R&Dt+1 8,513 0.034a -0.415a -0.036a -0.083a 0.401a -0.104a -0.049a -0.087a 0.050a -0.349a -0.197a
(2) ψ 8,513 0.015 -0.252a -0.102a -0.200a 0.277a -0.047a -0.118a -0.014 -0.195a -0.235a -0.098a
(3) SIZE 8,513 -0.015 0.442a 0.150a 0.312a -0.402a 0.182a 0.183a 0.009 0.231a 0.448a 0.169a
(4) ANALYST 8,513 0.000 0.142a 0.082a 0.104a -0.256a 0.027b 0.122a -0.018c 0.158a 0.150a -0.058a
(5) BIDASK 8,510 -0.004 -0.158a -0.077a -0.093a 0.293a 0.029a -0.107a -0.005 -0.112a -0.146a -0.093a
(6) AGE 8,513 -0.017 0.500a 0.036a 0.163a -0.290a 0.154a 0.041a 0.069a 0.107a 0.401a 0.332a
(7) StdROE 8,513 -0.001 -0.035a -0.016 0.005 0.038a 0.097a -0.010 -0.012 0.026b -0.002 0.014
(8) StdCF 8,513 -0.004 0.191a 0.033a 0.073a -0.103a 0.085a 0.081a 0.010 0.074a 0.195a -0.021
(9) StdRET 8,513 0.003 -0.300a -0.037a -0.036a 0.422a 0.089a -0.103a -0.116a -0.070a -0.227a -0.223a
(10) MB 8,513 -0.000 -0.004 -0.006 0.005 0.014 0.012 0.013 -0.019c 0.020c 0.014 0.008
(11) ROA 8,513 -0.009 0.186a 0.030a 0.021b -0.515a -0.348a 0.056a 0.281a 0.104a 0.156a 0.081a
(12) WC 8,513 1.000 -0.016 -0.007 0.017 0.031a -0.028b -0.013 -0.001 -0.046a -0.018 -0.023
(13) DIV 8,513 1.000 0.000 0.118a -0.315a 0.151a 0.047a 0.049a 0.024b 0.345a 0.279a
(14) MERGER 8,513 1.000 0.130a -0.019c 0.013 0.020b -0.040a 0.034a 0.043a 0.016
(15) RESTRUCT 8,513 1.000 0.037a 0.172a 0.104a -0.022b 0.179a 0.146a 0.078a
(16) LOSS 8,513 1.000 0.084a -0.045a -0.202a -0.048a -0.231a -0.104a
(17) LEV 8,513 1.000 0.012 -0.142a -0.014 0.130a 0.183a
(18) AUDIT 8,513 1.000 -0.013 0.089a 0.066a 0.045b
(19) EM 8,513 1.000 0.006 0.044a 0.021
(20) DVS_GEO 7,194 1.000 -0.049a -0.065a
(21) DVS_BIZ 6,471 1.000 0.245a
(22) GINDEX 3,033 1.000
Note: All variables are defined in Table 4.1. Superscripts a, b and c stand for statistical significance at a p-value of less than 1%, 5% and 10% levels respectively.
196
Table 5.5 shows that idiosyncratic volatility is positively related to subsequent year‟s
R&D costs. The correlation values between all variables are found to be lower than 0.80,
thus they are not highly correlated with each other. There is also no indication of
multicollinearity problem as the variance inflation factors (VIF) of the relevant
regressions are found to be less than the cut-off value of 10. It is also observed that
idiosyncratic volatility is negatively related to firm size indicating that a greater amount
of private information is impounded in stock prices of small firms. This is consistent
with the empirical findings of Chen et al. (2007) and Bakke and Whited (2010). Further,
idiosyncratic volatility is found to be negatively associated with analyst following and
positively associated with bid-ask spreads in the R&D costs sample, in line with
findings by Chen et al. (2007).
A summary of information reported in Table 5.5 reveals that higher R&D investment of
the subsequent year is observed in firms that are smaller, younger and less diversified.
Firms with higher R&D investment portray greater return volatility and higher leverage.
They are also more likely to incur losses while less likely to pay dividend. The sign of
variables such as return on assets (ROA), loss dummy (LOSS), dividend dummy (DIV),
leverage (LEV) and business diversification level (DVS_BIZ) in Table 5.5 is consistent
with the prediction made in item 4.5.4.1. The correlations between R&D costs and
variables such as firm size (SIZE) and firm age (AGE) reported in Table 5.5 are not as
expected in item 4.5.4.1.
197
Table 5.6 presents the Pearson correlations values between all variables used in the
analysis for the CAPEX sample.
In Table 5.6, idiosyncratic volatility is negatively related to CAPEX of the subsequent
year. All variables examined for the CAPEX sample are not highly correlated with each
other as the correlation values shown are not greater than 0.80. One exception is the
correlation value reported between two control variables, i.e., return on assets (ROA)
and free cash flow (FCF) that is at 0.875. Wooldridge (2009, pp. 98-99) opines that high
correlations among control variables do not affect the relationship between the
dependent (CAPEX of the subsequent year) and independent variable (idiosyncratic
volatility). As an alternative, the VIF of the relevant regressions are examined and they
are all found to be less than the cut-off value of 10, indicating no sign of
multicollinearity problem.
It is also noted that idiosyncratic volatility for CAPEX sample is negatively related to
firm size and analyst following but positively related to bid-ask spreads. These results
provide empirical support to the research findings by Chen et al. (2007) and Bakke and
Whited (2010) when they conclude that greater private information is integrated in stock
prices of small firms, firms with low analyst following and high bid-ask spreads.
198
Table 5.6 Pearson Correlations – Capital Expenditure
Variable N (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
N 14,966 14,966 14,966 14,966 14,962 14,966 14,966 14,966 14,966 14,966 14,966 14,966
(1) CAPEXt+1 14,966 1.000 -0.100a
0.201a
0.159a
-0.153a
0.060a
0.006 0.075a
-0.180a
-0.002 0.219a
-0.011
(2) ψ 14,966 1.000 -0.549a
-0.362a
0.468a
-0.233a
0.018b
-0.160a
0.158a
-0.002 -0.238a
0.012
(3) SIZE 14,966 1.000 0.634a
-0.421a
0.411a
-0.004 0.448a
-0.398a
-0.010 0.333a
-0.016b
(4) ANALYST 14,966 1.000 -0.320a
0.131a
-0.010 0.341a
-0.284a
0.007 0.199a
-0.001
(5) BIDASK 14,962 1.000 -0.117a
0.017b
-0.097a
0.457a
-0.005 -0.267a
-0.003
(6) AGE 14,966 1.000 -0.017b
0.221a
-0.241a
0.004 0.177a
-0.014c
(7) StdROE 14,966 1.000 -0.002 0.010 0.015c
-0.006 -0.000
(8) StdCF 14,966 1.000 -0.114a
0.004 0.070a
-0.004
(9) StdRET 14,966
1.000 00.013 -0.391a
0.004
(10) MB 14,966
1.000 -0.019b
-0.000
(11) ROA 14,966
1.000 -0.031a
(12) WC 14,966
1.000
Note: All variables are defined in Table 4.1. Superscripts a, b and c stand for statistical significance at a p-value of less than 1%, 5% and 10% levels
respectively.
199
Table 5.6 Pearson Correlations – Capital Expenditure (Continued)
(13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23)
N 14,966 14,966 14,966 14,966 14,966 14,966 14,966 14,966 12,208 11,278 5,204
(1) CAPEXt+1 14,966 0.137a
-0.064a
-0.130a
-0.204a
0.105a
-0.053a
-0.017a
0.259a
-0.088a
0.021a
0.037a
(2) ψ 14,966 -0.234a
-0.077a
-0.131a
0.223a
-0.108a
-0.107a
-0.002 -0.024a
-0.168a
-0.175a
-0.090a
(3) SIZE 14,966 0.414a
0.121a
0.210a
-0.328a
0.399a
0.181a
0.001 0.335a
0.146a
0.332a
0.152a
(4) ANALYST 14,966 0.143a
0.054a
0.054a
-0.227a
0.068a
0.129a
-0.022a
0.194a
0.122a
0.083a
-0.027b
(5) BIDASK 14,962 -0.148a
-0.052a
-0.051a
0.270a
-0.011 -0.091a
-0.005 -0.126a
-0.093a
-0.104a
-0.075a
(6) AGE 14,966 0.430a
0.024a
0.146a
-0.212a
0.179a
0.036a
0.054a
0.162a
0.105a
0.309a
0.314a
(7) StdROE 14,966 -0.010 -0.003 -0.005 0.018b
0.031a
-0.007 0.001 -0.006 -0.007 0.016c
0.012
(8) StdCF 14,966 0.181a
0.035a
0.082a
-0.077a
0.129a
0.080a
0.006 0.064a
0.083a
0.158a
0.014
(9) StdRET 14,966 -0.279a
-0.019b
-0.003 0.430a
-0.005 -0.088a
-0.066a
-0.316a
-0.033a
-0.177a
-0.164a
(10) MB 14,966 -0.009 -0.008 0.001 0.020b
0.069a
0.010 -0.024a
-0.028a
0.017c
0.008 0.006
(11) ROA 14,966 0.205a
0.009 -0.063a
-0.599a
-0.024a
0.050a
0.309a
0.875a
0.023b
0.113a
0.051a
(12) WC 14,966 -0.014c
-0.005 -0.011 0.030a
-0.024a
-0.010 -0.000 -0.039a
-0.029a
-0.015 -0.016
(13) DIV 14,966 1.000 -0.013 0.055a
-0.265a
0.209a
0.063a
0.048a
0.138a
-0.006 0.240a
0.227a
(14) MERGER 14,966 1.000 0.108a
-0.005 0.048a
0.003 -0.034a
-0.035a
0.045a
0.041a
0.021
(15) RESTRUCT 14,966 1.000 0.102a
0.161a
0.060a
-0.038a
-0.003
0.239a
0.157a
0.093a
(16) LOSS 14,966 1.000 0.008 -0.046a
-0.215a
-0.486a
0.015c
-0.138a
-0.072a
(17) LEV 14,966
1.000 0.043a
-0.071a
-0.020b
-0.057a
0.163a
0.172a
(18) AUDIT 14,966
1.000 -0.009 0.048a
0.056a 0.041
a 0.031
b
(19) EM 14,966
1.000 0.145a
0.006 0.035a 0.018
(20) FCF 14,966 1.000 -0.036b -0.100
a 0.033
b
(21) DVS_GEO 12,208
1.000 0.018c 0.005
(22) DVS_BIZ 11,278
1.000 0.204a
(23) GINDEX 5,204
1.000
Note: All variables are defined in Table 4.1. Superscripts a, b and c stand for statistical significance at a p-value of less than 1%, 5% and 10%
levels respectively.
200
Further, Table 5.6 reports that CAPEX of the subsequent year is positively associated
with several control variables such as firm size (SIZE), analyst following (ANALYST),
firm age (AGE), return on assets (ROA) as well as free cash flow (FCF). A negative
association is shown between CAPEX and dividend dummy (DIV), merger dummy
(MERGER), restructuring dummy (RESTRUCT) and loss dummy (LOSS). These
finding are consistent with predictions made in item 4.5.4.1.
Table 5.7 presents the Pearson correlations values between all variables for corporate
expenditure represented by SGA costs.
Table 5.7 reveal a positive correlation between a current year‟s idiosyncratic volatility
and the subsequent year‟s SGA costs. All variables examined for this sample are not
highly correlated to each other as their correlation values is lower than 0.80. There is
also no sign of multicollinearity because the VIF of the relevant regressions are found to
be less than the cut-off value of 10. Data reported in Table 5.7 also show that
idiosyncratic volatility is negative associated to firm size and analyst following while
positively associated to bid-ask spreads. These findings are in line with empirical studies
such as Chen et al. (2007) and Bakke and Whited (2010).
201
Table 5.7 Pearson Correlations – Selling, General and Administrative Costs
Variable N (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
N 13,780 13,780 13,780 13,780 13,777 13,780 13,780 13,780 13,780 13,780 13,780 13,780
(1) SGAt+1 13,780 1.000 0.246a
-0.426a
-0.172a
0.166a
-0.078a
0.029a
-0.148a
0.105a
0.013 -0.201a
0.002
(2) ψ 13,780 1.000 -0.566a
-0.368a
0.470a
-0.234a
0.029a
-0.162a
0.129a
-0.005 -0.221a
0.011
(3) SIZE 13,780 1.000 0.636a
-0.414a
0.396a
-0.027a
0.451a
-0.377a
-0.005 0.295a
-0.016c
(4) ANALYST 13,780 1.000 -0.311a
0.117a
-0.023a
0.342a
-0.275a
0.009 0.204a
-0.014c
(5) BIDASK 13,777 1.000 -0.108a
0.025a
-0.094a
0.447a
-0.008 -0.269a
-0.003
(6) AGE 13,780 1.000 -0.014c
0.215a
-0.230a
0.008 0.158a
-0.029a
(7) StdROE 13,780 1.000 -0.002 0.042a
0.019b
-0.041a
-0.000
(8) StdCF 13,780 1.000 -0.107a
0.005 0.064a
-0.009
(9) StdRET 13,780 1.000 0.008 -0.384a
0.014
(10) MB 13,780 1.000 -0.008 -0.001
(11) ROA 13,780
1.000 -0.042a
(12) WC 13,780
1.000
Note: All variables are defined in Table 4.1. Superscripts a, b and c stand for statistical significance at a p-value of less than 1%, 5% and 10% levels respectively.
202
Table 5.7 Pearson Correlations – Selling, General and Administrative Costs (Continued)
N (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23)
N 13,780 13,780 13,780 13,780 13,780 13,780 13,780 13,780 11,543 10,327 4,922
(1) SGAt+1 13,780 -0.206a
-0.022a
0.063a
0.159
-0.200a
-0.061a
-0.039a
0.113a
0.032a
-0.143a
-0.044a
(2) ψ 13,780 -0.236a
-0.077a
-0.138a
0.211a
-0.102a
0.110a
-0.003 0.061a
-0.170a
-0.177a
-0.088a
(3) SIZE 13,780 0.395a
0.117a
0.208a
-0.290a
0.397a
0.181a
-0.007 -0.068a
0.142a
0.319a
0.159a
(4) ANALYST 13,780 0.131a
0.054a
0.051a
-0.215a
0.059a
0.135a
-0.025a
-0.047a
0.123a
0.071a
-0.024c
(5) BIDASK 13,777 -0.142a
-0.051a
-0.052a
0.267a
-0.005
-0.091a
-0.010 0.035a
-0.091a
-0.096a
-0.079a
(6) AGE 13,780 0.429a
0.019b
0.138a
-0.187a
0.172a
0.035a
0.054a
0.016c
0.098a
0.302a
0.315a
(7) StdROE 13,780 -0.025a
-0.006 0.007 0.031a
0.064a
-0.012 -0.018 0.003 0.020b
-0.003 0.012
(8) StdCF 13,780 0.173a
0.034a
0.081a
-0.066a
0.127a
0.079a
0.003 -0.043a
0.083a
0.151a
0.013
(9) StdRET 13,780 -0.267a
-0.016c
0.003
0.421a
0.011
-0.086a
-0.117a
-0.012 -0.025a
-0.162a
-0.167a
(10) MB 13,780 -0.007 -0.007 0.008 0.015c
0.063a
0.012 -0.007
-0.001 0.018c
0.011 0.007
(11) ROA 13,780 0.191a
0.009
0.101a
-0.619a
-0.054a
0.048a
0.349a
0.016c
0.001
0.082a
0.044a
(12) WC 13,780 -0.027a
-0.008 -0.016c
0.047a
-0.021b
0.006 -0.002 -0.012 -0.008
-0.022b
-0.120a
(13) DIV 13,780 1.000 -0.020b
0.048a
-0.238a
0.205a
0.059a
0.051a
0.010 -0.014
0.229a
0.228a
(14) MERGER 13,780 1.000 0.108a
0.007
0.045a
0.003
-0.039a
-0.033a
0.046a
0.035a
0.019
(15) RESTRUCT 13,780 1.000 0.128a
0.162a
0.063a
-0.056a
-0.040a
0.233a
0.147a
0.095a
(16) LOSS 13,780 1.000 0.032a
-0.044a
-0.236a
-0.018b
-0.032a
-0.112a
-0.064a
(17) LEV 13,780
1.000 0.036a
-0.072a
0.009 -0.062a
0.166a
0.178a
(18) AUDIT 13,780 1.000 -0.004 0.008 0.056a 0.044
a 0.026
c
(19) EM 13,780 1.000 -0.001 0.001
0.037a
0.021
(20) EMP 13,780 1.000 -0.096a
-0.036a 0.016
(21) DVS_GEO 11,543
1.000 0.015 -0.000
(22) DVS_BIZ 10,327
1.000 0.207a
(23) GINDEX 4,922 1.000
Note: All variables are defined in Table 4.1. Superscripts a, b and c stand for statistical significance at a p-value of less than 1%, 5% and 10% levels
respectively.
203
Data reported in Table 5.7 suggests that SGA costs in the subsequent year is negatively
associated with several variables such as firm size (SIZE), analyst following
(ANALYST), firm age (AGE), cash flow volatility (StdCF), return on assets (ROA),
dividend dummy (DIV), leverage (LEV) and business diversification (DVS_BIZ). The
findings in Table 5.7 also show that SGA of the subsequent year is positively associated
with return volatility (StdRET), restructuring dummy (RESTRUCT) and employee
intensity (EMP). The sign of variables DIV, RESTRUCT, LOSS, LEV and EMP are
consistent with the predictions made in item 4.5.4.1.
5.2.3 Descriptive Statistics by Firm Size
The descriptive statistics of all variables used in the analysis by firm size of three
corporate expenditures, namely, R&D expenditure, CAPEX and SGA costs are
exhibited in Tables 5.8 to 5.10 respectively. These tables summarize the analysed data
for the full sample as well as two sub-samples being large and small firms. Large (small)
firms are firms that are above (below) the median value of the natural logarithm of the
total assets of the full sample.
204
Table 5.8 Descriptive Statistics – R&D Expenditure by Firm Size Full Sample Large Firms Small Firms
Variable N Mean Median Std Dev N Mean Median Std Dev N Mean Median Std Dev
R&D t+1 (Value) 8,513 $156.80 $24.40 $631.63 4,257 $295.43 $65.10 $871.21 4,256 $18.14 $11.17 $21.42
R&D t+1 8,513 -2.943 -2.749 1.383 4,257 -3.499 -3.354 1.298 4,256 -2.387 -2.251 1.233
ψ 8,513 1.909 1.527 1.667 4,257 1.061 1.019 0.890 4,256 2.756 2.391 1.824
1-R2 8,513 0.797 0.822 0.158 4,257 0.713 0.735 0.151 4,256 0.880 0.916 0.114
AT (Value) 8,513 $3,649.84 $379.72 $15,481.43 4,257 $7,160.69 $1,587.09 $21,323.19 4,256 $138.15 $114.50 $99.11
SIZE 8,513 6.121 5.939 1.909 4,257 7.650 7.370 1.325 4,256 4.592 4.741 0.926
ANALYST 8,513 7.154 5.000 6.585 4,257 10.547 9.000 7.316 4,256 3.761 3.000 3.189
BIDASK 8,510 0.006 0.002 0.010 4,254 0.002 0.001 0.003 4,256 0.009 0.005 0.012
FIRM AGE (Years) 8,513 20.457 15.000 14.956 4,257 26.508 19.000 17.363 4,256 14.404 12.000 8.524
AGE 8,513 2.783 2.708 0.677 4,257 3.047 2.944 0.698 4,256 2.518 2.485 0.537
StdROE 8,513 0.769 0.074 9.994 4,257 0.756 0.050 13.529 4,256 0.781 0.115 4.090
StdCF 8,513 90.215 13.993 388.443 4,257 171.281 40.654 537.115 4,256 9.130 5.637 11.598
StdRET 8,513 0.034 0.031 0.019 4,257 0.027 0.024 0.014 4,256 0.041 0.037 0.022
MB 8,513 3.988 2.519 68.048 4,257 4.469 2.507 87.376 4,256 3.507 2.539 40.327
ROA 8,513 -0.061 0.032 0.352 4,257 0.033 0.052 0.117 4,256 -0.156 -0.021 0.465
WC 8,513 16.693 0.378 675.765 4,257 9.878 0.271 485.526 4,256 23.509 0.561 823.193
DIV 8,513 0.296 0.000 0.456 4,257 0.462 0.000 0.499 4,256 0.129 0.000 0.336
MERGER 8,513 0.081 0.000 0.273 4,257 0.117 0.000 0.322 4,256 0.045 0.000 0.207
RESTRUCT 8,513 0.408 0.000 0.491 4,257 0.553 1.000 0.497 4,256 0.262 0.000 0.440
LOSS 8,513 0.374 0.000 0.484 4,257 0.203 0.000 0.402 4,256 0.545 1.000 0.498
LEV 8,513 0.442 0.404 0.314 4,257 0.505 0.506 0.237 4,256 0.378 0.293 0.365
AUDIT 8,513 0.248 0.000 0.432 4,257 0.321 0.000 0.467 4,256 0.174 0.000 0.379
EM 8,513 -0.006 0.007 0.191 4,257 -0.006 0.004 0.108 4,256 -0.007 0.009 0.248
DVS_GEO 7,194 0.750 0.707 0.557 3,899 0.856 0.878 0.520 3,295 0.624 0.584 0.574
DVS_BIZ 6,471 0.380 0.000 0.554 3,366 0.565 0.427 0.625 3,105 0.179 0.000 0.371
GINDEX 3,033 9.136 9.000 2.496 2,492 9.323 9.000 2.487 541 8.279 8.000 2.357
Note: All variables are defined in Table 4.1. The variables R&D t+1 (Value) and AT (Value) are stated in USD million. Large (small) firms are defined as firms
above (below) the median value of natural logarithm of total assets of the full sample.
205
Table 5.9 Descriptive Statistics – Capital Expenditure by Firm Size Full Sample Large Firms Small Firms
Variable N Mean Median Std Dev N Mean Median Std Dev N Mean Median Std Dev
CAPEXt+1 (Value) 14,966 $223.06 $20.68 $1,138.89 7,483 $433.26 $88.81 $1,582.77 7,483 $12.86 $4.73 $27.58
CAPEXt+1 14,966 -3.485 -3.470 1.042 7,483 -3.318 -3.341 0.919 7,483 -3.652 -3.634 1.128
ψ 14,966 1.855 1.473 1.658 7,483 1.099 1.050 0.942 7,483 2.611 2.194 1.862
1-R2 14,966 0.791 0.814 0.158 7,483 0.718 0.741 0.153 7,483 0.864 0.900 0.125
AT (Value) 14,966 $3,634.68 $571.69 $13,760.20 7,483 $7,058.82 $1,988.03 $18,847.70 7,483 $210.54 $171.32 $152.72
SIZE 14,966 6.432 6.349 1.793 7,483 7.864 7.595 1.197 7,483 4.999 5.144 0.946
ANALYST 14,966 7.267 5.000 6.485 7,483 10.483 9.000 7.127 7,483 4.051 3.000 3.553
BIDASK 14,962 0.005 0.002 0.010 7,480 0.002 0.001 0.004 7,482 0.008 0.004 0.012
FIRM AGE (Years) 14,966 20.659 15.000 14.544 7,483 25.794 20.000 16.597 7,483 15.524 13.000 9.740
AGE 14,966 2.800 2.708 0.675 7,483 3.024 2.996 0.698 7,483 2.575 2.565 0.568
StdROE 14,966 0.924 0.056 52.873 7,483 1.141 0.042 73.967 7,483 0.707 0.078 10.964
StdCF 14,966 87.982 17.108 324.939 7,483 164.984 50.300 446.245 7,483 10.979 6.758 13.773
StdRET 14,966 0.033 0.029 0.017 7,483 0.027 0.023 0.015 7,483 0.038 0.034 0.018
MB 14,966 4.293 2.261 49.083 7,483 3.630 2.251 13.186 7,483 4.956 2.273 68.146
ROA 14,966 -0.009 0.041 0.218 7,483 0.043 0.051 0.095 7,483 -0.060 0.022 0.284
WC 14,966 9.955 0.229 509.067 7,483 1.174 0.159 53.512 7,483 18.736 0.348 717.855
DIV 14,966 0.365 0.000 0.482 7,483 0.533 1.000 0.499 7,483 0.198 0.000 0.399
MERGER 14,966 0.075 0.000 0.263 7,483 0.097 0.000 0.297 7,483 0.052 0.000 0.222
RESTRUCT 14,966 0.328 0.000 0.469 7,483 0.412 0.000 0.492 7,483 0.243 0.000 0.429
LOSS 14,966 0.291 0.000 0.454 7,483 0.166 0.000 0.372 7,483 0.416 0.000 0.493
LEV 14,966 0.454 0.454 0.213 7,483 0.536 0.541 0.186 7,483 0.373 0.336 0.207
AUDIT 14,966 0.257 0.000 0.437 7,483 0.316 0.000 0.465 7,483 0.198 0.000 0.399
EM 14,966 0.000 0.007 0.141 7,483 0.001 0.006 0.090 7,483 0.000 0.008 0.178
FCF 14,966 0.039 0.075 0.201 7,483 0.087 0.084 0.070 7,483 -0.008 0.058 0.267
DVS_GEO 12,208 0.574 0.523 0.560 6,413 0.635 0.627 0.554 5,795 0.507 0.369 0.560
DVS_BIZ 11,278 0.363 0.000 0.517 5,682 0.501 0.322 0.580 5,596 0.223 0.000 0.398
GINDEX 5,204 9.047 9.000 2.498 4,023 9.261 9.000 2.531 1,181 8.315 8.000 2.231
Note: All variables are defined in Table 4.1. The variables CAPEXt+1 (Value) and AT (Value) are stated in USD million. Large (small) firms are defined as firms above (below) the
median value of natural logarithm of total assets of the full sample.
206
Table 5.10 Descriptive Statistics – Selling, General and Administrative Costs by Firm Size Full Sample Large Firms Small Firms
Variable N Mean Median Std Dev N Mean Median Std Dev N Mean Median Std Dev
SGAt+1 (Value) 13,780 $675.70 $121.72 $2,646.73 6,890 $1,277.52 $366.68 $3,644.44 6,890 $73.88 $50.78 $70.23
SGAt+1 13,780 -1.586 -1.442 0.961 6,890 -1.934 -1.767 0.982 6,890 -1.238 -1.151 0.802
ψ 13,780 1.808 1.451 1.545 6,890 1.084 1.038 0.911 6,890 2.532 2.142 1.701
1-R2 13,780 0.788 0.810 0.158 6,890 0.715 0.738 0.153 6,890 0.861 0.895 0.127
AT (Value) 13,780 $3,686.18 $593.86 $14,148.17 6,890 $7,150.56 $2,018.73 $19,399.43 6,890 $221.81 $182.26 $159.08
SIZE 13,780 6.468 6.387 1.769 6,890 7.878 7.610 1.185 6,890 5.058 5.205 0.938
ANALYST 13,780 7.304 5.000 6.543 6,890 10.528 9.000 7.201 6,890 4.079 3.000 3.603
BIDASK 13,777 0.005 0.002 0.010 6,888 0.002 0.001 0.004 6,889 0.008 0.004 0.012
FIRM AGE (Years) 13,780 20.959 15.000 14.676 6,890 26.002 20.000 16.723 6,890 15.917 13.000 10.013
AGE 13,780 2.814 2.708 0.676 6,890 3.031 2.996 0.700 6,890 2.598 2.565 0.574
StdROE 13,780 0.489 0.054 8.691 6,890 0.295 0.042 4.599 6,890 0.684 0.071 11.396
StdCF 13,780 89.851 17.474 333.856 6,890 168.556 50.695 458.630 6,890 11.146 6.871 14.254
StdRET 13,780 0.032 0.028 0.017 6,890 0.027 0.023 0.015 6,890 0.037 0.034 0.017
MB 13,780 4.133 2.228 50.784 6,890 3.677 2.256 13.690 6,890 4.589 2.198 70.502
ROA 13,780 0.006 0.044 0.184 6,890 0.044 0.051 0.094 6,890 -0.032 0.030 0.237
WC 13,780 0.707 0.224 13.857 6,890 0.563 0.163 18.172 6,890 0.852 0.318 7.333
DIV 13,780 0.373 0.000 0.484 6,890 0.533 1.000 0.499 6,890 0.212 0.000 0.409
MERGER 13,780 0.077 0.000 0.267 6,890 0.100 0.000 0.300 6,890 0.054 0.000 0.226
RESTRUCT 13,780 0.340 0.000 0.474 6,890 0.423 0.000 0.494 6,890 0.257 0.000 0.437
LOSS 13,780 0.271 0.000 0.445 6,890 0.165 0.000 0.371 6,890 0.378 0.000 0.485
LEV 13,780 0.454 0.453 0.211 6,890 0.535 0.541 0.187 6,890 0.373 0.339 0.202
AUDIT 13,780 0.258 0.000 0.438 6,890 0.318 0.000 0.466 6,890 0.199 0.000 0.399
EM 13,780 0.001 0.007 0.121 6,890 0.001 0.006 0.090 6,890 0.002 0.008 0.146
EMP 13,780 0.007 0.004 0.019 6,890 0.006 0.003 0.018 6,890 0.008 0.004 0.019
DVS_GEO 11,543 0.589 0.544 0.561 5,953 0.649 0.642 0.553 5,590 0.525 0.410 0.562
DVS_BIZ 10,327 0.377 0.000 0.522 5,200 0.510 0.340 0.583 5,127 0.242 0.000 0.410
GINDEX 4,922 9.081 9.000 2.512 3,749 9.318 9.000 2.536 1,173 8.327 8.000 2.277
Note: All variables are defined in Table 4.1. The variables SGAt+1 (Value) and AT (Value) are stated in USD million. Large (small) firms are defined as firms above (below) the
median value of natural logarithm of total assets of the full sample.
207
Descriptive statistics reported in Tables 5.8 to 5.10 suggest that large firms invest more
in corporate expenditure of the subsequent year, namely, R&D expenditure, CAPEX and
SGA costs. This is consistent with prior empirical findings in the areas of R&D costs
(Rothwell, 1984), CAPEX (Vogt, 1994) and SGA costs (Banker et al., 2011b). However,
large firms display a lower level of stock price informativeness represented by
idiosyncratic volatility as shown in all three corporate expenditure samples in this study.
This is consistent with findings by Chen et al. (2007) and Bakke and Whited (2010) that
firm size is negatively associated with idiosyncratic volatility and are supported by the
Pearson correlations results displayed for the three corporate expenditure samples
displayed in Tables 5.5 to 5.7.
Tables 5.8 to 5.10 also reveal that for all three proxies of corporate expenditure, namely,
R&D expenditure, CAPEX and SGA costs, large firms are widely followed by analysts.
They are older, more profitable and are highly-leveraged. These firms also possess
higher free cash flows and are more likely to pay dividends but the volatilities of their
cash flows is are greater. It is also found in Tables 5.8 to 5.10 that a majority of large
firms are audited by specialist auditors. Large firms are more diversified and are
intensive in merger and restructuring activities. Small firms, on the other hand, are more
likely to grow faster (proxied by market-to-book value) though incurring losses. The
availability of slack resources (represented by working capital ratio) and employee
intensity are found to be greater in small firms.
208
5.2.4 Descriptive Statistics by Analyst Following
The descriptive statistics of all variables used in the study by analyst following for all
three corporate expenditures are presented in Tables 5.11 to 5.13 respectively. These
tables summarize the analysed data for the full sample as well as two sub-samples, i.e.,
firms with high and low analyst following. Firms with high (low) analyst following are
firms above (below) the median value of the sample on the number of analysts that
follows a firm.
209
Table 5.11 Descriptive Statistics – R&D Expenditure by Analyst Following Full Sample High Analyst Following Low Analyst Following
Variable N Mean Median Std Dev N Mean Median Std Dev N Mean Median Std Dev
R&D t+1 (Value) 8,513 $156.80 $24.40 $631.63 4,025 $304.56 $64.50 $885.09 4,488 $24.28 $10.72 $130.94
R&D t+1 8,513 -2.943 -2.749 1.383 4,025 -3.051 -2.838 1.311 4,488 -2.846 -2.645 1.438
ψ 8,513 1.909 1.527 1.667 4,025 1.195 1.137 0.961 4,488 2.548 2.170 1.891
1-R2 8,513 0.797 0.822 0.158 4,025 0.734 0.757 0.152 4,488 0.853 0.898 0.141
AT (Value) 8,513 $3,649.84 $379.72 $15,481.43 4,025 $7,009.70 $1,292.35 $21,201.66 4,488 $636.59 $142.28 $5,686.52
SIZE 8,513 6.121 5.939 1.909 4,025 7.278 7.164 1.706 4,488 5.084 4.958 1.425
ANALYST 8,513 7.154 5.000 6.585 4,025 12.262 10.000 6.315 4,488 2.573 2.000 1.444
BIDASK 8,510 0.006 0.002 0.010 4,023 0.002 0.001 0.002 4,487 0.009 0.005 0.012
FIRM AGE (Years) 8,513 20.457 15.000 14.956 4,025 23.059 16.000 17.192 4,488 18.123 14.000 12.155
AGE 8,513 2.783 2.708 0.677 4,025 2.870 2.773 0.733 4,488 2.704 2.639 0.611
StdROE 8,513 0.769 0.074 9.994 4,025 0.506 0.061 7.605 4,488 1.004 0.092 11.726
StdCF 8,513 90.215 13.993 388.443 4,025 167.690 34.444 545.634 4,488 20.733 7.041 95.028
StdRET 8,513 0.034 0.031 0.019 4,025 0.029 0.026 0.014 4,488 0.039 0.035 0.022
MB 8,513 3.988 2.519 68.048 4,025 5.158 2.929 96.236 4,488 2.939 2.136 21.820
ROA 8,513 -0.061 0.032 0.352 4,025 0.006 0.052 0.190 4,488 -0.121 0.007 0.442
WC 8,513 16.693 0.378 675.765 4,025 15.691 0.353 524.678 4,488 17.591 0.402 787.029
DIV 8,513 0.296 0.000 0.456 4,025 0.358 0.000 0.480 4,488 0.240 0.000 0.427
MERGER 8,513 0.081 0.000 0.273 4,025 0.109 0.000 0.312 4,488 0.056 0.000 0.229
RESTRUCT 8,513 0.408 0.000 0.491 4,025 0.471 0.000 0.499 4,488 0.351 0.000 0.477
LOSS 8,513 0.374 0.000 0.484 4,025 0.254 0.000 0.436 4,488 0.481 0.000 0.500
LEV 8,513 0.442 0.404 0.314 4,025 0.462 0.452 0.253 4,488 0.424 0.360 0.359
AUDIT 8,513 0.248 0.000 0.432 4,025 0.302 0.000 0.459 4,488 0.199 0.000 0.399
EM 8,513 -0.006 0.007 0.191 4,025 -0.011 0.003 0.159 4,488 -0.002 0.010 0.216
DVS_GEO 7,194 0.750 0.707 0.557 3,540 0.831 0.854 0.541 3,654 0.671 0.655 0.562
DVS_BIZ 6,471 0.380 0.000 0.554 3,066 0.462 0.000 0.619 3,405 0.305 0.000 0.475
GINDEX 3,033 9.136 9.000 2.496 2,054 9.111 9.000 2.452 979 9.190 9.000 2.585
Note: All variables are defined in Table 4.1. The variables R&D t+1 (Value) and AT (Value) are stated in USD million. Firms with high (low) analyst following are defined as firms
above (below) the median value of the full sample on the number of analysts that follow the firms.
210
Table 5.12 Descriptive Statistics - Capital Expenditure by Analyst Following Full Sample High Analyst Following Low Analyst Following
Variable N Mean Median Std Dev N Mean Median Std Dev N Mean Median Std Dev
CAPEXt+1 (Value) 14,966 $223.06 $20.68 $1,138.89 7,259 $406.99 $68.60 $1,567.32 7,707 $49.82 $7.25 $378.59
CAPEXt+1 14,966 -3.485 -3.470 1.042 7,259 -3.339 -3.343 0.979 7,707 -3.622 -3.594 1.081
ψ 14,966 1.855 1.473 1.658 7,259 1.206 1.154 0.960 7,707 2.467 2.040 1.923
1-R2 14,966 0.791 0.814 0.158 7,259 0.735 0.760 0.152 7,707 0.844 0.885 0.145
AT (Value) 14,966 $3,634.68 $571.69 $13,760.20 7,259 $6,637.07 $1,572.99 $18,676.44 7,707 $806.82 $228.05 $4,762.66
SIZE 14,966 6.432 6.349 1.793 7,259 7.443 7.361 1.583 7,707 5.479 5.430 1.420
ANALYST 14,966 7.267 5.000 6.485 7,259 12.219 10.000 6.071 7,707 2.603 2.000 1.443
BIDASK 14,962 0.005 0.002 0.010 7,257 0.002 0.001 0.002 7,705 0.008 0.004 0.012
FIRM AGE (Years) 14,966 20.659 15.000 14.544 7,259 22.400 16.000 16.061 7,707 19.019 15.000 12.739
AGE 14,966 2.800 2.708 0.675 7,259 2.860 2.773 0.710 7,707 2.743 2.708 0.634
StdROE 14,966 0.924 0.056 52.873 7,259 0.339 0.045 5.699 7,707 1.475 0.070 73.469
StdCF 14,966 87.982 17.108 324.939 7,259 154.151 38.162 446.662 7,707 25.659 8.654 95.540
StdRET 14,966 0.033 0.029 0.017 7,259 0.028 0.025 0.013 7,707 0.037 0.033 0.019
MB 14,966 4.293 2.261 49.083 7,259 5.076 2.684 68.899 7,707 3.556 1.931 14.366
ROA 14,966 -0.009 0.041 0.218 7,259 0.032 0.055 0.143 7,707 -0.047 0.024 0.265
WC 14,966 9.955 0.229 509.067 7,259 8.716 0.199 390.749 7,707 11.122 0.258 599.546
DIV 14,966 0.365 0.000 0.482 7,259 0.422 0.000 0.494 7,707 0.312 0.000 0.463
MERGER 14,966 0.075 0.000 0.263 7,259 0.092 0.000 0.289 7,707 0.058 0.000 0.234
RESTRUCT 14,966 0.328 0.000 0.469 7,259 0.358 0.000 0.480 7,707 0.299 0.000 0.458
LOSS 14,966 0.291 0.000 0.454 7,259 0.191 0.000 0.393 7,707 0.385 0.000 0.487
LEV 14,966 0.454 0.454 0.213 7,259 0.477 0.486 0.203 7,707 0.433 0.422 0.220
AUDIT 14,966 0.257 0.000 0.437 7,259 0.310 0.000 0.462 7,707 0.208 0.000 0.406
EM 14,966 0.000 0.007 0.141 7,259 -0.004 0.005 0.126 7,707 0.004 0.009 0.153
FCF 14,966 0.039 0.075 0.201 7,259 0.076 0.088 0.126 7,707 0.005 0.060 0.246
DVS_GEO 12,208 0.574 0.523 0.560 6,118 0.634 0.619 0.566 6,090 0.514 0.406 0.548
DVS_BIZ 11,278 0.363 0.000 0.517 5,371 0.405 0.000 0.561 5,907 0.325 0.000 0.471
GINDEX 5,204 9.047 9.000 2.498 3,583 9.035 9.000 2.463 1,621 9.073 9.000 2.574
Note: All variables are defined in Table 4.1. The variables CAPEXt+1 (Value) and AT (Value) are stated in USD million. Firms with high (low) analyst following are defined as firms
above (below) the median value of the full sample on the number of analysts that follow the firms.
211
Table 5.13 Descriptive Statistics – Selling, General and Administrative Costs by Analyst Following Full Sample High Analyst Following Low Analyst Following
Variable N Mean Median Std Dev N Mean Median Std Dev N Mean Median Std Dev
SGAt+1 (Value) 13,780 $675.70 $121.72 $2,646.73 6,708 $1,226.16 $295.65 $3,663.01 7,072 $153.57 $56.97 $603.08
SGAt+1 13,780 -1.586 -1.442 0.961 6,708 -1.743 -1.564 0.983 7,072 -1.438 -1.317 0.916
ψ 13,780 1.808 1.451 1.545 6,708 1.192 1.147 0.930 7,072 2.392 1.985 1.770
1-R2 13,780 0.788 0.810 0.158 6,708 0.733 0.759 0.152 7,072 0.840 0.879 0.146
AT (Value) 13,780 $3,686.18 $593.86 $14,148.17 6,708 $6,739.18 $1,612.35 $19,207.03 7,072 $790.33 $239.94 $4,787.11
SIZE 13,780 6.468 6.387 1.769 6,708 7.466 7.385 1.563 7,072 5.522 5.480 1.394
ANALYST 13,780 7.304 5.000 6.543 6,708 12.261 10.000 6.154 7,072 2.601 2.000 1.441
BIDASK 13,777 0.005 0.002 0.010 6,707 0.002 0.001 0.002 7,070 0.008 0.004 0.013
FIRM AGE (Years) 13,780 20.959 15.000 14.676 6,708 22.545 16.000 16.158 7,072 19.455 15.000 12.939
AGE 13,780 2.814 2.708 0.676 6,708 2.866 2.773 0.711 7,072 2.765 2.708 0.637
StdROE 13,780 0.489 0.054 8.691 6,708 0.337 0.045 5.907 7,072 0.634 0.065 10.680
StdCF 13,780 89.851 17.474 333.856 6,708 157.740 38.495 458.730 7,072 25.457 8.818 95.273
StdRET 13,780 0.032 0.028 0.017 6,708 0.028 0.025 0.013 7,072 0.037 0.032 0.019
MB 13,780 4.133 2.228 50.784 6,708 5.062 2.652 71.555 7,072 3.251 1.891 12.941
ROA 13,780 0.006 0.044 0.184 6,708 0.041 0.056 0.125 7,072 -0.028 0.027 0.221
WC 13,780 0.707 0.224 13.857 6,708 0.439 0.201 3.284 7,072 0.961 0.247 19.073
DIV 13,780 0.373 0.000 0.484 6,708 0.424 0.000 0.494 7,072 0.323 0.000 0.468
MERGER 13,780 0.077 0.000 0.267 6,708 0.095 0.000 0.293 7,072 0.060 0.000 0.238
RESTRUCT 13,780 0.340 0.000 0.474 6,708 0.369 0.000 0.483 7,072 0.312 0.000 0.463
LOSS 13,780 0.271 0.000 0.445 6,708 0.177 0.000 0.382 7,072 0.360 0.000 0.480
LEV 13,780 0.454 0.453 0.211 6,708 0.475 0.484 0.202 7,072 0.435 0.423 0.217
AUDIT 13,780 0.258 0.000 0.438 6,708 0.312 0.000 0.463 7,072 0.208 0.000 0.406
EM 13,780 0.001 0.007 0.121 6,708 -0.002 0.005 0.092 7,072 0.005 0.009 0.143
EMP 13,780 0.007 0.004 0.019 6,708 0.006 0.003 0.015 7,072 0.008 0.004 0.022
DVS_GEO 11,543 0.589 0.544 0.561 5,762 0.649 0.638 0.566 5,781 0.528 0.438 0.550
DVS_BIZ 10,327 0.377 0.000 0.522 4,930 0.415 0.000 0.565 5,397 0.342 0.000 0.477
GINDEX 4,922 9.081 9.000 2.512 3,356 9.071 9.000 2.472 1,566 9.104 9.000 2.596
Note: All variables are defined in Table 4.1. The variables SGAt+1 (Value) and AT (Value) are stated in USD million. Firms with high (low) analyst following are
defined as firms above (below) the median value of the full sample on the number of analysts that follow the firms.
212
The data reported in Tables 5.11 to 5.13 further shows that firms with high analyst
following tend to invest more in the subsequent year‟s R&D costs, CAPEX and SGA
costs. High analyst coverage is closely related to large firm size as supported by the
Pearson correlations results in Tables 5.5 to 5.7 and this is consistent with the findings
documented by Bhushan (1989b). As predicted in item 4.5.4.1(b), firms with higher
analyst following are larger, hence are more financially sound to invest more in
corporate expenditure in the subsequent year.
It is also observed from Tables 5.11 to 5.13 that firms with low analyst following
possess higher idiosyncratic volatility. These findings provide empirical support to the
study by Chen et al. (2007), highlighting that private information is negatively related
with analyst following. Firms with high analyst following appear to exhibit higher cash
flow volatility, are more fast-growing and tend to be more profitable. These firms
possess higher free cash flow and tend to pay dividends. They are also more diversified
and are mostly audited by the specialist auditors.
5.2.5 Descriptive Statistics by Bid-ask Spreads
Two sub-groups are formed to analyse the characteristics of firms with high and low
bid-ask spreads based on the median value of average bid-ask spreads of the full sample.
Tables 5.14 to 5.16 provide the relevant descriptive statistics of all variables used in the
study by bid-ask spreads for the three corporate expenditures, namely, R&D expenditure,
CAPEX and SGA costs.
213
Table 5.14 Descriptive Statistics – R&D Expenditure by Bid-ask Spreads Full Sample Large Bid-ask Spreads Small Bid-ask Spreads
Variable N Mean Median Std Dev N Mean Median Std Dev N Mean Median Std Dev
R&D t+1 (Value) 8,510 $156.78 $24.40 $631.72 4,255 $33.60 $11.48 $140.71 4,255 $279.95 $54.59 $864.92
R&D t+1 8,510 -2.942 -2.749 1.383 4,255 -2.658 -2.457 1.410 4,255 -3.227 -3.034 1.293
Ψ 8,510 1.909 1.528 1.667 4,255 2.683 2.355 1.894 4,255 1.134 1.113 0.877
1-R2 8,510 0.797 0.822 0.158 4,255 0.866 0.913 0.133 4,255 0.727 0.753 0.149
AT (Value) 8,510 $3,650.16 $379.51 $15,484.09 4,255 $672.34 $124.22 $3,443.74 4,255 $6,627.98 $1,127.74 $21,212.54
SIZE 8,510 6.121 5.939 1.909 4,255 4.985 4.822 1.465 4,255 7.256 7.028 1.602
ANALYST 8,510 7.154 5.000 6.585 4,255 3.710 3.000 3.617 4,255 10.598 9.000 7.065
BIDASK 8,510 0.006 0.002 0.010 4,255 0.010 0.006 0.012 4,255 0.001 0.001 0.001
FIRM AGE (Years) 8,510 20.446 15.000 14.948 4,255 16.558 13.000 11.464 4,255 24.334 17.000 16.890
AGE 8,510 2.782 2.708 0.676 4,255 2.615 2.565 0.602 4,255 2.950 2.833 0.705
StdROE 8,510 0.769 0.074 9.996 4,255 1.026 0.115 9.910 4,255 0.511 0.051 10.076
StdCF 8,510 90.225 13.990 388.510 4,255 27.507 6.669 160.668 4,255 152.943 31.085 517.912
StdRET 8,510 0.034 0.031 0.019 4,255 0.042 0.038 0.023 4,255 0.027 0.025 0.011
MB 8,510 3.988 2.519 68.060 4,255 3.926 2.146 91.840 4,255 4.051 2.840 28.824
ROA 8,510 -0.061 0.032 0.352 4,255 -0.159 -0.025 0.458 4,255 0.036 0.057 0.139
WC 8,510 16.698 0.378 675.884 4,255 19.897 0.433 809.037 4,255 13.500 0.332 509.101
DIV 8,510 0.296 0.000 0.456 4,255 0.185 0.000 0.389 4,255 0.406 0.000 0.491
MERGER 8,510 0.081 0.000 0.273 4,255 0.053 0.000 0.223 4,255 0.110 0.000 0.312
RESTRUCT 8,510 0.408 0.000 0.491 4,255 0.350 0.000 0.477 4,255 0.466 0.000 0.499
LOSS 8,510 0.374 0.000 0.484 4,255 0.559 1.000 0.497 4,255 0.188 0.000 0.391
LEV 8,510 0.442 0.404 0.314 4,255 0.435 0.361 0.374 4,255 0.449 0.443 0.239
AUDIT 8,510 0.248 0.000 0.432 4,255 0.201 0.000 0.401 4,255 0.295 0.000 0.456
EM 8,510 -0.006 0.007 0.191 4,255 -0.009 0.009 0.248 4,255 -0.004 0.004 0.109
DVS_GEO 7,191 0.750 0.707 0.557 3,404 0.667 0.654 0.569 3,787 0.824 0.834 0.536
DVS_BIZ 6,468 0.379 0.000 0.554 3,213 0.271 0.000 0.457 3,255 0.486 0.078 0.616
GINDEX 3,030 9.136 9.000 2.496 818 8.853 9.000 2.466 2,212 9.240 9.000 2.500
Note: All variables are defined in Table 4.1. The variables R&D t+1 (Value) and AT (Value) are stated in USD million. Firms with high (low) bid-ask spreads are
defined as firms above (below) the median value of the average bid-ask spreads of the full sample.
214
Table 5.15 Descriptive Statistics – Capital Expenditure by Bid-ask Spreads Full Sample Large Bid-ask Spreads Small Bid-ask Spreads
Variable N Mean Median Std Dev N Mean Median Std Dev N Mean Median Std Dev
CAPEXt+1 (Value) 14,962 $223.10 $20.67 $1,139.03 7,481 $53.88 $6.13 $284.36 7,481 $392.32 $58.56 $1,567.43
CAPEXt+1 14,962 -3.485 -3.470 1.042 7,481 -3.631 -3.607 1.112 7,481 -3.338 -3.348 0.945
ψ 14,962 1.855 1.473 1.658 7,481 2.575 2.214 1.920 7,481 1.136 1.125 0.881
1-R2 14,962 0.791 0.814 0.158 7,481 0.854 0.902 0.140 7,481 0.728 0.755 0.149
AT (Value) 14,962 $3,634.85 $571.45 $13,761.99 7,481 $958.94 $200.72 $3,808.75 7,481 $6,310.76 $1,428.93 $18,707.77
SIZE 14,962 6.431 6.348 1.793 7,481 5.445 5.302 1.495 7,481 7.417 7.265 1.501
ANALYST 14,962 7.267 5.000 6.485 7,481 4.047 3.000 4.032 7,481 10.488 9.000 6.863
BIDASK 14,962 0.005 0.002 0.010 7,481 0.009 0.005 0.012 7,481 0.001 0.001 0.000
FIRM AGE (Years) 14,962 20.654 15.000 14.539 7,481 17.492 13.000 12.043 7,481 23.815 17.000 16.056
AGE 14,962 2.799 2.708 0.675 7,481 2.658 2.565 0.628 7,481 2.941 2.833 0.690
StdROE 14,962 0.924 0.056 52.880 7,481 1.578 0.080 74.582 7,481 0.270 0.042 5.438
StdCF 14,962 87.989 17.106 324.981 7,481 31.874 8.214 139.073 7,481 144.104 35.456 430.813
StdRET 14,962 0.033 0.029 0.017 7,481 0.039 0.035 0.020 7,481 0.026 0.024 0.011
MB 14,962 4.293 2.261 49.090 7,481 4.326 1.953 65.736 7,481 4.261 2.594 22.331
ROA 14,962 -0.008 0.041 0.218 7,481 -0.068 0.014 0.277 7,481 0.051 0.059 0.107
WC 14,962 9.958 0.230 509.135 7,481 11.981 0.262 608.871 7,481 7.934 0.199 384.369
DIV 14,962 0.365 0.000 0.482 7,481 0.262 0.000 0.440 7,481 0.469 0.000 0.499
MERGER 14,962 0.075 0.000 0.263 7,481 0.053 0.000 0.223 7,481 0.097 0.000 0.296
RESTRUCT 14,962 0.328 0.000 0.469 7,481 0.293 0.000 0.455 7,481 0.362 0.000 0.481
LOSS 14,962 0.291 0.000 0.454 7,481 0.439 0.000 0.496 7,481 0.143 0.000 0.350
LEV 14,962 0.454 0.454 0.213 7,481 0.437 0.422 0.223 7,481 0.472 0.482 0.201
AUDIT 14,962 0.257 0.000 0.437 7,481 0.213 0.000 0.410 7,481 0.302 0.000 0.459
EM 14,962 0.000 0.007 0.141 7,481 -0.001 0.009 0.176 7,481 0.001 0.006 0.093
FCF 14,962 0.039 0.075 0.201 7,481 -0.010 0.053 0.258 7,481 0.089 0.090 0.097
DVS_GEO 12,204 0.574 0.523 0.560 5,856 0.510 0.388 0.555 6,348 0.633 0.615 0.558
DVS_BIZ 11,274 0.363 0.000 0.517 5,698 0.292 0.000 0.455 5,576 0.436 0.072 0.564
GINDEX 5,200 9.046 9.000 2.498 1,467 8.839 9.000 2.498 3,733 9.128 9.000 2.494
Note: All variables are defined in Table 4.1. The variables CAPEXt+1 (Value) and AT (Value) are stated in USD million. Firms with high (low) bid-ask spreads are
defined as firms above (below) the median value of the average bid-ask spreads of the full sample
215
Table 5.16 Descriptive Statistics – Selling, General and Administrative Costs by Bid-ask Spreads Full Sample Large Bid-ask Spreads Small Bid-ask Spreads
Variable N Mean Median Std Dev N Mean Median Std Dev N Mean Median Std Dev
SGAt+1 (Value) 13,777 $675.82 $121.73 $2,647.01 6,889 $185.46 $53.84 $640.96 6,888 $1,166.25 $278.97 $3,622.61
SGAt+1 13,777 -1.586 -1.441 0.961 6,889 -1.444 -1.287 0.968 6,888 -1.728 -1.577 0.933
ψ 13,777 1.808 1.451 1.545 6,889 2.488 2.152 1.767 6,888 1.127 1.120 0.852
1-R2 13,777 0.788 0.810 0.158 6,889 0.850 0.896 0.142 6,888 0.726 0.754 0.149
AT (Value) 13,777 $3,686.55 $593.83 $14,149.67 6,889 $1,011.27 $212.53 $3,920.75 6,888 $6,362.21 $1,442.24 $19,255.92
SIZE 13,777 6.468 6.387 1.770 6,889 5.514 5.359 1.492 6,888 7.422 7.274 1.488
ANALYST 13,777 7.305 5.000 6.544 6,889 4.096 3.000 4.127 6,888 10.514 9.000 6.930
BIDASK 13,777 0.005 0.002 0.010 6,889 0.009 0.005 0.013 6,888 0.001 0.001 0.000
FIRM AGE (Years) 13,777 20.956 15.000 14.673 6,889 17.957 14.000 12.300 6,888 23.956 18.000 16.167
AGE 13,777 2.814 2.708 0.676 6,889 2.683 2.639 0.633 6,888 2.945 2.890 0.692
StdROE 13,777 0.489 0.054 8.692 6,889 0.704 0.074 10.909 6,888 0.274 0.042 5.659
StdCF 13,777 89.861 17.460 333.891 6,889 33.678 8.464 149.297 6,888 146.053 35.493 440.898
StdRET 13,777 0.032 0.028 0.017 6,889 0.039 0.035 0.019 6,888 0.026 0.024 0.011
MB 13,777 4.133 2.229 50.790 6,889 4.003 1.900 68.045 6,888 4.264 2.589 23.001
ROA 13,777 0.006 0.044 0.184 6,889 -0.044 0.019 0.230 6,888 0.056 0.060 0.099
WC 13,777 0.707 0.224 13.858 6,889 0.732 0.249 6.947 6,888 0.683 0.203 18.327
DIV 13,777 0.373 0.000 0.483 6,889 0.275 0.000 0.447 6,888 0.470 0.000 0.499
MERGER 13,777 0.077 0.000 0.267 6,889 0.056 0.000 0.230 6,888 0.098 0.000 0.298
RESTRUCT 13,777 0.340 0.000 0.474 6,889 0.308 0.000 0.462 6,888 0.371 0.000 0.483
LOSS 13,777 0.271 0.000 0.445 6,889 0.409 0.000 0.492 6,888 0.133 0.000 0.340
LEV 13,777 0.454 0.453 0.211 6,889 0.439 0.423 0.221 6,888 0.470 0.480 0.199
AUDIT 13,777 0.258 0.000 0.438 6,889 0.212 0.000 0.409 6,888 0.304 0.000 0.460
EM 13,777 0.001 0.007 0.121 6,889 0.001 0.008 0.147 6,888 0.001 0.006 0.088
EMP 13,777 0.007 0.004 0.019 6,889 0.007 0.004 0.020 6,888 0.006 0.003 0.018
DVS_GEO 11,540 0.589 0.544 0.561 5,616 0.527 0.425 0.557 5,924 0.647 0.631 0.559
DVS_BIZ 10,324 0.377 0.000 0.522 5,227 0.314 0.000 0.467 5,097 0.441 0.083 0.566
GINDEX 4,919 9.082 9.000 2.512 1,447 8.878 9.000 2.515 3,472 9.166 9.000 2.506
Note: All variables are defined in Table 4.1. The variables SGAt+1 (Value) and AT (Value) are stated in USD million. Firms with high (low) bid-ask spreads are
defined as firms above (below) the median value of the average bid-ask spreads of the full sample.
216
Analysed data summarized in Tables 5.14 to 5.16 reveal that firms with large bid-ask
spreads on average spend less in corporate expenditure of the subsequent year, namely,
R&D expenditure, CAPEX and SGA costs. Nevertheless, idiosyncratic volatility is
found to be higher in firms with large bid-ask spreads for all three corporate
expenditures. These findings is consistent with those reported by Chan et al. (2013) that
idiosyncratic volatility is positively associated with bid-ask spreads.
Bid-ask spreads are larger in small-sized firms and this finding is consistent with those
reported in Stoll (2000). Large bid-ask spreads are also observed in firms with relatively
lower analyst coverage. In terms of firm characteristics, firms with large bid-ask spreads
appear to be younger and they possess lower cash flow volatility but higher working
capital ratio (representing immediate slack resources). These firms mostly incur losses
and they are less diversified. Further, they have a lower tendency to pay dividends and
undertake fewer merger and restructuring activities.
5.3 Hypothesis 1 - Stock Price Informativeness and Corporate Expenditure
Drawing from the learning theory, Hypothesis 1 of the current study is developed to
examine the association between a current year‟s stock price informativeness and
corporate expenditure in the subsequent year. The results of multivariate and robustness
tests of the main model employed in this study are presented in items 5.3.1 and 5.3.2
respectively.
217
5.3.1 Multivariate Tests
Hypothesis 1 is empirically tested using a regression analysis which is presented in
Equation 5.1:
∑ ∑ ∑
(5.1)
where:
is the corporate expenditure for firm i of the subsequent year ( ). As
mentioned earlier, the three proxies of corporate expenditure employed in this study are
R&D costs, CAPEX and SGA costs. The coefficient is the intercept while coefficient
is the coefficient of interest. The independent variable represents idiosyncratic
volatility of the current year and denotes a set of control variables in the
same year. Year and Industry dummies are also included in the model while
represents unspecified random factors.
Hypothesis 1 of the study postulates that a current year‟s idiosyncratic volatility is
negatively associated with the subsequent year‟s corporate expenditure, thus the
coefficient of interest, is expected to be negative. Tables 5.17 to 5.19 present the
regression results of multivariate tests of the main model that examines the association
between a current year‟s idiosyncratic volatility and three proxies of corporate
expenditure in the subsequent year, namely, R&D expenditure, CAPEX and SGA costs.
The reported t-statistics are adjusted using standard errors corrected for
heteroskedasticity by applying the White (1980) variance correction method. Besides,
218
statistical tests are conducted using the two-way cluster-robust standard errors, i.e., by
both firm and year based on the Petersen (2009) procedure to account for cross-sectional
and time-series correlation. Table 5.17 reports regression results of eight models when
corporate expenditure is represented by R&D costs. The eight models presented are as
follows:
a) Models 1a and 1b. This is the initial sample obtained using the sample selection
procedure outlined in item 4.7.
b) Models 2a and 2b when variable CAPEX of the subsequent year is added as a
control variable.
c) Models 3a and 3b when two diversification variables, DVS_GEO and DVS_BIZ
are included as control variables.
d) Models 4a and 4b when GINDEX is included to control for firms‟ corporate
governance.
219
Table 5.17 Effect of Stock Price Informativeness on R&D Expenditure (H1)
∑ ∑ ∑
(Model 1a) (Model 1b) Expected
White-adjusted
Petersen Clustering
Direction Coefficient t-statistic Coefficient t-statistic
Intercept 0.270 0.82 0.408 0.82
ψ ?
-0.064 -6.28 ***
-0.049 -2.94 **
SIZE +ve
-0.510 -10.41 ***
-0.498 -5.14 ***
SIZE2 ?
0.012 3.25 ***
0.011 1.49
ANALYST +ve
0.072 27.19 ***
0.072 13.83 ***
AGE +ve
-0.063 -2.74 ***
-0.064 -1.57
StdROE +ve
0.003 4.77 ***
0.003 4.74 ***
StdCF +ve
0.000 0.29
0.000 .
StdRET ?
-0.937 -1.00
-2.983 -2.08 *
MB +ve
0.000 1.01
0.000 0.97
ROA -ve
-0.582 -6.64 ***
-0.595 -4.04 ***
WC -ve
0.000 1.49
0.000 .
DIV -ve
-0.467 -14.81 ***
-0.477 -6.73 ***
MERGER ?
0.057 1.39
0.042 0.87
RESTRUCT +ve
0.136 5.41 ***
0.128 3.23 **
LOSS +ve
0.481 13.65 ***
0.484 9.82 ***
LEV -ve
-0.343 -6.24 ***
-0.336 -3.91 ***
AUDIT +ve
0.054 2.11 **
0.052 1.25
EM -ve
-0.022 -0.35
-0.028 -0.48
Year dummies
Included
Included
Industry dummies
Included
Included
Adjusted R2
0.445
0.441
F-Statistics
214.05
226.06
p-value
<0.0001
<0.0001
N 8,513
8,513
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less than 1%,
5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the procedure of White
(1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
220
Table 5.17 Effect of Stock Price Informativeness on R&D Expenditure (H1)
(Continued)
∑
∑
∑
(Model 2a)
(Model 2b)
Expected White-adjusted
Petersen Clustering
Direction Coefficient t-statistic Coefficient t-statistic
Intercept 0.081 0.24 0.259 0.51
ψ ? -0.065 -6.32 ***
-0.050 -3.01 **
SIZE +ve -0.510 -10.35 ***
-0.499 -5.27 ***
SIZE2 ? 0.012 3.27 **
0.011 1.53
ANALYST +ve 0.071 27.15 ***
0.071 13.95 ***
AGE +ve -0.064 -2.77 *
-0.066 -1.59
StdROE +ve 0.003 4.71 ***
0.003 4.61 ***
StdCF +ve 0.000 0.34
0.000 .
StdRET ? -1.292 -1.24
-3.629 -2.07 *
MB +ve 0.000 1.02
0.000 0.99
ROA -ve -0.579 -6.30 ***
-0.596 -3.82 ***
WC -ve 0.000 1.39
0.000 .
DIV -ve -0.461 -14.58 ***
-0.472 -6.77 ***
MERGER ? 0.047 1.13
0.032 0.64
RESTRUCT +ve 0.134 5.31 ***
0.126 3.18 **
LOSS +ve 0.471 13.01 ***
0.477 9.81 ***
LEV -ve -0.333 -6.01 ***
-0.326 -3.78 ***
AUDIT +ve 0.054 2.11 **
0.052 1.26
EM -ve -0.030 -0.44
-0.028 -0.43
CAPEXt+1 -ve -0.051 -3.52 ***
-0.046 -1.64
Year dummies
Included
Included
Industry dummies
Included
Included
Adjusted R2
0.444
0.441
F-Statistics
206.44
217.74
p-value
<0.0001
<0.0001
N 8,483 8,483
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less than 1%,
5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the procedure of White
(1980) and (b) clustering by firm and year using the Petersen (2009) procedure
221
Table 5.17 Effect of Stock Price Informativeness on R&D Expenditure (H1)
(Continued)
∑ ∑ ∑
(Model 3a)
(Model 3b)
Expected White-adjusted
Petersen Clustering
Direction Coefficient t-statistic Coefficient t-statistic
Intercept 0.282 0.99
0.459 1.29
ψ ? -0.069 -5.32 ***
-0.054 -2.63 **
SIZE +ve -0.617 -9.79 ***
-0.606 -6.52 ***
SIZE2 ? 0.024 5.35 ***
0.023 3.34 **
ANALYST +ve 0.060 19.30 ***
0.060 11.25 ***
AGE +ve 0.020 0.71
0.014 0.29
StdROE +ve 0.002 5.14 ***
0.002 4.65 ***
StdCF +ve 0.000 2.01 **
0.000 .
StdRET ? -0.983 -0.56
-3.284 -2.19 *
MB +ve 0.000 1.10
0.000 .
ROA -ve -0.596 -6.95 ***
-0.609 -5.30 ***
WC -ve 0.001 1.28
0.002 1.11
DIV -ve -0.392 -10.78 ***
-0.400 -5.05 ***
MERGER ? 0.066 1.42
0.054 1.20
RESTRUCT +ve 0.128 4.22
0.122 2.53 **
LOSS +ve 0.323 8.22 ***
0.327 6.96 ***
LEV -ve -0.469 -5.82 ***
-0.464 -4.20 ***
AUDIT +ve 0.031 0.98
0.030 0.61
EM -ve -0.073 -0.55
-0.078 -0.41
CAPEXt+1 -ve -0.038 -1.84 *
-0.032 -0.95
DVS_GEO -ve 0.224 8.07 ***
0.227 4.72 **
DVS_BIZ -ve -0.218 -7.28 ***
-0.218 -4.34 **
Year dummies
Included
Included
Industry dummies
Included
Included
Adjusted R2
0.403
0.401
F-Statistics
102.36
109.66
p-value
<0.0001
<0.0001
N 5,247
5,247
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less than 1%,
5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the procedure of White
(1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
222
Table 5.17 Effect of Stock Price Informativeness on R&D Expenditure (H1)
(Continued)
∑ ∑ ∑
(Model 4a)
(Model 4b)
Expected White-adjusted
Petersen Clustering
Direction Coefficient t-statistic Coefficient t-statistic
Intercept 2.434 5.04 ***
2.333 3.10 **
ψ ? -0.026 -0.99
-0.024 -0.86
SIZE +ve -0.965 -8.79 ***
-0.969 -5.84 ***
SIZE2 ? 0.040 5.78 ***
0.040 3.79 **
ANALYST +ve 0.070 18.80 ***
0.069 10.58 ***
AGE +ve 0.031 0.85
0.037 0.60
StdROE +ve 0.002 5.12 ***
0.002 4.68 ***
StdCF +ve 0.000 -1.55
0.000 .
StdRET ? -0.158 -0.05
-2.171 -0.42
MB +ve 0.000 1.03
0.000 .
ROA -ve -0.869 -4.12 ***
-0.860 -3.96 **
WC -ve 0.000 -0.28
0.000 -0.19
DIV -ve -0.420 -8.87 ***
-0.437 -5.07 ***
MERGER ? 0.139 2.31 **
0.155 2.76 *
RESTRUCT +ve 0.121 3.09 ***
0.126 2.03
LOSS +ve 0.192 3.10 ***
0.189 2.15 *
LEV -ve -0.489 -5.22 ***
-0.483 -3.52 **
AUDIT +ve 0.022 0.57
0.016 0.28
EM -ve 0.059 0.25
-0.016 -0.07
CAPEXt+1 -ve -0.105 -4.04 ***
-0.102 -2.27 *
GINDEX -ve -0.016 -2.07 **
-0.017 -1.28
Year dummies
Included
Included
Industry dummies
Included
Included
Adjusted R2
0.406
0.403
F-Statistics
65.78
81.03
p-value
<0.0001
<0.0001
N 3,033
3,033
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less than
1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the procedure of
White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
223
Table 5.17 highlights that the coefficients for idiosyncratic volatility, ψ are negative at p
< 0.01 for Model 1a using the White (1980) procedure and at p < 0.05 for Model 1b
using the Petersen clustering procedure. These findings are in line with the Hypothesis 1,
indicating that the level of R&D expenditure in the subsequent year is high when stock
price informativeness is at low level during the current year, and vice versa.
To reflect the resource constraint situation faced by most firms, variable CAPEXt+1
(capital expenditure level in the subsequent year) is added in as a control variable in
Models 2a and 2b. As R&D expenditure forms part of SGA costs, the variable of SGAt+1
(SGA costs of the subsequent year) is not included in the model. As predicted in item
4.5.4.1(o), CAPEX level in the subsequent year is negatively associated with R&D
expenditure level in the subsequent year, as shown in Models 2a and 2b of Table 5.17.
Firms investing more in tangible assets (CAPEX) are more likely to spend less in
intangibles like R&D costs (Banker et al., 2011b). The coefficients of idiosyncratic
volatility, ψ remains both significantly negative at p < 0.01 using White-adjusted
procedure (Model 2a) and at p < 0.05 using Petersen clustering procedures (Model 2b)
at a lower sample size of 8,483 firm-year observations, supporting Hypothesis 1 of the
study.
Models 3a and 3b displayed in Table 5.17 include two variables of diversification,
namely, diversification in different geographic areas (DVS_GEO) and diversification in
different business lines (DIV_BIZ). The results are qualitatively similar to Models 1a
and 1b as well as Models 2a and 2b reported in Table 5.17 and this rules out the
224
possibility that the negative relationship between idiosyncratic volatility and R&D
investments are driven by a firm‟s diversification strategy. As expected in item
4.5.4.1(p), diversification in different business lines is negatively related to subsequent
year‟s R&D investment, because highly diversified firms emphasize in achieving short-
term financial performance targets, thus avoiding risky R&D projects. Contrary to the
prediction in item 4.5.4.1(p), geographic diversification is positively associated to R&D
expenditure. This could be due to geographically-diversified firms focusing on both
financial and strategic controls and practising openness in performance evaluation,
hence encouraging risk-taking behaviour in R&D projects.
When variable GINDEX is included in Models 4a and 4b as a control variable
representing corporate governance, the sample size is reduced to 3,033 firm-year
observations due to its missing values. GINDEX is negatively associated with R&D
expenditure as well-governed firms are expected to invest more in R&D to reap its
benefits (refer item 4.5.4.2(c)). However, the significance level for idiosyncratic
volatility, ψ disappears for both Models 4a and 4b that use White-adjusted and Petersen
clustering procedure respectively, possibly caused by a smaller sample size.
Consistent with prediction made in item 4.5.4.1, analyst following (ANALYST),
volatility of return on equity (StdROE), restructuring activities (RESTRUCT) and loss
dummy (LOSS) are positively related to subsequent year‟s R&D expenditure level,
while return on equity (ROA), dividend dummy (DIV) and leverage (LEV) are
negatively associated to R&D investment in the subsequent year. On the other hand, the
225
results show that young firms tend to invest more in R&D initiatives, against the earlier
prediction made in item 4.5.4.1(c) that older firms are normally larger and financially
stronger to invest in R&D. The negative coefficient of variable, firm size (SIZE) and the
positive coefficient of variable, squared value of firm size (SIZE2) imply a non-linear
relationship between firm size and R&D investment. When idiosyncratic volatility
increases from low levels, small firms tend to invest more in R&D projects, thereby
highlighting a negative association between idiosyncratic volatility and R&D
expenditure in the subsequent year. When idiosyncratic volatility reaches a certain
threshold value, the association between firm size and R&D costs changes to positive
indicating large firms are investing more in R&D activities compared to small firms.
Table 5.18 demonstrates the effect of current year‟s stock price informativeness on
subsequent year‟s capital expenditure. It reports regression results of eight models as
follows:
a) Models 5a and 5b using the initial sample obtained from the sample selection
procedure outlined in item 4.7.
b) Models 6a and 6b when variable SGA costs of the subsequent year is included as a
control variable.
c) Models 7a and 7b when two diversification variables, DVS_GEO and DVS_BIZ
are added as control variables.
d) Models 8a and 8b when GINDEX is included as a control variable of corporate
governance.
226
Table 5.18 Effect of Stock Price Informativeness on CAPEX (H1)
∑ ∑ ∑
(Model 5a)
(Model 5b) Expected
White-adjusted
Petersen Clustering
Direction Coefficient t-statistic Coefficient t-statistic
Intercept -3.523 -20.32 *** -3.347 -13.51 ***
ψ ?
-0.018 -2.46 **
0.003 0.26
SIZE +ve
0.033 0.88
0.040 0.69
SIZE2 ?
-0.004 -1.75 *
-0.005 -1.39
ANALYST +ve
0.011 7.05 ***
0.012 4.26 ***
AGE +ve
-0.012 -0.86
-0.019 -0.80
StdROE ?
0.000 4.73 ***
0.000 .
StdCF ?
0.000 3.98 ***
0.000 .
StdRET ?
-2.441 -3.56 ***
-6.448 -5.82 ***
MB +ve
0.000 0.67
0.000 .
ROA +ve
-0.658 -5.32 ***
-0.562 -1.45
WC -ve
0.000 0.03
0.000 .
DIV -ve
0.115 6.20 ***
0.100 2.68 ***
MERGER -ve
-0.208 -8.21 ***
-0.227 -8.13 ***
RESTRUCT -ve
-0.164 -9.94 ***
-0.162 -7.18 ***
LOSS -ve
-0.179 -5.76 ***
-0.168 -6.54 ***
LEV -ve
0.304 6.67 ***
0.317 4.11 ***
AUDIT +ve
0.042 2.66 ***
0.046 1.90
EM -ve
-0.253 -2.29 **
-0.274 -4.01 ***
FCF +ve
1.467 9.72 ***
1.345 3.44 ***
Year dummies
Included
Included
Industry dummies
Included
Included
Adjusted R2
0.248
0.239
F-Statistics
150.18
156.27
p-value
<0.0001
<0.0001
N 14,966 14,966
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less than 1%,
5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the procedure of White
(1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
227
Table 5.18 Effect of Stock Price Informativeness on CAPEX (H1) (Continued)
∑ ∑ ∑
(Model 6a)
(Model 6b)
Expected White-adjusted
Petersen Clustering
Direction Coefficient t-statistic Coefficient t-statistic
Intercept -3.263 -19.64 *** -3.076 -14.22 ***
ψ ? -0.023 -3.02 ***
-0.003 -0.25
SIZE +ve -0.055 -1.57
-0.048 -1.03
SIZE2 ? 0.000 0.21
0.000 -0.11
ANALYST +ve 0.013 7.88 ***
0.013 4.39 ***
AGE +ve -0.025 -1.83 *
-0.033 -1.28
StdROE ? 0.001 0.83
0.001 1.29
StdCF ? 0.000 3.78 ***
0.000 .
StdRET ? -2.055 -2.78 ***
-6.237 -5.54 **
MB +ve 0.000 0.88
0.000 .
ROA +ve -0.540 -4.30 ***
-0.447 -1.12
WC -ve 0.000 0.27
0.000 0.29
DIV -ve 0.117 6.30 ***
0.102 2.59 **
MERGER -ve -0.220 -8.61 ***
-0.236 -9.42 ***
RESTRUCT -ve -0.162 -9.74 ***
-0.160 -7.35 ***
LOSS -ve -0.111 -4.05 ***
-0.101 -2.84 **
LEV -ve 0.321 6.97 ***
0.342 5.13 ***
AUDIT +ve 0.055 3.42 ***
0.057 2.31 *
EM -ve -0.346 -3.51 ***
-0.375 -3.50 **
FCF +ve 1.539 10.99 ***
1.413 3.25 **
SGAt+1 -ve -0.042 -3.49 ***
-0.039 -1.63
Year dummies
Included
Included
Industry dummies
Included
Included
Adjusted R2
0.237
0.228
F-Statistics
128.40
137.35
p-value
<0.0001
<0.0001
N 13,971 13,971
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less than
1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the procedure of
White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
228
Table 5.18 Effect of Stock Price Informativeness on CAPEX (H1) (Continued)
∑ ∑ ∑
(Model 7a)
(Model 7b)
Expected White-adjusted
Petersen Clustering
Direction Coefficient t-statistic Coefficient t-statistic
Intercept -3.157 -15.38 ***
-2.972 -10.90 ***
ψ ? -0.037 -4.19 ***
-0.016 -1.03
SIZE +ve -0.055 -1.21
-0.046 -0.73
SIZE2 ? 0.001 0.42
0.000 0.03
ANALYST +ve 0.009 4.12 ***
0.010 2.84 **
AGE +ve -0.013 -0.72
-0.023 -0.85
StdROE ? 0.001 0.65
0.001 0.78
StdCF ? 0.000 2.17 **
0.000 .
StdRET ? -2.186 -2.32 **
-6.460 -6.62 ***
MB +ve 0.000 1.18
0.000 .
ROA +ve -0.623 -3.70 ***
-0.523 -1.39
WC -ve 0.001 0.49
0.001 0.36
DIV -ve 0.113 5.01 ***
0.099 2.30 *
MERGER -ve -0.193 -6.26 ***
-0.211 -6.13 ***
RESTRUCT -ve -0.120 -5.95 ***
-0.118 -5.66 ***
LOSS -ve -0.071 -1.93 *
-0.065 -1.36
LEV -ve 0.302 5.17 ***
0.328 4.02 ***
AUDIT +ve 0.077 3.96 ***
0.084 2.96 **
EM -ve -0.173 -1.45
-0.207 -1.98 *
FCF +ve 1.593 8.33 ***
1.440 3.02 **
SGAt+1 -ve -0.017 -1.15
-0.015 -0.58
DVS_GEO ? -0.056 -3.05 ***
-0.052 -1.60
DVS_BIZ ? -0.029 -1.65 *
-0.023 -0.80
Year dummies
Included
Included
Industry dummies
Included
Included
Adjusted R2
0.226
0.216
F-Statistics
69.49
66.26
p-value
<0.0001
<0.0001
N 8,455 8,455
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less than
1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the procedure of
White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
229
Table 5.18 Effect of Stock Price Informativeness on CAPEX (H1) (Continued)
∑ ∑ ∑
(Model 8a)
(Model 8b)
Expected White-adjusted
Petersen Clustering
Direction Coefficient t-statistic Coefficient t-statistic
Intercept -2.647 -7.78 ***
-2.728 -5.83 ***
ψ ? -0.018 -1.34
-0.017 -0.71
SIZE +ve -0.254 -3.44 ***
-0.243 -2.51 *
SIZE2 ? 0.012 2.66 ***
0.012 1.92
ANALYST +ve 0.013 5.65 ***
0.012 4.02 **
AGE +ve 0.007 0.34
0.010 0.25
StdROE ? -0.001 -1.51
-0.001 -2.11
StdCF ? 0.000 2.84 ***
0.000 .
StdRET ? 2.423 1.42
1.373 0.48
MB +ve 0.000 0.69
0.000 .
ROA +ve -0.588 -1.77 *
-0.528 -1.89
WC -ve -0.202 -5.80 ***
-0.205 -3.45 **
DIV -ve 0.130 4.81 ***
0.123 3.04 **
MERGER -ve -0.206 -5.20 ***
-0.196 -6.00 ***
RESTRUCT -ve -0.130 -5.44 ***
-0.127 -3.97 **
LOSS -ve -0.035 -0.72
-0.032 -0.94
LEV -ve 0.137 1.87 *
0.133 1.42
AUDIT +ve 0.067 3.13 ***
0.063 2.23
EM -ve -0.276 -1.43
-0.320 -2.80 **
FCF +ve 2.916 7.04 ***
2.859 7.90 ***
SGAt+1 -ve -0.048 -2.85 ***
-0.045 -1.53
GINDEX -ve 0.008 1.91 *
0.008 1.03
Year dummies
Included
Included
Industry dummies
Included
Included
Adjusted R2
0.290
0.288
F-Statistics
62.41
61.85
p-value
<0.0001
<0.0001
N 4,960 4,960
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less than
1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the procedure of
White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
230
It is observed from Model 5a in Table 5.18 that idiosyncratic volatility is negatively
associated with subsequent year‟s capital expenditure at p <0.05 when White (1980)
procedure is applied to the regression of the main model. These findings indicate that
Hypothesis 1 is supported when corporate expenditure is represented by CAPEX. The
significance level for coefficient of idiosyncratic volatility, ψ increases to p < 0.01 when
variable SGAt+1 (SGA of the subsequent year) is added as a control variable in Model 6a.
The negative relationship between idiosyncratic volatility and CAPEX of the subsequent
year remains significant when two diversification variables in the form of geographical
diversification (DVS_GEO) and business lines diversification (DVS_BIZ) are added in
as additional control variables in Model 7a. This shows that at low level of idiosyncratic
volatility, the investment level in capital projects is higher, and vice versa, providing
support to Hypothesis 1.
Nevertheless, when Petersen‟s (2009) procedure is employed to control for cross-
sectional and time-series correlation, Model 5b shows insignificant positive results for
coefficient idiosyncratic volatility, ψ. Further, variables SGAt+1 (SGA in the subsequent
year) are added in Model 6b while two diversification variables, namely, DVS_GEO
(geographic diversification) and DVS_BIZ (business diversification) are added in Model
7b as additional control variables. Both Models 6b and 7b show insignificant negative
relationship between idiosyncratic volatility and CAPEX in the subsequent year. This
indicates that idiosyncratic volatility does not play a fundamental role in determining
CAPEX level of the subsequent year. Both Models 8a and 8b also demonstrate
231
insignificant negative relationships between idiosyncratic volatility and CAPEX of the
subsequent year when GINDEX is included by using White (1980) and Petersen (2009)
procedures respectively, probably due to a smaller sample size of 4,960.
The control variable analyst following is positively associated to CAPEX confirming the
expectation in item 4.5.4.1(b) that firms with higher analyst following invest more in
CAPEX. Also, within prediction made in item 4.5.4.1(r), free cash flow is found to be
positively associated with CAPEX, suggesting that firms with free cash flow invest
wastefully on capital projects for empire building (Jensen, 1986). As expected in item
4.5.4.1, CAPEX level is lower when firms undertake merger and restructuring activities,
make losses and when SGA costs of the subsequent year increased. Better audit quality
and lower earnings management are also shown to be positively associated with higher
CAPEX level, in line with predictions made in item 4.5.4.2. Higher stock return
volatility is negatively related to CAPEX indicating investors‟ reluctance in investing
capital projects due to uncertain information environment in evaluating managers‟
actions (Gillan et al., 2003). Contrary to predictions made in both items 4.5.4.1(j) and
4.5.4.1(n), firms that are more likely to pay dividends and those highly-leveraged firms
incur more CAPEX.
232
Table 5.19 presents the regression results of Hypothesis 1 for SGA costs using the
following eight models:
a) Models 9a and 9b using the initial sample derived from the sample selection
procedure outlined in item 4.7.
b) Models 10a and 10b when variable CAPEX of the subsequent year is controlled.
c) Models 11a and 11b when two diversification variables, DVS_GEO and
DVS_BIZ are added as control variables.
d) Models 12a and 12b when corporate governance variable, GINDEX is included as
a control variable.
233
Table 5.19 Effect of Stock Price Informativeness on SGA Costs (H1)
∑ ∑ ∑
(Model 9a)
(Model 9b) Expected
White-adjusted
Petersen Clustering
Direction Coefficient t-statistic Coefficient t-statistic
Intercept 0.346 2.39 ** 0.412 1.57
ψ ?
-0.029 -5.45 ***
-0.028 -2.40 *
SIZE +ve
-0.603 -25.34 ***
-0.604 -15.19 ***
SIZE2 ?
0.023 13.25 ***
0.023 7.59 ***
ANALYST +ve
0.025 18.22 ***
0.024 6.29 ***
AGE +ve
0.123 10.58 ***
0.122 5.06 ***
StdROE ?
0.001 3.69 ***
0.001 3.37 **
StdCF ?
0.000 -1.02
0.000 .
StdRET ?
-2.992 -5.94 ***
-3.533 -5.08 ***
MB +ve
0.000 0.78
0.000 0.69
ROA -ve
-0.311 -6.95 ***
-0.321 -5.38 ***
WC -ve
0.000 -1.03
0.000 -0.73
DIV -ve
-0.112 -7.03 ***
-0.115 -3.45 **
MERGER ?
0.017 0.79
0.014 0.64
RESTRUCT +ve
0.209 15.56 ***
0.206 6.15 ***
LOSS ?
0.045 2.56 ***
0.045 1.72
LEV -ve
0.003 0.08
0.011 0.16
AUDIT +ve
0.036 2.63 ***
0.034 1.39
EM -ve
-0.181 -3.52 ***
-0.184 -3.72 ***
EMP +ve
1.846 5.67 ***
1.811 3.40 **
Year dummies
Included
Included
Industry dummies
Included
Included
Adjusted R2
0.478
0.476
F-Statistics
383.06
407.54
p-value
<0.0001
<0.0001
N 13,780 13,780
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less than 1%,
5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the procedure of White
(1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
234
Table 5.19 Effect of Stock Price Informativeness on SGA Costs (H1) (Continued)
∑
∑
∑
(Model 10a)
(Model 10b) Expected White-adjusted
Petersen Clustering
Direction Coefficient t-statistic Coefficient t-statistic
Intercept 0.251 1.70 0.328 1.28
ψ ? -0.030 -5.54 ***
-0.028 -2.48 **
SIZE +ve -0.601 -25.20 ***
-0.602 -15.17 ***
SIZE2 ? 0.022 13.11 ***
0.023 7.49 ***
ANALYST +ve 0.025 18.40 ***
0.024 6.28 ***
AGE +ve 0.118 10.34 **
0.118 5.00 ***
StdROE ? 0.001 3.62 ***
0.001 3.31 **
StdCF ? 0.000 -0.85
0.000 .
StdRET ? -3.039 -5.71 ***
-3.682 -4.70 ***
MB +ve 0.000 0.80
0.000 0.72
ROA -ve -0.276 -6.14 ***
-0.288 -4.42 ***
WC -ve 0.000 -0.90
0.000 -0.65
DIV -ve -0.105 -6.65 ***
-0.108 -3.28 **
MERGER ? 0.010 0.48
0.008 0.34
RESTRUCT +ve 0.205 15.25 ***
0.203 5.98 ***
LOSS ? 0.042 2.39 **
0.042 1.65
LEV -ve 0.006 0.17
0.014 0.21
AUDIT +ve 0.036 2.59 ***
0.033 1.36
EM -ve -0.225 -3.95 ***
-0.226 -3.85 ***
EMP +ve 1.933 5.68 ***
1.890 3.47 **
CAPEXt+1 -ve -0.029 -3.91 ***
-0.028 -1.85
Year dummies
Included
Included
Industry dummies
Included
Included
Adjusted R2
0.479
0.477
F-Statistics
373.00
400.18
p-value
<0.0001
<0.0001
N 13,753 13,753
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less than
1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the procedure of
White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
235
Table 5.19 Effect of Stock Price Informativeness on SGA Costs (H1)
(Continued)
∑
∑
∑
(Model 11a)
(Model 11b)
Expected White-adjusted
Petersen Clustering
Direction Coefficient t-statistic Coefficient t-statistic
Intercept 0.339 1.91 *
0.408 1.34
ψ ? -0.028 -4.20 ***
-0.028 -2.94 **
SIZE +ve -0.610 -19.83 ***
-0.612 -11.49 ***
SIZE2 ? 0.022 10.57 ***
0.023 6.02 ***
ANALYST +ve 0.024 14.10 ***
0.023 5.36 ***
AGE +ve 0.103 6.62 ***
0.104 3.70 ***
StdROE ? 0.001 2.25 **
0.001 1.79
StdCF ? 0.000 2.81 ***
0.000 .
StdRET ? -2.577 -3.96 ***
-3.056 -4.02 ***
MB +ve 0.000 0.34
0.000 .
ROA -ve -0.285 -4.48 ***
-0.293 -3.49 **
WC -ve 0.002 3.99
0.002 3.52 **
DIV -ve -0.035 -1.82 *
-0.038 -1.13
MERGER ? 0.060 2.43 **
0.059 2.37 *
RESTRUCT +ve 0.191 11.71 ***
0.189 5.11 ***
LOSS ? 0.008 0.36
0.005 0.22
LEV -ve 0.010 0.24
0.015 0.21
AUDIT +ve -0.002 -0.09
-0.005 -0.17
EM -ve -0.279 -3.49 ***
-0.294 -3.51 **
EMP +ve 3.111 2.83 ***
3.035 2.19 *
CAPEXt+1 -ve -0.015 -1.62
-0.014 -0.82
DVS_GEO +ve 0.116 7.97 ***
0.118 4.27 ***
DVS_BIZ +ve -0.051 -3.26 ***
-0.055 -1.92
Year dummies
Included
Included
Industry dummies
Included
Included
Adjusted R2
0.450
0.447
F-Statistics
190.44
195.44
p-value
<0.0001
<0.0001
N 8,354 8,354
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less than
1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the procedure of
White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
236
Table 5.19 Effect of Stock Price Informativeness on SGA Costs (H1)
(Continued)
∑
∑
∑
(Model 12a)
(Model 12b)
Expected White-adjusted
Petersen Clustering
Direction Coefficient t-statistic Coefficient t-statistic
Intercept 1.658 5.57 ***
1.577 3.53 **
ψ ? 0.028 2.05 **
0.025 1.07
SIZE +ve -0.877 -14.69 ***
-0.873 -9.81 ***
SIZE2 ? 0.040 10.82 ***
0.040 7.29 ***
ANALYST +ve 0.018 9.12 ***
0.017 4.94 ***
AGE +ve 0.124 5.74 ***
0.127 3.64 **
StdROE ? 0.001 2.05 **
0.001 1.97
StdCF ? 0.000 -2.57 **
0.000 .
StdRET ? -5.676 -3.69 ***
-5.915 -2.11
MB +ve 0.000 1.31
0.000 .
ROA -ve 0.359 2.66 **
0.380 2.06
WC -ve -0.061 -4.37 ***
-0.062 -2.61 *
DIV -ve -0.075 -2.98 ***
-0.079 -2.15 *
MERGER ? -0.026 -0.70
-0.018 -0.43
RESTRUCT +ve 0.152 7.13 ***
0.154 4.23 **
LOSS ? 0.012 0.33
0.014 0.24
LEV -ve -0.044 -0.71
-0.046 -0.49
AUDIT +ve 0.045 2.18 **
0.042 1.24
EM -ve -0.565 -4.67 ***
-0.598 -3.24 **
EMP +ve 2.672 5.91 ***
2.608 3.62 **
CAPEXt+1 -ve -0.050 -3.42 ***
-0.048 -1.91
GINDEX -ve 0.000 -0.03
-0.001 -0.10
Year dummies
Included
Included
Industry dummies
Included
Included
Adjusted R2
0.401
0.400
F-Statistics
100.82
99.28
p-value
<0.0001
<0.0001
N 4,919 4,919
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less than 1%,
5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the procedure of White
(1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
237
There is a significant negative relationship between idiosyncratic volatility, ψ and SGA
costs of the subsequent year when applying White (1980) procedure (refer Model 9a of
Table 5.19). This finding indicates that Hypothesis 1 is supported. The main model
remains robust after including additional variables such as CAPEXt+1 (CAPEX of
subsequent year) in Model 10a as well as both geographic location (DVS_GEO) and
business lines diversification (DVS_BIZ) shown in Model 11a. Similar results are
obtained by employing the Petersen (2009) procedure in Models 9b, 10b and 11b even
though the statistical significance level achieved are lower. This proves that Hypothesis
1 is well supported when corporate expenditure is proxied by SGA costs. Firm managers
extract valuable feedback from the stock markets at low (high) levels of idiosyncratic
volatility and respond accordingly by achieving high (low) levels of selling, general and
administrative cost.
A positive relationship exists between firm size and SGA costs when firm size exceeded
a certain threshold value of idiosyncratic volatility. Prior to that threshold value, firm
size is negatively associated with SGA costs implying that small firms increase their
SGA expenditure as stock price informativeness improves. As expected in item 4.5.4.1,
higher levels of SGA costs are seen in firms that are older, have higher analyst following
and greater employee intensity. Firms with high SGA costs are more likely to undertake
restructuring activities but have a less tendency to pay dividend. SGA expenditure
reduces current year profit, hence is negatively associated with ROA, as expected in
item 4.5.4.1(h). In line with scarce resource constraint commonly faced by firms, the
variable, CAPEX of the subsequent year (CAPEXt+1) is negatively associated with SGA
238
expenditure level. Firms operating under a competitive environment tend to invest more
in SGA costs (Banker et al., 2011b), thus geographic diversification (DVS_GEO) is
found to be positively related to SGA costs in the subsequent year but diversification in
the form of business lines (DVS_BIZ) has a negative association with SGA of the
subsequent year. Stock return volatility (StdROE) is also found to be negatively
associated with SGA of the subsequent year, implying an ambiguous information
environment faced by investors (Bhagat & Bolton, 2008). This phenomenon prohibits
managers learning new firm-specific information and responding accordingly (refer to
item 4.5.4.1(f)).
In all the models reported in Table 5.19, corporate governance variable such as audit
quality (AUDIT) is positively associated with SGA in the subsequent year. Further, a
negative relationship is reported between earnings management (EM) and SGA of the
subsequent year. This is within the expectations made in item 4.5.4.2. However, when
GINDEX is included as a control variable, Model 12a presents a positive relationship
between idiosyncratic volatility and SGA of the subsequent year at p <0.05 when White
(1980) procedure is applied. Model 12b shows an insignificant result for coefficient
idiosyncratic volatility, ψ by applying Petersen (2009) procedure to control for cross-
sectional and time-series correlation. This finding is largely due to the smaller sample
size arising from missing values for GINDEX.
239
5.3.1.1 Direction of Idiosyncratic Volatility
Applying ideas from the learning theory, Hypothesis 1 posits that firm managers obtain
new private information from stock markets and react accordingly by making changes in
their R&D expenditure, CAPEX and SGA costs. Table 5.20 demonstrates how
idiosyncratic volatility is related to R&D expenditure level as firms‟ idiosyncratic
volatility improves (Models 13a and 13b) and deteriorates (Models 14a and 14b) from
the previous year.
240
Table 5.20 Effect of Stock Price Informativeness on R&D Expenditure
– by Direction of Idiosyncratic Volatility‟s Movement
∑
∑ ∑
Increasing ψ
(Model 13a)
(Model 13b)
White-adjusted
Petersen Clustering
Coefficient t-statistic Coefficient t-statistic
Intercept 0.365 0.59
0.531 0.80
ψ -0.072 -5.23 ***
-0.057 -3.04 **
SIZE -0.555 -7.50 ***
-0.521 -5.00 ***
SIZE2 0.016 3.00 ***
0.013 1.93
ANALYST 0.069 18.72 ***
0.069 10.56 ***
AGE -0.105 -2.97 ***
-0.110 -2.25 *
StdROE 0.002 3.44 ***
0.002 3.81 ***
StdCF 0.000 0.19
0.000 .
StdRET 1.119 0.93
-0.407 -0.33
MB 0.003 4.70 ***
0.003 3.96 ***
ROA -0.489 -4.05 ***
-0.502 -2.37 *
WC 0.000 1.15
0.000 .
DIV -0.495 -10.25 ***
-0.502 -7.87 ***
MERGER 0.064 1.08
0.042 1.00
RESTRUCT 0.102 2.73 ***
0.094 2.17 *
LOSS 0.468 9.65 ***
0.473 9.50 ***
LEV -0.365 -4.48 ***
-0.364 -3.70 **
AUDIT 0.017 0.44
0.015 0.28
EM 0.230 2.12 **
0.226 2.35 *
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.445
0.441
F-Statistics 94.55
103.23
p-value <0.0001
<0.0001
N 3,729 3,729
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value
less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using
the procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
241
Table 5.20 Effect of Stock Price Informativeness on R&D Expenditure
– by Direction of Idiosyncratic Volatility‟s Movement
(Continued)
∑
∑ ∑
Decreasing ψ
(Model 14a)
(Model 14b)
White-adjusted
Petersen Clustering
Coefficient t-statistic Coefficient t-statistic
Intercept
0.546 1.45
0.726 1.47
ψ
-0.107 -5.92 ***
-0.091 -3.21 **
SIZE
-0.545 -8.49 ***
-0.543 -4.33 ***
SIZE2
0.012 2.61 ***
0.013 1.38
ANALYST
0.070 18.52 ***
0.071 12.84 ***
AGE
-0.032 -1.09
-0.032 -0.70
StdROE
0.004 2.70 ***
0.004 3.50 **
StdCF
0.000 0.28
0.000 .
StdRET
-4.062 -2.83 ***
-6.694 -2.69 **
MB
0.000 0.76
0.000 0.74
ROA
-0.687 -6.72 ***
-0.710 -5.93 ***
WC
0.000 3.02 ***
0.000 .
DIV
-0.467 -11.23 ***
-0.481 -6.20 ***
MERGER
0.039 0.68
0.034 0.50
RESTRUCT
0.157 4.62 ***
0.152 2.89 **
LOSS
0.479 10.65 ***
0.482 8.09 ***
LEV
-0.300 -4.81 ***
-0.294 -3.35 **
AUDIT
0.078 2.25 **
0.074 1.51
EM
-0.153 -1.88 *
-0.149 -2.09 *
Year dummies
Included
Included
Industry dummies
Included
Included
Adjusted R2
0.450
0.446
F-Statistics
123.31
130.12
p-value
<0.0001
<0.0001
N 4,784 4,784
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value
less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using
the procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
242
Table 5.20 reveals varying responsiveness of firm managers in making R&D investment
as firm-level idiosyncratic volatility moves in different directions. The coefficient
idiosyncratic volatility, ψ of 0.107 (0.091) presented in Model 14a (Model 14b) is larger
than 0.072 (0.057) shown in Model 13a (Model 13b) using White (1980) procedure
(Petersen (2009) procedure). This relationship between idiosyncratic volatility and R&D
level of the subsequent year are depicted in Figure 5.2.
Figure 5.2 Association between Current Year‟s Idiosyncratic
Volatility and R&D Expenditure of the Subsequent Year
Figure 5.2 illustrates how managers respond when firm-level idiosyncratic volatility
moves upwards or downwards. The relationship between idiosyncratic volatility and
R&D expenditure level of the subsequent year is stronger (weaker) when firm-level
stock price informativeness decreases (increases) from the previous year. This implies
R&Dt+1
Idiosyncratic Volatility, ψt
Decreasing ψ
Increasing ψ
243
that managers are more responsive when firms‟ idiosyncratic volatilities deteriorate
compared to when idiosyncratic volatilities of their firms improves from the previous
year.
Table 5.21 exhibits the association between idiosyncratic volatility and CAPEX level
when firm-level idiosyncratic volatility either advances (Models 15a and 15b) or
deteriorates (Models 16a and 16b) from the previous year.
244
Table 5.21 Effect of Stock Price Informativeness on CAPEX
– by Direction of Idiosyncratic Volatility‟s Movement
∑ ∑ ∑
Increasing ψ
(Model 15a)
(Model 15b)
White-adjusted
Petersen Clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -3.506 -13.30 ***
-3.367 -10.22 ***
Ψ -0.009 -0.90
0.001 0.12
SIZE 0.047 0.90
0.062 0.76
SIZE2 -0.004 -1.23
-0.006 -0.96
ANALYST 0.009 3.82 ***
0.010 2.90 **
AGE -0.026 -1.21
-0.038 -1.62
StdROE 0.000 5.00 ***
0.000 .
StdCF 0.000 2.53 **
0.000 .
StdRET -2.819 -3.06 ***
-5.130 -6.15 ***
MB 0.000 0.26
0.000 0.19
ROA -0.719 -3.81 ***
-0.629 -1.72
WC 0.000 -4.25 ***
0.000 .
DIV 0.099 3.44 ***
0.090 1.77
MERGER -0.206 -5.30 ***
-0.232 -5.92 ***
RESTRUCT -0.133 -5.33 ***
-0.128 -5.26 ***
LOSS -0.250 -6.22 ***
-0.244 -8.42 ***
LEV 0.246 3.45 ***
0.258 3.22 **
AUDIT 0.015 0.64
0.020 0.75
EM -0.397 -2.61 ***
-0.404 -5.97 ***
FCF 1.333 6.20 ***
1.222 3.43 **
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.216
0.211
F-Statistics 53.83
55.89
p-value <0.0001
<0.0001
N 6,313 6,313
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-
value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a)
heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using
the Petersen (2009) procedure.
245
Table 5.21 Effect of Stock Price Informativeness on CAPEX
– by Direction of Idiosyncratic Volatility‟s Movement
(Continued)
∑ ∑ ∑
Decreasing ψ
(Model 16a)
(Model 16b)
White-adjusted
Petersen Clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -3.478 -15.18 ***
-3.271 -9.75 ***
ψ -0.030 -2.46 **
0.005 0.29
SIZE 0.006 0.13
0.012 0.23
SIZE2 -0.004 -1.07
-0.004 -1.23
ANALYST 0.013 6.20 ***
0.014 4.55 ***
AGE -0.001 -0.07
-0.008 -0.26
StdROE -0.001 -0.35
-0.001 -0.30
StdCF 0.000 3.03 ***
0.000 .
StdRET -2.192 -2.14 **
-7.738 -6.28 ***
MB 0.000 0.60
0.000 .
ROA -0.564 -3.52 ***
-0.479 -1.27
WC 0.000 6.04 ***
0.000 .
DIV 0.126 5.23 ***
0.111 2.32
MERGER -0.208 -6.19 ***
-0.218 -5.86 ***
RESTRUCT -0.183 -8.32 ***
-0.186 -7.05 ***
LOSS -0.102 -2.71 ***
-0.089 -3.10 **
LEV 0.361 6.10 ***
0.375 4.46 ***
AUDIT 0.065 3.08 ***
0.066 2.64 **
EM -0.146 -1.10
-0.179 -2.12 *
FCF 1.623 10.09 ***
1.510 3.34 **
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.271
0.260
F-Statistics 98.63
110.01
p-value <0.0001
<0.0001
N 8,653 8,653
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value
less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using
the procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009)
procedure.
246
The impact of idiosyncratic volatility on CAPEX when firm-level idiosyncratic
volatility moves upwards or downwards is illustrated in Table 5.21. When White (1980)
procedures are applied, idiosyncratic volatility is negatively associated with CAPEX at p
< 0.05 when firm-level stock price informativeness is decreasing (Model 16a) and this
impact is greater compared to when idiosyncratic volatility is increasing (Model 15a).
However, by employing Petersen (2009) procedure, the coefficients of idiosyncratic
volatility, ψ are insignificant for both Models 15b and 16b, indicating that the direction
of movement in idiosyncratic volatility is irrelevant in determining subsequent year‟s
CAPEX investment. This is consistent with the insignificant results observed between
current year‟s idiosyncratic volatility and subsequent year‟s CAPEX in the main model
shown in Table 5.18.
Table 5.22 reports the impact of stock price informativeness on subsequent year‟s SGA
costs level when firm-level idiosyncratic volatility is either increasing (Models 17a and
17b) or decreasing (Models 18a and 18b) from the previous year.
247
Table 5.22 Effect of Stock Price Informativeness on SGA Costs
– by Direction of Idiosyncratic Volatility‟s Movement
∑
∑ ∑
Increasing ψ
(Model 17a)
(Model 17b)
White-adjusted
Petersen Clustering
Coefficient t-statistic Coefficient t-statistic
Intercept 0.405 1.58
0.463 1.43
ψ -0.029 -3.82 ***
-0.021 -1.21
SIZE -0.617 -16.71 ***
-0.606 -15.06 ***
SIZE2 0.024 9.27 ***
0.023 7.16 ***
ANALYST 0.026 13.33 ***
0.025 4.51 ***
AGE 0.094 5.10 ***
0.094 3.24 **
StdROE 0.001 3.27 ***
0.001 4.31 ***
StdCF 0.000 -0.34
0.000 .
StdRET -1.373 -1.97 **
-2.460 -2.80 **
MB 0.003 2.32 **
0.003 2.58 **
ROA -0.258 -4.28 ***
-0.273 -3.73 ***
WC 0.001 0.98
0.001 0.96
DIV -0.080 -3.26 ***
-0.084 -2.11 *
MERGER 0.017 0.55
0.007 0.29
RESTRUCT 0.195 9.67 ***
0.195 5.13 ***
LOSS 0.028 1.06
0.028 1.16
LEV 0.028 0.51
0.028 0.38
AUDIT 0.041 1.98 **
0.041 1.65
EM -0.126 -1.52
-0.141 -1.97 *
EMP 1.735 4.27 ***
1.699 2.83 **
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.448
0.444
F-Statistics 143.60
148.06
p-value <0.0001
<0.0001
N 5,809 5,809
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-
value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a)
heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using
the Petersen (2009) procedure.
248
Table 5.22 Effect of Stock Price Informativeness on SGA Costs
– by Direction of Idiosyncratic Volatility‟s Movement
(Continued)
∑ ∑ ∑
Decreasing ψ
(Model 18a)
(Model 18b)
White-adjusted
Petersen Clustering
Coefficient t-statistic Coefficient t-statistic
Intercept 0.550 3.05 ***
0.616 2.66 **
ψ -0.058 -6.30 ***
-0.060 -3.76 ***
SIZE -0.637 -19.58 ***
-0.644 -13.99 ***
SIZE2 0.024 10.45 ***
0.025 6.94 ***
ANALYST 0.022 11.54 ***
0.022 6.69 ***
AGE 0.136 9.12 ***
0.137 5.97 ***
StdROE 0.001 1.51
0.001 2.15 *
StdCF 0.000 -1.14
0.000 .
StdRET -4.497 -5.90 ***
-4.660 -5.75 ***
MB 0.000 -0.36
0.000 .
ROA -0.336 -5.07 ***
-0.343 -4.16 ***
WC 0.000 -3.47 ***
0.000 .
DIV -0.139 -6.70 ***
-0.140 -3.87 ***
MERGER 0.018 0.60
0.018 0.61
RESTRUCT 0.218 12.16 ***
0.216 6.23 ***
LOSS 0.058 2.40 **
0.056 1.85
LEV -0.014 -0.30
-0.002 -0.03
AUDIT 0.028 1.52
0.026 0.81
EM -0.206 -3.06 ***
-0.205 -3.79 ***
EMP 1.962 3.83 ***
1.933 2.80 **
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.498
0.496
F-Statistics 240.61
276.01
p-value <0.0001
<0.0001
N 7,971 7,971
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value
less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using
the procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
249
Table 5.22 presents the impact of idiosyncratic volatility on SGA costs level when firms‟
stock price informativeness improves and diminishes from the previous year. When
White‟s (1980) procedure is applied, the effect of current year‟s idiosyncratic volatility
to subsequent year‟s SGA costs is greater when firm-level stock price informativeness is
decreasing (see Model 18a) compared when it is improving (see Model 17a). A more
apparent contrast is observed when Petersen‟s (2009) procedure is used in Models 17b
and 18b to control for time-series and cross-sectional correlation. A significant negative
relationship is noted between current year‟s idiosyncratic volatility and subsequent
year‟s SGA costs level when firms‟ stock price informativeness declines in Model 18b
whilst the coefficient of idiosyncratic volatility, ψ is insignificantly negative in Model
17b when the firms‟ stock price informativeness improves. The association between
idiosyncratic volatility and SGA is illustrated in Figure 5.3 when firm-level
idiosyncratic volatility moves in different directions.
Figure 5.3 Association between Current Year‟s Idiosyncratic
Volatility and SGA Costs of the Subsequent Year
SGAt+1
Idiosyncratic Volatility, ψt
Decreasing ψ
Increasing ψ
250
Figure 5.3 shows that the inverse relationship between current year‟s idiosyncratic
volatility and the subsequent year‟s SGA costs level is stronger (weaker) when firms are
experiencing worsened (improved) idiosyncratic volatility. This implies firm managers
respond differently to varying movements in idiosyncratic volatility.
5.3.1.2 Summary of Findings
Hypothesis 1 of this study conjectures that there is a negative association between stock
price informativeness of a current year and corporate expenditure in the subsequent year,
proxied by R&D expenditure, CAPEX and SGA costs. The multivariate tests conducted
suggest a significant negative relationship between current year‟s stock price
informativeness and R&D level in the subsequent year. This impact of stock price
informativeness on subsequent year‟s R&D expenditure level is greater when firm-level
idiosyncratic volatility diminishes as compared to a situation when it increases from the
previous year. A significant negative association is also noted between a current year‟s
stock price informativeness and SGA level of the subsequent year especially when firms‟
idiosyncratic volatilities worsen from the previous year. The result of the association
between a current year‟s stock price informativeness and the subsequent year‟s CAPEX
level is non-robust and insignificant. These findings indicate that Hypothesis 1 of the
study is supported when corporate expenditure is represented by R&D expenditure and
SGA costs, but not by capital expenditure.
251
5.3.2 Robustness Tests
It is possible that the association between stock price informativeness and corporate
expenditure might be driven by firms investing in high level of corporate expenditure
could have greater stock price informativeness. A change model and two-stage least
squares (2SLS) regression approach are carried out to address this endogeneity issue.
5.3.2.1 Change Model
Change model deals with reverse causality problem by examining the changes in
subsequent years‟ corporate expenditure (proxied by R&D, CAPEX and SGA) as a
result of a change in stock price informativeness, as presented in Equation 5.2.
∑ ∑ ∑
(5.2)
where:
is change in corporate expenditure for firm i from year t to year t+1, is
intercept and is the coefficient of interest. is change in idiosyncratic volatility
from year t-1 to year t, is changes in a set of control variables (excluded
dummy variables) from year t-1 to year t and is unspecific random factors.
Hypothesis 1 of this study predicts a negative relationship between stock price
informativeness and corporate expenditure, hence the sign for coefficient is expected
to be negative. Table 5.23 shows the results of change model when corporate
252
expenditure is represented by R&D costs for the full sample (Models 19a and 19b) as
well as when idiosyncratic volatility is either increasing (Models 20a and 20b) or
decreasing (Models 21a and 21b). This table also highlights changes in R&D
expenditure when there is a 20 per cent reduction in the relative idiosyncratic volatility,
1-R2 (see Models 22a and 22b).
253
Table 5.23 Changes in R&D Expenditure Following Changes in Stock Price
Informativeness
∑ ∑
∑
Full Sample
(Model 19a)
(Model 19b)
White-adjusted
Petersen Clustering
Coefficient t-statistic Coefficient t-statistic
Intercept
-0.019 -0.13
-0.009 -0.20
∆ψ
0.002 0.35
0.008 1.16
∆SIZE
0.394 4.16 ***
0.388 3.62 **
∆SIZE2
-0.024 -3.02 ***
-0.022 -3.31 **
∆ANALYST
0.000 -0.10
-0.001 -0.20
∆AGE
0.128 1.20
0.138 1.12
∆StdROE
0.000 0.02
0.000 -0.38
∆StdCF
0.000 -1.08
0.000 .
∆StdRET
-0.428 -0.93
-1.095 -2.75
∆MB
0.000 -1.63
0.000 .
∆ROA
0.023 0.46
0.031 0.75
∆WC
0.000 3.89 ***
0.000 .
∆LEV
-0.061 -1.29
-0.050 -1.37
∆EM
0.037 1.31
0.037 1.94 *
Year dummies
Included
Included
Industry dummies
Included
Included
Adjusted R
2
0.053
0.033
F Statistics
14.62
5.87
p-value
<0.0001
<0.0001
N 6,636 6,636
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less
than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the
procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
254
Table 5.23 Changes in R&D Expenditure Following Changes in Stock Price
Informativeness (Continued)
∑ ∑
∑
Increasing ψ
(Model 20a) (Model 20b)
White-adjusted
Petersen Clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -0.202 -1.57
-0.159 -1.56
∆ψ 0.012 1.13
0.015 2.92 ** ∆SIZE 0.487 3.49 ***
0.511 2.56 ** ∆SIZE
2 -0.030 -2.43 **
-0.031 -2.19 * ∆ANALYST 0.001 0.56
0.001 0.28
∆AGE 0.295 1.81 *
0.360 1.30
∆StdROE 0.001 0.67
0.000 0.65
∆StdCF 0.000 -1.30
0.000 .
∆StdRET 0.197 0.41
0.082 0.24
∆MB 0.000 -0.18
0.000 .
∆ROA 0.064 0.86
0.059 2.31 * ∆WC 0.000 5.83 ***
0.000 .
∆LEV -0.091 -1.26
-0.066 -0.91
∆EM 0.037 0.78
0.042 1.02
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.059
0.042
F Statistics 7.90
16.67
p-value <0.0001
<0.0001
N 2,952 2,952 Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value
less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using
the procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009)
procedure.
255
Table 5.23 Changes in R&D Expenditure Following Changes in Stock Price
Informativeness (Continued)
∑ ∑
∑
Decreasing ψ
(Model 21a) (Model 21b)
White-adjusted
Petersen Clustering
Coefficient t-statistic Coefficient t-statistic
Intercept
0.079 0.39
0.093 0.83
∆ψ
-0.016 -2.10 **
-0.010 -0.83
∆SIZE
0.307 2.42 **
0.271 2.06 *
∆SIZE2
-0.018 -1.69 *
-0.013 -1.21
∆ANALYST
-0.001 -0.64
-0.001 -0.80
∆AGE
-0.076 -0.54
-0.135 -1.32
∆StdROE
0.000 0.10
0.000 .
∆StdCF
0.000 -0.55
0.000 .
∆StdRET
-1.038 -1.75 *
-1.936 -3.91 ***
∆MB
0.000 -1.77 *
0.000 -1.62
∆ROA
-0.008 -0.14
0.007 0.10
∆WC
0.000 1.51
0.000 .
∆LEV
-0.069 -1.30
-0.063 -1.89
∆EM
0.030 0.82
0.027 0.79
Year dummies
Included
Included
Industry dummies
Included
Included
Adjusted R
2
0.050
0.031
F Statistics
8.21
3.83
p-value
<0.0001
<0.0001
N 3,684 3,684
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value
less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using
the procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009)
procedure.
256
Table 5.23 Changes in R&D Following Changes in Stock Price
Informativeness (Continued)
∑ ∑
∑
Decreasing ψ
(Model 22a) (Model 22b)
20% reduction in 1-R2
20% reduction in 1-R2
White-adjusted
Petersen Clustering
Coefficient t-statistic Coefficient t-statistic
Intercept
-0.106 -3.08 ***
-0.071 -2.83 **
∆ψ
-0.058 -3.19 ***
-0.056 -3.95 ***
∆SIZE
0.402 1.79 *
0.283 1.61
∆SIZE2
-0.022 -1.35
-0.013 -0.91
∆ANALYST
0.001 0.18
0.000 0.02
∆AGE
0.196 0.79
-0.029 -0.16
∆StdROE
-0.001 -0.39
-0.003 -4.34 ***
∆StdCF
0.000 0.16
0.000 .
∆StdRET
-0.462 -0.47
-2.829 -2.48 **
∆MB
0.000 1.84 *
0.000 .
∆ROA
0.080 0.93
0.094 1.54
∆WC
-0.001 -0.55
-0.001 -0.74
∆LEV
0.134 1.00
0.103 1.74
∆EM
0.067 0.98
0.054 2.72 **
Year dummies
Included
Included
Industry dummies
Included
Included
Adjusted R
2
0.089
0.055
F Statistics
3.85
180.08
p-value
<0.0001
<0.0001
N 794 794
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value
less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using
the procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
257
It is observed from Table 5.23 that there is no significant change in the subsequent
year‟s R&D expenditure as a result of changes in a current year‟s idiosyncratic volatility
when White (1980) and Petersen (2009) procedures are conducted respectively (see
Models 19a and 19b). The lack of statistical significance could be attributable to the
confounding effect when there is no distinction made between idiosyncratic volatility-
increasing and idiosyncratic volatility-decreasing phenomena. Model 21a using White
(1980) method shows that when firms‟ idiosyncratic volatility is decreasing, a
significant negative association between changes in the subsequent year‟s R&D
expenditure following changes in a current year‟s idiosyncratic volatility, within the
prediction of Hypothesis 1. An insignificant positive association is, however, observed
between the changes in these two variables as firm-level idiosyncratic volatility
increases (Model 20a). These findings suggest that firm managers are exploiting
valuable information that they have yet to possess from the stock markets and react by
making changes to R&D expenditure in the subsequent year. However, this only takes
place in firms that are experiencing a downward movement in idiosyncratic volatility
from the previous year, but not otherwise.
The use of Petersen (2009) clustering procedure presents a slightly different result. A
significant positive relationship is found between changes in a current year‟s
idiosyncratic volatility and changes in the subsequent year‟s R&D expenditure when
firm-level stock price informativeness is strengthening (see Model 20b). This finding is
in contrast to the negative relationship expected by Hypothesis 1. It signifies that when
stock price informativeness improves, firm managers respond favourably to positive
258
feedback obtained from stock markets and intensify R&D activities in the subsequent
year to reap greater benefits from R&D investments.
An insignificant negative relationship, however, is observed between current year‟s
stock price informativeness and R&D investment in the subsequent year when firm-
level idiosyncratic volatility is decreasing (Model 21b). Firm managers seem to be
indifferent when stock price informativeness deteriorates. Further analyses are
performed to find out the impact of changes in the subsequent year‟s R&D expenditure
following changes in idiosyncratic volatility when the relative idiosyncratic volatility,33
1-R2 drops by trial and error, for example, by 5%, 10%, 15%, 20% and so forth. It is
observed that when firms experience a decrease in the relative idiosyncratic volatility by
20 per cent, the coefficient of ∆ψ (changes in idiosyncratic volatility) becomes
significantly negative as shown in Models 22a and 22b displayed in Table 5.23. This
finding reveals that when stock price informativeness worsens, managers do not initiate
immediate changes in R&D expenditure in the subsequent year if there is little
movement in idiosyncratic volatility. Managers are likely to assess whether the drop in
stock price informativeness is temporary or long-term in nature and by doing so, they
will only react by increasing R&D investment when it is critically compelling, that is,
when the firm-level 1-R2 decreases by 20 per cent. This asymmetric cost response
reflects the cost “stickiness” behaviour of firm managers in modifying R&D investment
33 Relative idiosyncratic volatility is used because it is more straightforward as compared to idiosyncratic
volatility which is measured in the form of natural logarithm.
259
as stock price informativeness changes. This result is consistent with prior studies
analysing asymmetric cost response particularly in the area of SGA costs (see Anderson
et al. (2003) and Balakrishnan and Gruca (2008)).
An attempt is made to find out the changes in subsequent year‟s R&D expenditure as a
result of a further decrease in 1-R2, for example, by 25 per cent. However, the sample
size becomes very small to observe any significant relationship between changes in
R&D expenditure in the subsequent year following a current year change in
idiosyncratic volatility.
Figures 5.4 and 5.5 depict the relationships between changes in idiosyncratic volatility
in a current year and changes in R&D expenditure in the subsequent year when Petersen
clustering procedure is carried out.
260
Figure 5.4 Association between Changes in Current Year‟s Idiosyncratic
Volatility and Changes in R&D Expenditure of Subsequent Year
when Idiosyncratic Volatility Increases
Figure 5.4 illustrates how managers respond when firm-level idiosyncratic volatility
moves upwards. A significant positive relationship is observed between changes in a
current year‟s idiosyncratic volatility and changes in the subsequent year‟s R&D
expenditure. This shows that managers derive the feedback from the stock markets
when stock price informativeness is improving and intensify their R&D expenditure in
the subsequent year to benefit more from the investment.
∆R&D t+1
∆Idiosyncratic Volatility t
Increasing ψ
261
Figure 5.5 Association between Changes in Current Year‟s Idiosyncratic
Volatility and Changes in R&D Expenditure of Subsequent Year
when 1-R2 Drops 20%
Figure 5.5 presents the changes in subsequent year‟s R&D expenditure following
changes in idiosyncratic volatility for a sample of 794 firms that experience a significant
drop of relative idiosyncratic volatility (1-R2), that is, by 20 per cent. Changes in a
current year‟s idiosyncratic volatility are negatively associated with changes in the
subsequent year‟s R&D expenditure, indicating that managers are responding to
decreasing stock price informativeness by increasing their R&D activities in the
following year.
∆R&D t+1
∆Idiosyncratic Volatility t
Decreasing ψ
262
The significant result exhibited in Table 5.23 show that the main model is robust to
reverse causality. Variable ∆CAPEXt+1 (Changes in CAPEX in the subsequent year) is
also added in as a control variable in all the models displayed in Table 5.23 and the
results (untabulated) are qualitatively similar.
Next, Table 5.24 shows the results of changes in CAPEX following changes in stock
price informativeness for the full sample (Models 23a and 23b) as well as when
idiosyncratic volatility either increases (Models 24a and 24b) or deteriorates (see
Models 25a and 25b). This table also highlights changes in CAPEX when there is a 20
per cent34
reduction in the relative idiosyncratic volatility, 1-R2, in Models 26a and 26b.
34 This follows a trial and error procedure undertaken in analysing change model for R&D expenditure.
263
Table 5.24 Changes in CAPEX Following Changes in Stock Price Informativeness
∑
∑
∑
Full Sample
(Model 23a)
(Model 23b)
White-adjusted
Petersen Clustering
Coefficient t-statistic Coefficient t-statistic
Intercept
0.140 1.72 *
0.049 0.50
∆ψ
0.004 0.59
0.028 2.24 *
∆SIZE
0.191 1.48
0.151 1.73
∆SIZE2
0.001 0.10
0.006 0.83
∆ANALYST
0.000 -0.25
0.001 0.31
∆AGE
-0.291 -1.90 *
-0.259 -1.29
∆StdROE
0.000 0.60
0.001 1.00
∆StdCF
0.000 -1.23
0.000 .
∆StdRET
-1.902 -3.12 ***
-6.287 -1.84
∆MB
0.000 1.29
0.000 1.31
∆ROA
0.435 5.48 ***
0.554 6.53 ***
∆WC
0.000 2.99 ***
0.000 .
∆LEV
-0.065 -0.71
-0.103 -1.61
∆EM
-0.209 -3.63 ***
-0.248 -4.48 ***
∆FCF
-0.110 -1.01
-0.230 -1.35
Year dummies
Included
Included
Industry dummies
Included
Included
Adjusted R
2
0.093
0.065
F Statistics
43.58
20.65
p-value
<0.0001
<0.0001
N 11,697 11,697
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less
than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using
the procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
264
Table 5.24 Changes in CAPEX Following Changes in Stock Price Informativeness
(Continued)
∑ ∑
∑
Increasing ψ
(Model 24a) (Model 24b)
White-adjusted
Petersen Clustering
Coefficient t-statistic Coefficient t-statistic
Intercept 0.366 2.66 ***
0.254 1.33
∆ψ 0.015 1.06
0.021 1.74
∆SIZE 0.447 1.98 **
0.456 1.76
∆SIZE2 -0.013 -0.82
-0.013 -0.74
∆ANALYST 0.000 -0.12
0.001 0.17
∆AGE -0.358 -1.58
-0.355 -2.22 * ∆StdROE 0.002 2.38 **
0.003 2.85 ** ∆StdCF 0.000 -1.54
0.000 .
∆StdRET 0.225 0.21
-2.749 -0.91
∆MB 0.000 2.66 ***
0.000 .
∆ROA 0.436 4.23 ***
0.544 6.58 *** ∆WC 0.000 4.84 ***
0.000 .
∆LEV 0.022 0.20
-0.045 -0.42
∆EM -0.239 -3.49 ***
-0.269 -4.05 *** ∆FCF -0.118 -0.89
-0.214 -1.29
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.065
0.047
F Statistics 13.29
9.05
p-value <0.0001
<0.0001
N 4,978 4,978 Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value
less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using
the procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
265
Table 5.24 Changes in CAPEX Following Changes in Stock Price Informativeness
(Continued)
∑ ∑
∑
Decreasing ψ
(Model 25a) (Model 25b)
White-adjusted
Petersen Clustering
Coefficient t-statistic Coefficient t-statistic
Intercept 0.006 0.06
-0.071 -0.68
∆ψ -0.010 -0.90
0.008 0.61
∆SIZE -0.032 -0.21
-0.110 -0.67
∆SIZE2 0.014 1.31
0.022 1.70
∆ANALYST 0.000 0.04
0.002 0.45
∆AGE -0.299 -1.43
-0.259 -0.74
∆StdROE -0.001 -1.36
-0.002 -3.59 **
∆StdCF 0.000 -0.70
0.000 .
∆StdRET -3.440 -3.67 ***
-8.362 -2.35 *
∆MB 0.000 0.89
0.000 2.00 *
∆ROA 0.451 3.86 **
0.579 3.73 ***
∆WC 0.000 0.47
0.000 .
∆LEV -0.107 -0.87
-0.135 -0.81
∆EM -0.200 -2.31 **
-0.249 -3.89 ***
∆FCF -0.135 -0.80
-0.278 -1.43
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R
2 0.110
0.079
F Statistics 30.61
13.82
p-value <0.0001
<0.0001
N 6,719 6,719
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-
value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a)
heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using the
Petersen (2009) procedure.
266
Table 5.24 Changes in CAPEX Following Changes in Stock Price Informativeness
(Continued)
∑ ∑
∑
Decreasing ψ
(Model 26a) (Model 26b)
20% reduction in 1-R
2 20% reduction in 1-R
2
White-adjusted Petersen Clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -0.067 -0.52
-0.102 -1.42
∆ψ -0.016 -0.49
-0.019 -0.87
∆SIZE -0.618 -1.58
-0.736 -1.29
∆SIZE2 0.040 1.65 *
0.050 1.20
∆ANALYST 0.000 0.10
0.002 0.22
∆AGE -0.609 -1.41
-0.821 -1.84
∆StdROE -0.011 -2.14 **
-0.013 -3.00 **
∆StdCF 0.000 -0.89
0.000 .
∆StdRET -7.085 -4.81 *** -12.682 -14.35 ***
∆MB -0.001 -1.32
0.000 -0.82
∆ROA 0.654 3.56 *** 0.691 2.68 **
∆WC 0.000 2.22 **
0.000 2.83 **
∆LEV -0.025 -0.13
-0.170 -0.97
∆EM -0.160 -1.40
-0.235 -1.85
∆FCF 0.014 0.05
-0.129 -0.59
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R
2 0.169
0.136
F Statistics 11.43
12.72
p-value <0.0001
<0.0001
N 1,442 1,442
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value
less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using
the procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
267
It is observed in Table 5.24 that almost all models (Models 23a to 26b) examined using
White (1980) and Petersen (2009) methods show insignificant results for the coefficient
∆ψ (change in idiosyncratic volatility). The only exception is observed in Model 23b
(full sample) where a positive change in CAPEX of the subsequent year at p < 0.10 is
observed following changes in idiosyncratic volatility using the Petersen (2009)
procedure on the full sample. The overall insignificant results could be driven by reverse
causality or possibly because current year‟s idiosyncratic volatility is not a key
determinant for subsequent year‟s CAPEX, as reflected by non-robust results observed
in the main model (Refer Table 5.18 of item 5.3.1).
Table 5.25 shows the results of movements in SGA costs following changes in stock
price informativeness for the full sample (see Models 27a and 27b), increasing
idiosyncratic volatility (Models 28a and 28b), decreasing idiosyncratic volatility
(Models 29a and 29b) as well as when there is a 20 per cent reduction in relative
idiosyncratic volatility, 1-R2
(Models 30a and 30b).
268
Table 5.25 Changes in SGA Costs Following Changes in Stock Price Informativeness
∑
∑
∑
Full Sample
(Model 27a)
(Model 27b)
White-adjusted
Petersen Clustering
Coefficient t-statistic Coefficient t-statistic
Intercept
-0.003 -0.11
0.011 0.35
∆ψ
0.006 2.85 ***
0.008 3.43 **
∆SIZE
0.134 3.05 ***
0.128 2.75 **
∆SIZE2
0.001 0.21
0.002 0.75
∆ANALYST
-0.003 -3.71 ***
-0.003 -3.02 **
∆AGE
0.134 2.69 ***
0.128 2.97 **
∆StdROE
0.000 0.23
0.000 -0.27
∆StdCF
0.000 -2.53 **
0.000 .
∆StdRET
-0.353 -1.37
-0.928 -1.89
∆MB
0.000 -1.22
0.000 .
∆ROA
-0.021 -0.86
-0.016 -0.53
∆WC
0.000 0.56
0.000 0.74
∆LEV
0.150 4.65 ***
0.161 5.53 ***
∆EM
0.015 0.69
0.014 0.81
∆EMP
1.703 1.67 *
1.792 2.80 **
Year dummies
Included
Included
Industry dummies
Included
Included
Adjusted R
2
0.069
0.045
F Statistics
29.60
17.61
p-value
<0.0001
<0.0001
N 10,814 10,814
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less
than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the
procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
269
Table 5.25 Changes in SGA Costs Following Changes in Stock Price
Informativeness (Continued)
∑ ∑
∑
Increasing ψ
(Model 28a) (Model 28b)
White-adjusted
Petersen Clustering
Coefficient t-statistic Coefficient t-statistic
Intercept
-0.086 -1.66 *
-0.070 -2.05 * ∆ψ
0.013 2.94 ***
0.014 2.72 ** ∆SIZE
0.121 1.80 *
0.145 3.26 ** ∆SIZE
2
0.002 0.37
0.001 0.33
∆ANALYST
-0.002 -1.57
-0.002 -3.91 *** ∆AGE
0.158 2.03 **
0.177 1.74
∆StdROE
0.001 1.37
0.001 1.06
∆StdCF
0.000 -2.35 **
0.000 .
∆StdRET
-0.066 -0.22
-0.219 -0.31
∆MB
0.000 0.66
0.000 .
∆ROA
0.000 -0.01
-0.003 -0.11
∆WC
0.001 3.05 ***
0.001 4.08 *** ∆LEV
0.133 2.94 ***
0.149 4.81 *** ∆EM
-0.012 -0.36
-0.010 -0.82
∆EMP
1.856 1.06
1.813 1.01
Year dummies
Included
Included
Industry dummies
Included
Included
Adjusted R
2
0.068
0.042
F Statistics
12.96
8.31
p-value
<0.0001
<0.0001
N 4,605 4,605 Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less
than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the
procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
270
Table 5.25 Changes in SGA Costs Following Changes in Stock Price
Informativeness (Continued)
∑
∑
∑
Decreasing ψ
(Model 29a) (Model 29b)
White-adjusted
Petersen Clustering
Coefficient t-statistic Coefficient t-statistic
Intercept
0.042 1.22
0.050 1.79
∆ψ
-0.004 -0.99
-0.002 -0.59
∆SIZE
0.119 2.08 **
0.093 1.29
∆SIZE2
0.002 0.48
0.005 1.08
∆ANALYST
-0.003 -3.48 ***
-0.004 -2.30 *
∆AGE
0.096 1.49
0.066 1.97 *
∆StdROE
0.000 -0.18
0.000 .
∆StdCF
0.000 -1.75 *
0.000 .
∆StdRET
-0.671 -1.91 *
-1.390 -5.50 ***
∆MB
0.000 -2.10 **
0.000 -6.37 ***
∆ROA
-0.043 -1.23
-0.035 -0.97
∆WC
0.000 -0.06
0.000 -0.16
∆LEV
0.167 3.92 ***
0.176 4.79 ***
∆EM
0.037 1.38
0.033 1.26
∆EMP
1.721 1.74 *
1.812 1.69
Year dummies
Included
Included
Industry dummies
Included
Included
Adjusted R
2
0.072
0.049
F Statistics
18.10
11.62
p-value
<0.0001
<0.0001
N 6,209 6,209
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value
less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using
the procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
271
Table 5.25 Changes in SGA Costs Following Changes in Stock Price
Informativeness (Continued)
∑ ∑
∑
Decreasing ψ
(Model 30a)
(Model 30b)
20% reduction in 1-R
2
20% reduction in 1-R
2
White-adjusted
Petersen Clustering
Coefficient t-statistic Coefficient t-statistic
Intercept
-0.046 -1.44
-0.059 -7.87 ***
∆ψ
-0.019 -1.75 *
-0.020 -2.70 **
∆SIZE
-0.030 -0.20
-0.116 -1.52
∆SIZE2
0.008 0.87
0.015 2.61 **
∆ANALYST
0.001 0.66
0.001 1.02
∆AGE
0.249 2.01 **
0.185 1.29
∆StdROE
-0.006 -2.67 ***
-0.008 -4.66 ***
∆StdCF
0.000 -0.27
0.000 .
∆StdRET
-0.487 -0.76
-2.161 -3.99 ***
∆MB
-0.001 -2.14 **
-0.001 -1.06
∆ROA
-0.013 -0.20
-0.010 -0.20
∆WC
0.000 -1.17
0.000 .
∆LEV
0.387 5.66 ***
0.353 7.41 ***
∆EM
0.029 0.56
0.002 0.10
∆EMP
2.045 0.72
1.583 1.33
Year dummies
Included
Included
Industry dummies
Included
Included
Adjusted R
2
0.113
0.072
F Statistics
7.16
19.06
p-value
<0.0001
<0.0001
N 1,357 1,357
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less
than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the
procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
272
Table 5.25 shows a significant positive change in subsequent year‟s SGA cost following
a change in idiosyncratic volatility when both White (1980) and Petersen (2009)
procedures are applied on the full sample in Models 27a and 27b. This is in contrast to
the prediction made in Hypothesis 1, that is, a negative relationship is expected between
changes in a current year‟s idiosyncratic volatility and changes in the subsequent year‟s
SGA costs.
When the subsamples are differentiated between increasing (Models 28a and 28b) and
decreasing (Models 29a and 29b) idiosyncratic volatilities, different results of these two
sub-samples are derived. When firms‟ idiosyncratic volatility is improving, both Models
28a and 28b show significant positive association between change in the subsequent
year‟s SGA costs following a change in idiosyncratic volatility by applying White (1980)
and Petersen (2009) procedures respectively. This implies that when stock price
informativeness improves, firm managers react to the information obtained from the
stock markets by increasing SGA costs in the subsequent year as idiosyncratic volatility
rises. Consistent with results observed in R&D expenditure in Table 5.23, these findings
signify that when stock price informativeness improves, firm managers extract positive
feedback from stock markets and increase SGA costs in the subsequent year in view of
its ability to generate long-term firm value.
When firms‟ idiosyncratic volatility is declining, an insignificant negative association is
found between changes in a current year‟s idiosyncratic volatility and changes in SGA
costs of the subsequent year (Models 29a and 29b of Table 5.25). Further analyses are
273
explored to detect the impact on changes in the subsequent year‟s SGA costs following a
change in idiosyncratic volatility when the relative idiosyncratic volatility, 1-R2
falls, for
example, by 5%, 10%, 15%, 20% and so forth. The percentage used for the examination
is determined arbitrarily using a trial and error procedure. It is observed that when the
relative idiosyncratic volatility of the current year drops by 20 per cent, the coefficient
of ∆ψ (changes in idiosyncratic volatility) becomes significantly negative as shown in
Models 30a and 30b of Table 5.25 with the application of White (1980) and Petersen
(2009) procedures respectively. This asymmetric cost response is partly due to the cost
“stickiness” behaviour of firm managers in altering SGA as stock price informativeness
changes. When stock price informativeness deteriorates, it seems that managers do not
immediately initiate any changes to the subsequent year‟s SGA costs if there are
immaterial movements in idiosyncratic volatility. They will only respond when the
relative idiosyncratic volatility, 1-R2 decreases by at least 20 per cent, probably because
managers need to assess the nature of the decline in the informativeness of stock prices.
If the decrease is assumed to be temporary, managers will not take any action to change
SGA costs. They will only respond when stock price informativeness continues to
deteriorate until a certain threshold value is reached, which is a 20 per cent drop in
relative idiosyncratic volatility (1-R2) in this sample. The “sticky” cost behaviour
demonstrated is in line with those presented by Anderson et al. (2003) where firm
managers are found to make asymmetric cost adjustments when sales revenue move in
different directions due to the managers‟ expectation on future sales demand.
274
Additional analyses are done to explore the changes in subsequent year‟s SGA costs
following a further decrease in 1-R2
of, for example, 25 per cent. No significant results,
however, are observed due to the small sample size.
Figures 5.6 and 5.7 illustrate the relationships between changes in idiosyncratic
volatility in a current year and changes in SGA costs in the subsequent year when
Petersen clustering procedure is carried out.
Figure 5.6 Association between Changes in Current Year‟s Idiosyncratic
Volatility and Changes in SGA Costs of Subsequent Year
when Idiosyncratic Volatility Increases
∆SGA t+1
∆Idiosyncratic Volatility t
Increasing ψ
275
Figure 5.6 shows changes in a current year idiosyncratic volatility is positively related to
changes in SGA costs of the subsequent year when idiosyncratic volatility is increasing.
This indicates that when stock price informativeness improves, firm managers respond
to the market feedback by boosting SGA costs in the subsequent year as idiosyncratic
volatility rises.
Figure 5.7 Association between Changes in Current Year‟s Idiosyncratic
Volatility and Changes in SGA Costs of Subsequent Year
when 1-R2 Drops 20%
Figure 5.7 exhibits how firm managers react to decreasing relative idiosyncratic
volatility (1-R2) of 20 per cent. A negative association is found between changes in a
current year‟s idiosyncratic volatility and changes in the subsequent year‟s SGA
∆SGA t+1
∆Idiosyncratic Volatility t
Decreasing ψ
276
expenditure. This shows that firm managers are reacting to the decreasing stock price
informativeness by escalating their SGA expenditure in the following year.
In summary, the significant results exhibited in Table 5.25 show that the main model is
robust to reverse causality. Variable ∆CAPEXt+1 (changes in CAPEX in the subsequent
year) is also added in as a control variable for all the models in Table 5.25 and the
results (untabulated) are qualitatively similar.
5.3.2.2 Two-Stage Least Squares Regression
A common issue in empirical research in accounting and finance is the potential for
endogeneity problems. It is possible that corporate expenditure and stock price
informativeness are endogenously determined because firms with high level of corporate
expenditure, for example, may have better informed stock prices. In order to address this
issue, a 2SLS regression is conducted to tackle the potential reverse causality issue. In
performing the 2SLS, a suitable instrument variable is required to replace the original
independent variable in this study, that is, idiosyncratic volatility.
Ferreira and Laux (2007) establish that idiosyncratic volatility is significantly correlated
with a governance index developed by Gompers et al. (2003) while control variables
such as returns on equity, variance of returns on equity, leverage, market-to-book ratio,
market capitalization, dividend dummy, firm age and a diversification dummy, are
included in the model. Therefore, this study follows the model of Ferreira and Laux
(2007) by selecting all variables used as instrument variables.
277
In the first stage of 2SLS regression, a predicted value of idiosyncratic volatility
is obtained by using Ferreira and Laux (2007) model as follows:
∑ ∑ (5.3)
where:
represents idiosyncratic volatility of the current year for firm i in the year t and
GINDEX is the Governance index developed by Gompers et al. (2003) in the previous
year, t-1. The control variables included are ROE (return on equity), VROE (variance of
return on equity), LEV (leverage), MB (market-to-book ratio), MCAP (market
capitalization), DIV (dividend dummy), AGE (firm age) and DIVER (diversification
dummy). Year and Industry dummies are included in the model while is the error term.
In the second stage of 2SLS regression, the fitted value of idiosyncratic volatility,
substitutes the actual value of in the main model, as follows:
∑ ∑ ∑
(5.4)
278
where:
is the corporate expenditure of the subsequent year and is represented by
R&D expenditure, CAPEX and SGA costs. Coefficient is the intercept and is the
coefficient of interest. The original independent variable, idiosyncratic volatility is now
represented by the predicted value of idiosyncratic volatility,
denotes a set of control variables. Year and Industry
dummies are included in the model and represents unspecified random factors.
Table 5.26 presents the results of the first stage estimates following Ferreira and Laux
(2007) in Model 31. This table also reports the second stage results with the predicted
value of idiosyncratic volatility, substituting the actual value of idiosyncratic
volatility, as the independent variable in relation to:
a) R&D expenditure (Models 32a and 32b);
b) CAPEX (Models 33a and 33b); and
c) SGA costs (Models 34a and 34b).
279
Table 5.26 Results of Two-Stage Least Squares Regressions
∑
∑
First Stage Regression
Dependent variable= ψt
(Model 31)
Expected White- adjusted
Direction Coefficient t-statistic
Intercept 3.581 17.03 ***
GINDEXt-1 -ve -0.017 -3.07 ***
ROEt-1 +ve -0.002 -0.97
VROEt-1 -ve 0.000 -6.03 ***
LEVt-1 +ve 0.348 4.53 ***
MBt-1 -ve 0.000 -1.48
MCAPt-1 -ve -0.214 -15.71 ***
DIVt-1 +ve -0.199 -6.89 ***
AGEt-1 +ve -0.008 -0.30
DIVERt-1 -ve -0.138 -5.04 ***
Year dummies
Included
Industry dummies
Included
Adjusted R2
0.292
F-Statistics
88.71
p-value
<0.0001
N 4,886
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical
significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are
adjusted for (a) heteroskedasticity using the procedure of White (1980) and (b)
clustering by firm and year using the Petersen (2009) procedure.
280
Table 5.26 Result of Two-Stage Least Squares Regressions (Continued)
∑
∑
∑
Second Stage Regression
Dependent variable= R&Dt+1
(Model 32a) (Model 32b)
Expected White- adjusted Petersen Clustering
Direction Coefficient t-statistic Coefficient t-statistic
Intercept 3.458 6.06 ***
3.476 3.75 ***
PREDψt -ve -0.264 -2.15 **
-0.143 -1.98 *
SIZEt -ve -1.172 -8.89 ***
-1.179 -5.80 ***
SIZEt2 +ve 0.045 5.17 ***
0.047 3.62 **
ANALYSTt +ve 0.075 18.39 ***
0.075 10.29 ***
StdCFt +ve 0.000 1.45
0.000 1.33
StdRETt -ve -2.636 -0.75
-5.726 -1.03
EPt +ve -0.480 -1.44
-0.489 -1.01
ROAt -ve -0.018 -0.07
0.000 0.00
WCt +ve 0.021 2.22 **
0.021 2.35 *
MERGERt +ve 0.214 3.37 ***
0.225 5.12 ***
RESTRUCTt +ve 0.117 2.53 **
0.116 1.77
LOSSt +ve 0.382 4.67 ***
0.374 2.59 **
AUDITt +ve 0.084 1.77 *
0.082 1.01
EMt -ve -0.197 -0.98
-0.229 -1.30
INDDIRPCTt -ve -0.165 -3.65 ***
-0.167 -2.29 *
Year dummies
Included
Included
Industry dummies
Included
Included
Adjusted R
2
0.366
0.363
F-Statistics
44.55
63.07
p-value
<0.0001
<0.0001
N 2,114
2,114
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less
than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the
procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
281
Table 5.26 Result of Two-Stage Least Squares Regressions (Continued)
∑
∑
∑
Second Stage Regression
Dependent variable= CAPEXt+1
(Model 33a) (Model 33b)
Expected White- adjusted Petersen Clustering
Direction Coefficient t-statistic Coefficient t-statistic
Intercept
-3.061 -7.04 ***
-3.081 -4.28 ***
PREDψt -ve -0.130 -1.89 *
0.134 1.58
SIZEt -ve -0.109 -1.15
-0.133 -0.88
SIZEt2 +ve 0.004 0.71
0.008 0.87
ANALYSTt +ve 0.007 2.64 ***
0.009 2.61 **
StdCFt +ve 0.000 0.38
0.000 .
StdRETt -ve 2.999 1.46
-2.410 -1.16
EPt +ve 0.612 3.25 ***
0.544 2.66 **
ROAt -ve -0.506 -1.79 *
-0.426 -1.80
WCt +ve 0.001 0.09
0.001 0.09
MERGERt -ve -0.244 -5.83 ***
-0.251 -6.06 ***
RESTRUCTt -ve -0.116 -4.20 ***
-0.124 -3.32 **
LOSSt -ve -0.053 -0.92
-0.068 -1.23
AUDITt +ve 0.063 2.45 **
0.069 1.72
EMt -ve -0.284 -1.96 ***
-0.341 -2.09 *
FCFt +ve 2.417 8.48 ***
2.398 5.92 ***
INDDIRPCTt ? -0.014 -0.53
-0.006 -0.14
Year dummies
Included
Included
Industry dummies
Included
Included
Adjusted R
2
0.263
0.255
F-Statistics
45.52
47.05
p-value
<0.0001
<0.0001
N 3,628
3,628
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less than
1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the procedure of
White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
282
Table 5.26 Result of Two-Stage Least Squares Regressions (Continued)
∑
∑
∑
Second Stage Regression
Dependent variable= SGAt+1
(Model 34a) (Model 34b)
Expected White- adjusted Petersen Clustering
Direction Coefficient t-statistic Coefficient t-statistic
Intercept
3.320 10.31 ***
3.147 6.43 ***
PREDψt -ve -0.579 -8.91 ***
-0.259 -4.21 ***
SIZEt -ve -0.908 -14.17 ***
-0.928 -8.38 ***
SIZEt2 +ve 0.036 8.68 ***
0.040 5.52 ***
ANALYSTt +ve 0.012 5.56 ***
0.015 3.66 ***
StdCFt -ve 0.000 -0.58
0.000 .
StdRETt -ve -6.793 -4.19 ***
-11.988 -6.31 ***
EPt +ve -0.362 -3.11 ***
-0.440 -3.25 **
ROAt -ve 0.110 0.80
0.218 1.11
WCt -ve -0.017 -0.82
-0.003 -0.09
MERGERt +ve 0.020 0.51
0.011 0.26
RESTRUCTt +ve 0.179 7.17 ***
0.167 3.48 **
LOSSt +ve 0.079 1.80 *
0.062 1.05
AUDITt +ve -0.014 -0.55
-0.007 -0.18
EMt -ve -0.239 -2.24 **
-0.281 -2.09 *
EMPt +ve 2.225 1.53
2.896 1.09
INDDIRPCTt +ve 0.022 0.91
0.032 0.92
Year dummies
Included
Included
Industry dummies
Included
Included
Adjusted R
2
0.384
0.376
F-Statistics
74.95
72.05
p-value
<0.0001
<0.0001
N
3,436
3,436
Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less than 1%,
5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the procedure of White
(1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
283
Table 5.26 further shows the first stage regression that uses idiosyncratic volatility as the
dependent variable. The variable GINDEX has a significant negative relationship with
idiosyncratic volatility in Model 31 and this is consistent with the findings in Ferreira
and Laux (2007). Other variables have the same sign compared to the results in Ferreira
and Laux (2007) with the exception of the dividend dummy.
In the second stage regressions, the coefficient of is negatively correlated with
the subsequent year‟s R&D expenditure at varying levels of statistical significance in
Models 32a and 32b when the White (1980) and Petersen (2009) procedures are
conducted respectively. Nevertheless, non-robust stage-two regressions results are
exhibited when corporate expenditure is proxied by CAPEX. A negative relationship at
p < 0.10 is noted between and CAPEX of subsequent year in Model 33a when
using White (1980) method but an insignificant positive association is observed between
these two variables in Model 33b when Petersen (2009) procedure is applied. Further,
Table 5.26 highlights significant negative relationships between the predicted value of
idiosyncratic volatility, and subsequent year‟s SGA costs in Models 34a and
34b when the White (1980) and Petersen (2009) procedures are conducted respectively.
These findings in Table 5.26 indicate that the inverse relationship between a current
year‟s stock price informativeness and the subsequent year‟s corporate expenditure is
robust to potential endogeneity when it is represented by R&D expenditure and SGA
costs but not by CAPEX.
284
A comparison of results of control variables in Table 5.26 are made with those of the
main model presented in Table 5.17 to 5.19 in item 5.3.1. All control variables generally
show the same sign in the second stage regression results when they correlates with
corporate expenditure of the subsequent year. Several control variables such as firm age
(AGE), leverage (LEV), and dividend dummy (DIV) are not repeated in the second
stage regression as they are already used in the first stage regression to impose exclusion
restrictions on the model (Wooldridge, 2009, p. 521). Variable return volatility (StdROE)
is not included in the second stage regression as it is closely related to the variable
variance of return on equity (VROE) used in the stage one regression. Variables such as
earnings-to-price ratio (EP) and percentage of independent directors (INDDIRPCT) are
employed in the second stage regression to substitute market-to-book ratio (MB) and
GINDEX already employed in the first stage regression.
5.3.3 Summary of Findings – Hypothesis 1
Hypothesis 1 conjectures that there is a negative association between current year‟s
idiosyncratic volatility and subsequent year‟s corporate expenditure represented by
R&D expenditure, CAPEX and SGA costs. A summary of the results of the multivariate
and robustness tests conducted for each of the proxy of corporate expenditure are
highlighted in items (a) to (c).
285
a) R&D expenditure
A significant inverse relationship between current year idiosyncratic volatility and R&D
expenditure of the subsequent year is documented after controlling for relevant control
variables, capital expenditure of the subsequent year and diversification strategy. This
provides support to Hypothesis 1 of the study.
The robustness tests of change model and two-stage least squares regression confirm the
robustness of the lead-lag model to endogeneity. The test on change model provides
additional insights as change in R&D expenditure of the subsequent year following the
change in a current year‟s idiosyncratic volatility reveal different results when
idiosyncratic volatility moves upwards or downwards. When firm-level stock price
informativeness is improving, a change in current year‟s idiosyncratic volatility is
positively related to changes in R&D expenditure of the following year. However, when
stock price informativeness is deteriorating, managers do not react immediately to
modify the level of R&D expenditure until the relative idiosyncratic volatility (1-R2)
drops by at least 20 per cent. The two-stage least squares regression further substantiates
the significant negative association found between a current year‟s stock price
informativeness and R&D expenditure of subsequent year, after considering
endogeneity.
b) Capital expenditure
It is noted that current year‟s idiosyncratic volatility level is not significantly associated
with CAPEX level of the subsequent year. The test on change model also present non-
286
robust results on the association between changes in idiosyncratic volatility and changes
in subsequent year‟s CAPEX for both scenarios of improving and weakening
idiosyncratic volatilities. Weak statistical results are also revealed by the two-stage least
squares regression on CAPEX as seen in Table 5.26. As such, it is concluded that a
current year‟s stock price informativeness is not significantly related to CAPEX of
subsequent year, thus not supporting Hypothesis 1 of the study.
c) SGA costs
It is found that a current year‟s idiosyncratic volatility level has a significant negative
relationship with SGA costs level in the subsequent year after controlling for relevant
control variables, CAPEX of the subsequent year and diversification strategy adopted by
firms. These findings show that Hypothesis 1 is well supported.
The main model of SGA costs is robust to reverse causality and is not potentially
endogenous. A positive change in SGA costs in the subsequent year is observed as a
result of a change in idiosyncratic volatility when stock price informativeness improves.
On the other hand, when stock price informativeness diminishes, managers will only
initiate changes in the subsequent year‟s SGA costs when the relative idiosyncratic
volatility (1-R2) drops at least by 20 per cent. The two-stage least squares regression
further suggests a significant negative association between idiosyncratic volatility and
SGA costs of the subsequent year, after considering endogeneity. As such, it is
concluded that a current year‟s stock price informativeness is significantly related to
287
SGA costs of the subsequent year, providing support to Hypothesis 1 when corporate
expenditure is represented by SGA costs.
The findings on R&D and SGA provide evidence of learning hypothesis where
managers learn new private information of their own stock prices via input from the
stock markets. Additional insights on cost „stickiness‟ behaviour of firm managers are
revealed by using a change model that examines the relationship between changes in
current year‟s stock price informativeness and changes in the subsequent year‟s R&D
expenditure and SGA costs.
The results also indicate that stock price informativeness in a current year is not
associated with capital expenditure in the subsequent year. This indicates that firm
managers do not depend on capital markets to provide information for their capital
investment decisions. This is possibly due to needs of proper strategic planning and
consideration of funding before firms venture into capital projects. In addition, capital
expenditure are long-term oriented and thus any changes made in capital investment can
only be done after appropriate board approval. Therefore, stock prince informativeness
is not a significant determinant for capital expenditure in this study.
288
5.4 Hypotheses 2a to 2c – The Role of Information Asymmetry
Hypotheses 2a to 2c examine whether information asymmetry plays a role in the
relationship between current year‟s stock price informativeness and corporate
expenditure of the following year. Three proxies of information asymmetry are used in
this study, namely, firm size, analyst following and bid-ask spreads. Two sub-samples
are formed for each type of the corporate expenditure (R&D expenditure, CAPEX and
SGA costs) based on the median value of the full sample on firm size (natural logarithm
of total assets), analyst following (number of analysts that follow a firm) and bid-ask
spreads.
5.4.1 Firm Size
Table 5.27 presents the results of the effect of current year‟s stock price informativeness
on subsequent year‟s R&D level for the following types of firms:
a) Large firms – Models 35a and 35b.
b) Small firms – Models 36a and 36b.
c) Small firms with increasing idiosyncratic volatility – Models 37a and 37b.
d) Small firms with decreasing idiosyncratic volatility – Models 38a and 38b.
289
Table 5.27 Effect of Stock Price Informativeness on R&D Expenditure
– by Firm Size (H2a)
∑
∑
∑
Large Firms
Large Firms
(Model 35a)
(Model 35b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept 2.663 4.31 *** 2.617 2.35 *
ψ 0.034 1.35 0.018 0.61
SIZE -1.052 -6.94 *** -1.039 -3.57 **
SIZE2 0.047 5.08 *** 0.047 2.59 **
ANALYST 0.071 23.18 *** 0.069 12.96 ***
AGE 0.111 3.81 *** 0.111 1.81
StdROE 0.002 6.05 *** 0.002 5.46 ***
StdCF 0.000 -2.28 ** 0.000 .
StdRET -7.152 -3.55 *** -6.487 -3.49 **
MB 0.000 1.30 0.000 1.02
ROA -0.603 -2.59 *** -0.543 -1.75
WC 0.000 7.22 *** 0.000 .
DIV -0.505 -12.68 *** -0.513 -6.21 ***
MERGER 0.090 1.83 * 0.104 1.90
RESTRUCT 0.166 4.79 *** 0.167 2.79 **
LOSS 0.335 5.60 *** 0.329 5.04 ***
LEV -0.546 -6.80 *** -0.554 -3.45 **
AUDIT 0.022 0.66 0.020 0.36
EM -0.272 -1.67 * -0.360 -2.38 *
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.368
0.366
F-Statistics 78.54
107.23 p-value <0.0001
<0.0001
N 4,257 4,257 Note: Large (small) firms are defined as firms above (below) the median value of natural logarithm of total assets
of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value
less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the
procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
290
Table 5.27 Effect of Stock Price Informativeness on R&D Expenditure
– by Firm Size (H2a) (Continued)
∑
∑
∑
Small Firms
Small Firms
(Model 36a)
( Model 36b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -3.262 -8.17 *** -3.083 -5.06 ***
Ψ -0.077 -6.84 *** -0.060 -3.70 ***
SIZE 0.341 2.37 ** 0.338 1.46
SIZE2 -0.078 -4.87 *** -0.077 -2.99 **
ANALYST 0.063 11.35 *** 0.065 9.37 ***
AGE -0.243 -6.92 *** -0.240 -4.58 ***
StdROE 0.010 2.16 ** 0.010 1.50
StdCF 0.008 4.63 *** 0.008 4.31 ***
StdRET 0.902 0.96 -1.151 -0.93
MB 0.001 1.19 0.001 0.99
ROA -0.571 -6.40 *** -0.578 -3.96 ***
WC 0.000 1.12 0.000 .
DIV -0.425 -8.08 *** -0.440 -4.85 ***
MERGER -0.011 -0.17 -0.042 -0.54
RESTRUCT 0.153 4.28 *** 0.141 3.74 ***
LOSS 0.474 10.74 *** 0.481 8.53 ***
LEV -0.192 -2.97 *** -0.184 -2.43 *
AUDIT 0.108 2.87 *** 0.099 1.38
EM 0.088 1.34 0.093 1.26
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.375
0.368
F-Statistics 80.63
90.82 p-value <0.0001
<0.0001
N 4,256 4,256 Note: Large (small) firms are defined as firms above (below) the median value of natural logarithm of total assets
of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-
value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the
procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
291
Table 5.27 Effect of Stock Price Informativeness on R&D Expenditure
– by Firm Size (H2a) (Continued)
∑ ∑ ∑
Small Firms
Small Firms
Increasing ψ
Increasing ψ
(Model 37a)
(Model 37b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -3.407 -6.30 *** -3.153 -7.40 ***
ψ -0.069 -4.67 *** -0.058 -3.26 **
SIZE 0.319 1.50 0.328 1.81
SIZE2 -0.081 -3.45 *** -0.081 -3.81 ***
ANALYST 0.076 9.26 *** 0.080 12.38 ***
AGE -0.275 -5.31 *** -0.272 -4.79 ***
StdROE 0.012 1.50 0.011 1.53
StdCF 0.007 3.04 *** 0.007 3.49 **
StdRET 1.477 1.22 0.202 0.20
MB 0.002 2.58 *** 0.001 2.04 *
ROA -0.492 -3.84 *** -0.496 -2.66 **
WC 0.000 -0.14 0.000 .
DIV -0.359 -4.37 *** -0.375 -3.64 **
MERGER -0.020 -0.18 -0.054 -0.45
RESTRUCT 0.106 1.95 * 0.096 1.69
LOSS 0.476 7.51 *** 0.486 9.08 ***
LEV -0.246 -2.56 ** -0.236 -2.21 *
AUDIT 0.105 1.77 * 0.090 1.18
EM 0.333 2.91 *** 0.334 4.07 ***
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.358
0.350
F-Statistics 33.22
206.00 p-value <0.0001
<0.0001
N 1,851 1,851 Note: Large (small) firms are defined as firms above (below) the median value of natural logarithm of total assets of the
full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less than 1%,
5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the procedure of White (1980)
and (b) clustering by firm and year using the Petersen (2009) procedure.
292
Table 5.27 Effect of Stock Price Informativeness on R&D Expenditure
– by Firm Size (H2a) (Continued)
∑
∑
∑
Small Firms
Small Firms
Decreasing ψ
Decreasing ψ
(Model 38a)
(Model 38b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -3.018 -6.28 *** -2.757 -3.52 **
ψ -0.145 -6.94 *** -0.122 -4.59 ***
SIZE 0.376 2.06 ** 0.377 1.31
SIZE2 -0.082 -3.93 *** -0.082 -2.54 **
ANALYST 0.047 6.37 *** 0.049 6.88 ***
AGE -0.216 -4.71 *** -0.219 -3.60 **
StdROE 0.008 1.34 0.008 1.15
StdCF 0.009 3.41 *** 0.009 4.36 ***
StdRET -0.260 -0.16 -3.823 -1.78
MB 0.001 1.06 0.001 0.88
ROA -0.703 -8.22 *** -0.724 -6.86 ***
WC 0.000 1.73 * 0.001 1.55
DIV -0.496 -7.40 *** -0.513 -5.64 ***
MERGER -0.023 -0.26 -0.049 -0.65
RESTRUCT 0.192 4.18 *** 0.180 4.89 ***
LOSS 0.430 8.19 *** 0.441 6.11 ***
LEV -0.099 -1.53 -0.095 -1.29
AUDIT 0.108 2.25 ** 0.100 1.44
EM -0.063 -0.82 -0.049 -0.60
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.399
0.393
F-Statistics 50.88
59.86
p-value <0.0001
<0.0001
N 2,405 2,405 Note: Large (small) firms are defined as firms above (below) the median value of natural logarithm of total assets
of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a
p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using
the procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
293
Table 5.27 shows that the coefficient idiosyncratic volatility, ψ is significantly negative
for small firms (see Models 36a and 36b) but insignificant for large firms (see Models
35a and 35b). These findings indicate that the inverse relationship between a current
year‟s stock price informativeness and R&D expenditure of the subsequent year is
stronger when firm size is small. This scenario is justified by the greater extent of
private information available in small firms as reported by Bakke and Whited (2010).
Managers managing small firms can learn more from the market feedback on firms‟
growth prospects, sales demand and competition and react quicker in modifying firms‟
R&D expenditure in the subsequent year.
The sub-sample of small firms is further divided into two groups, one with increasing
idiosyncratic volatility and the other one with decreasing idiosyncratic volatility. It is
observed that the coefficient of idiosyncratic volatility, ψ is greater in Models 38a and
38b when idiosyncratic volatility is decreasing compared to Models 37a and 37b when
the idiosyncratic volatility is improving. Further, by applying Petersen (2009) procedure,
the significance level achieved by the coefficient idiosyncratic volatility in Model 38b
(decreasing stock price informativeness) is higher than that of Model 37b (strengthening
stock price informativeness). Consequently, managers of small firms are more
responsive to new private information embedded in the stock prices, thereby making
R&D investment in response to stock price informativeness, especially when
idiosyncratic volatility is worsening.
294
Table 5.28 reports the effect of current year‟s stock price informativeness on subsequent
year‟s CAPEX level of large and firms.
Table 5.28 further highlight that the coefficient idiosyncratic volatility, ψ is significantly
negative for large firms in Model 39a using White (1980) procedure but becomes
insignificantly positive in Model 39b when the Petersen (2009) procedure are applied to
control for cross-sectional and time series correlation. Insignificant association is also
noted between idiosyncratic volatility and the subsequent year‟s CAPEX for small firms
(Models 40a and 40b). These findings suggest non-robust results on the association
between a current year‟s stock price informativeness and CAPEX of the subsequent year
when the sample data is analysed in terms of their firm size.
295
Table 5.28 Effect of Stock Price Informativeness on CAPEX – by Firm Size (H2a)
∑
∑
∑
Large Firms
Large Firms
(Model 39a)
(Model 39b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -3.307 -8.48 *** -3.016 -4.69 ***
ψ -0.027 -2.03 ** 0.025 1.16
SIZE -0.132 -1.46 -0.147 -0.91
SIZE2 0.006 1.19 0.007 0.70
ANALYST 0.007 3.89 *** 0.007 2.02 *
AGE 0.010 0.62 0.014 0.45
StdROE 0.000 6.06 *** 0.000 .
StdCF 0.000 3.02 *** 0.000 .
StdRET 2.044 1.99 ** -3.899 -4.46 ***
MB -0.001 -1.05 -0.001 -1.28
ROA 0.103 0.51 0.205 0.50
WC 0.001 4.01 *** 0.001 3.33 **
DIV 0.111 4.83 *** 0.092 2.03 *
MERGER -0.186 -6.19 *** -0.210 -6.54 ***
RESTRUCT -0.179 -8.76 *** -0.183 -5.35 ***
LOSS 0.045 1.20 0.065 1.79
LEV 0.304 5.16 *** 0.327 3.54 **
AUDIT 0.043 2.27 ** 0.049 1.62
EM -0.296 -2.23 ** -0.371 -3.10 **
FCF 2.822 10.82 *** 2.674 3.30 **
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.270
0.256
F-Statistics 84.78
111.03
p-value <0.0001
<0.0001
N 7,483 7,483
Note: Large (small) firms are defined as firms above (below) the median value of natural logarithm of total assets
of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value
less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the
procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
296
Table 5.28 Effect of Stock Price Informativeness on CAPEX – by Firm Size (H2a)
(Continued)
∑
∑
∑
Small Firms
Small Firms
(Model 40a)
(Model 40b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -2.515 -5.57 *** -2.269 -4.01 ***
ψ -0.007 -0.76 0.007 0.51
SIZE -0.295 -2.44 ** -0.333 -1.80
SIZE2 0.034 2.67 *** 0.038 1.78
ANALYST 0.021 5.50 *** 0.022 4.30 ***
AGE -0.041 -1.77 -0.057 -1.76
StdROE 0.000 0.11 0.000 0.58
StdCF -0.003 -2.82 *** -0.003 -1.33
StdRET -5.054 -5.41 *** -7.630 -5.68 ***
MB 0.000 1.58 0.000 .
ROA -0.613 -4.17 *** -0.508 -1.46
WC 0.000 -0.01 0.000 .
DIV 0.116 3.74 *** 0.106 2.15 *
MERGER -0.243 -5.26 *** -0.251 -4.30 ***
RESTRUCT -0.138 -5.10 *** -0.129 -3.01 **
LOSS -0.217 -6.06 *** -0.209 -5.92 ***
LEV 0.339 5.11 *** 0.339 3.23 **
AUDIT 0.015 0.57 0.018 0.50
EM -0.238 -1.92 * -0.249 -3.03 **
FCF 1.199 7.63 *** 1.099 3.41 **
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.227 0.221
F-Statistics 67.73 62.17
p-value <0.0001 <0.0001
N 7,483 7,483 Note: Large (small) firms are defined as firms above (below) the median value of natural logarithm of total assets
of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a
p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using
the procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
297
Table 5.29 demonstrates the effect of current year‟s stock price informativeness on SGA
costs of the subsequent year for the following types of firms:
a) Large firms – Models 41a and 41b.
b) Small firms – Models 42a and 42b.
c) Small firms with increasing idiosyncratic volatility – Models 43a and 43b.
d) Small firms with decreasing idiosyncratic volatility – Models 44a and 44b.
298
Table 5.29 Effect of Stock Price Informativeness on SGA Costs – by Firm Size (H2a)
∑
∑
∑
Large Firms
Large Firms
(Model 41a)
(Model 41b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept 1.489 3.76 *** 1.418 1.82
ψ 0.000 -0.02 -0.020 -0.93
SIZE -0.875 -9.71 *** -0.868 -4.85 ***
SIZE2 0.040 7.60 *** 0.040 3.88 ***
ANALYST 0.020 11.91 *** 0.019 5.16 ***
AGE 0.201 12.61 *** 0.199 5.79 ***
StdROE 0.004 3.79 *** 0.003 4.98 ***
StdCF 0.000 -2.73 *** 0.000 .
StdRET -6.043 -6.50 *** -4.251 -4.99 ***
MB 0.002 2.09 ** 0.002 2.30 *
ROA 0.499 3.43 *** 0.517 1.53
WC 0.000 0.22 0.000 0.10
DIV -0.163 -7.48 *** -0.161 -4.24 ***
MERGER -0.006 -0.22 0.006 0.16
RESTRUCT 0.245 12.49 *** 0.247 5.67 ***
LOSS 0.006 0.16 0.000 0.00
LEV -0.152 -2.68 *** -0.159 -1.41
AUDIT 0.036 1.93 * 0.032 0.98
EM -0.766 -6.05 *** -0.787 -3.13 **
EMP 3.765 7.94 *** 3.708 3.55 **
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.450
0.448
F-Statistics 171.82
184.47
p-value <0.0001
<0.0001
N 6,890 6,890
Note: Large (small) firms are defined as firms above (below) the median value of natural logarithm of total assets
of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-
value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the
procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
299
Table 5.29 Effect of stock price informativeness on SGA Costs – by Firm Size (H2a)
(Continued)
∑
∑
∑
Small Firms
Small Firms
(Model 42a)
(Model 42b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -0.932 -3.48 *** -0.815 -2.21 *
ψ -0.030 -5.00 *** -0.025 -2.01 *
SIZE -0.279 -4.44 *** -0.286 -3.42 **
SIZE2 -0.009 -1.32 -0.008 -0.91
ANALYST 0.030 11.43 *** 0.030 4.68 ***
AGE 0.018 1.12 0.017 0.67
StdROE 0.001 2.99 *** 0.001 3.00 **
StdCF 0.004 5.56 *** 0.004 5.47 ***
StdRET -0.905 -1.55 -2.106 -3.30 **
MB 0.000 -1.90 0.000 .
ROA -0.388 -8.11 *** -0.407 -6.05 ***
WC -0.001 -1.24 -0.001 -0.90
DIV -0.069 -3.05 *** -0.074 -1.54
MERGER 0.045 1.44 0.028 0.85
RESTRUCT 0.148 8.65 *** 0.145 4.87 ***
LOSS 0.103 5.05 *** 0.102 3.20 **
LEV 0.190 4.52 *** 0.206 2.80 **
AUDIT 0.028 1.46 0.023 0.75
EM 0.031 0.55 0.037 0.69 EMP 0.191 0.69 0.119 0.21
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.386 0.381
F-Statistics 132.05 134.54
p-value <0.0001 <0.0001
N 6,890 6,890 Note: Large (small) firms are defined as firms above (below) the median value of natural logarithm of total assets
of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value
less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the
procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
300
Table 5.29 Effect of Stock Price Informativeness on SGA Costs – by Firm Size (H2a)
(Continued)
∑
∑
∑
Small Firms
Small Firms
Increasing ψ
Increasing ψ
(Model 43a)
(Model 43b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -1.297 -1.89 * -1.111 -1.21
ψ -0.023 -2.69 *** -0.015 -0.79
SIZE -0.401 -4.04 *** -0.410 -3.08 **
SIZE2 0.004 0.39 0.006 0.39
ANALYST 0.030 7.64 *** 0.031 3.74 ***
AGE 0.004 0.16 0.004 0.17
StdROE 0.001 3.05 *** 0.001 14.23 ***
StdCF 0.004 4.19 *** 0.004 6.39 ***
StdRET -0.070 -0.09 -1.108 -1.49
MB 0.006 2.46 ** 0.007 1.70
ROA -0.312 -5.05 *** -0.338 -4.49 ***
WC 0.000 -1.27 0.000 -0.91
DIV -0.046 -1.33 -0.050 -0.95
MERGER 0.000 0.00 -0.024 -0.73
RESTRUCT 0.128 5.00 *** 0.125 3.98 ***
LOSS 0.112 3.77 *** 0.114 5.30 ***
LEV 0.113 1.64 0.111 1.40
AUDIT 0.062 2.16 ** 0.053 1.78
EM 0.089 1.11 0.089 0.89 EMP 0.274 0.67 0.165 0.30
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.381 0.374
F-Statistics 54.94 59.52
p-value <0.0001 <0.0001
N 2,899 2,899
Note: Large (small) firms are defined as firms above (below) the median value of natural logarithm of total assets
of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value
less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the
procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
301
Table 5.29 Effect of Stock Price Informativeness on SGA Costs– by Firm Size (H2a)
(Continued)
∑
∑
∑
Small Firms
Small Firms
Decreasing ψ
Decreasing ψ
(Model 44a)
(Model 44b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -0.949 -3.28 *** -0.834 -2.53 **
ψ -0.053 -5.22 *** -0.049 -3.42 **
SIZE -0.174 -2.19 ** -0.179 -1.67
SIZE2 -0.021 -2.40 ** -0.020 -1.81
ANALYST 0.027 7.53 *** 0.027 4.59 ***
AGE 0.031 1.43 0.029 1.04
StdROE 0.001 1.03 0.001 1.39
StdCF 0.003 3.76 *** 0.003 5.39 ***
StdRET -1.934 -2.21 ** -3.296 -3.73 ***
MB 0.000 -1.77 * 0.000 .
ROA -0.436 -6.01 *** -0.455 -4.11 ***
WC -0.003 -0.79 -0.003 -0.69
DIV -0.097 -3.22 *** -0.101 -1.85
MERGER 0.076 1.78 * 0.063 1.21
RESTRUCT 0.167 7.27 *** 0.164 4.30 ***
LOSS 0.099 3.53 *** 0.098 1.98 *
LEV 0.222 3.96 *** 0.244 2.84 **
AUDIT 0.003 0.11 -0.001 -0.04
EM -0.020 -0.24 -0.006 -0.11 EMP 0.178 0.49 0.126 0.22
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.391 0.389
F-Statistics 78.75 81.23
p-value <0.0001 <0.0001
N 3,991 3,991 Note: Large (small) firms are defined as firms above (below) the median value of natural logarithm of total
assets of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at
a p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity
using the procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.
302
Table 5.29 shows a significant negative association between idiosyncratic volatility and
subsequent year‟s SGA costs in small firms (Models 42a and 42b) but no material
relationship is found between a current year‟s idiosyncratic volatility and the subsequent
year‟s SGA costs level in large firms (Models 41a and 41b). Further analyses on two
sub-samples of small firms based on increasing and decreasing idiosyncratic volatility
reveal asymmetric cost behaviour of firm managers. Only small firms with diminishing
idiosyncratic volatility are found to be responsive to feedback from the capital markets
and make changes to SGA costs (Model 44a and 44b). Small firms with intensifying
idiosyncratic volatility are shown to be making changes in SGA expenditure as shown in
Model 43a when White (1980) procedure is applied but no significant reaction is noted
in Model 43b when Petersen (2009) method is used to control for time series and cross-
sectional correlation.
These findings provide evidence that a larger amount of private information is available
in small firms, as opposed to large firms, thereby encouraging managerial learning. Thus,
their managers are motivated to be more responsive in changing firms‟ SGA costs level
in the subsequent year. The impact of stock price informativeness on SGA costs level of
the subsequent year is more apparent in small firms that are experiencing declining
stock price informativeness as it is imperative to take appropriate action compared to
those firms that are facing a situation of escalating stock price informativeness.
303
5.4.2 Analyst Following
Table 5.30 examines whether the relationship between a current year‟s stock price
informativeness and the subsequent year‟s R&D level is dependent on analyst following.
Eight models are presented in Table 5.30. These models are Models 45a and 45b for
firms with high analyst following; Models 46a and 46b for firms with low analyst
following; Models 47a and 47b for low analyst following firms with increasing
idiosyncratic volatility; as well as Models 48a and 48b for high analyst following firms
with decreasing idiosyncratic volatility.
304
Table 5.30 Effect of Stock Price Informativeness on R&D Expenditure
– by Analyst Following (H2b)
∑
∑
∑
High Analyst Following
High Analyst Following
(Model 45a)
(Model 45b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept 1.480 4.30 *** 1.616 2.24 *
Ψ 0.028 1.19 0.044 1.23
SIZE -0.667 -7.79 *** -0.673 -3.85 ***
SIZE2 0.034 5.94 *** 0.034 3.02 **
AGE 0.019 0.57 0.019 0.30
StdROE 0.003 2.23 ** 0.003 2.63 **
StdCF 0.000 -0.89 0.000 .
StdRET -3.680 -1.93 * -5.641 -1.56
MB 0.000 0.96 0.000 0.94
ROA -1.082 -8.53 *** -1.084 -8.16 ***
WC 0.000 4.86 *** 0.000 .
DIV -0.610 -13.10 *** -0.616 -6.87 ***
MERGER -0.056 -1.07 -0.060 -0.98
RESTRUCT 0.008 0.22 0.005 0.09
LOSS 0.353 6.67 *** 0.357 5.55 ***
LEV -0.603 -7.15 *** -0.591 -4.06 ***
AUDIT 0.006 0.16 0.005 0.09
EM -0.160 -1.95 * -0.176 -2.11 *
Industries dummies Included Included
Year dummies Included Included
Adjusted R2 0.392 0.390
F-Statistics 84.51 98.19
p-value <0.0001 <0.0001
N 4,025 4,025
Note: Firms with high (low) analyst following are defined as firms above (below) the median value of the full
sample on the number of analysts that follow the firms. All variables are defined in Table 4.1. ***, ** and * indicate
statistical significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a)
heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009)
procedure.
305
Table 5.30 Effect of Stock Price Informativeness on R&D Expenditure
– by Analyst Following (H2b) (Continued)
∑
∑
∑
Low Analyst Following
Low Analyst Following
(Model 46a)
(Model 46b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -1.044 -1.79 * -0.861 -1.08
ψ -0.086 -7.39 *** -0.072 -4.68 ***
SIZE -0.075 -0.80 -0.070 -0.41
SIZE2 -0.029 -3.31 *** -0.029 -1.85
AGE -0.164 -4.90 *** -0.167 -3.37 ***
StdROE 0.003 4.22 ** 0.003 4.41 ***
StdCF 0.001 3.07 *** 0.001 2.54 **
StdRET -0.293 -0.29 -2.222 -1.61
MB 0.002 1.56 0.002 1.56
ROA -0.616 -6.46 *** -0.627 -3.96 ***
WC 0.000 1.57 0.000 .
DIV -0.490 -10.97 *** -0.497 -5.44 ***
MERGER 0.109 1.61 0.082 1.27
RESTRUCT 0.150 4.14 *** 0.140 2.80 **
LOSS 0.483 10.76 *** 0.486 8.18 ***
LEV -0.334 -4.84 *** -0.332 -3.21 **
AUDIT 0.108 2.73 *** 0.105 1.70
EM 0.058 0.66 0.061 0.64
Year dummies Included Included
Industry dummies Included Included
Adjusted R2 0.453 0.450
F-Statistics 120.67 136.81
p-value <0.0001 <0.0001
N 4,488 4,488
Note: Firms with high (low) analyst following are defined as firms above (below) the median value of the full sample
on the number of analysts that follow the firms. All variables are defined in Table 4.1. ***, ** and * indicate
statistical significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a)
heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009)
procedure.
306
Table 5.30 Effect of Stock Price Informativeness on R&D Expenditure
– by Analyst Following (H2b) (Continued)
∑
∑
∑
Low Analyst Following
Low Analyst Following
Increasing ψ
Increasing ψ
(Model 47a)
(Model 47b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -2.590 -5.55 *** -2.272 -4.37 ***
ψ -0.076 -4.94 *** -0.063 -2.93 **
SIZE -0.030 -0.22 -0.022 -0.13
SIZE2 -0.036 -2.74 *** -0.036 -2.65 **
AGE -0.233 -4.58 *** -0.245 -4.56 ***
StdROE 0.003 4.19 *** 0.003 4.85 ***
StdCF 0.002 3.28 *** 0.002 4.81 ***
StdRET 0.791 0.63 -0.605 -0.47
MB 0.003 3.32 *** 0.003 4.03 ***
ROA -0.524 -4.01 *** -0.530 -2.30 *
WC 0.000 1.61 0.000 .
DIV -0.476 -6.68 *** -0.480 -5.03 ***
MERGER 0.230 2.12 ** 0.191 2.39 *
RESTRUCT 0.101 1.83 * 0.090 1.71
LOSS 0.432 6.82 *** 0.434 9.05 ***
LEV -0.352 -3.62 *** -0.357 -2.59 **
AUDIT 0.071 1.14 0.076 1.26
EM 0.260 2.19 ** 0.265 2.70 **
Year dummies Included Included
Industry dummies Included Included
Adjusted R2 0.444 0.440
F-Statistics 50.97 129.19
p-value <0.0001 <0.0001
N 1,945 1,945
Note: Firms with high (low) analyst following are defined as firms above (below) the median value of the full sample
on the number of analysts that follow the firms. All variables are defined in Table 4.1. ***, ** and * indicate statistical
significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a)
heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009)
procedure.
307
Table 5.30 Effect of Stock Price Informativeness on R&D Expenditure
– by Analyst Following (H2b) (Continued)
∑
∑
∑
Low Analyst Following
Low Analyst Following
Decreasing ψ
Decreasing ψ
(Model 48a)
(Model 48b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -0.403 -0.66 -0.177 -0.23
Ψ -0.162 -8.04 *** -0.147 -6.60 ***
SIZE -0.136 -1.16 -0.134 -0.66
SIZE2 -0.026 -2.37 ** -0.026 -1.43
AGE -0.111 -2.57 ** -0.117 -2.24 *
StdROE 0.010 1.86 * 0.010 2.05 *
StdCF 0.001 2.46 ** 0.001 2.16 *
StdRET -2.450 -1.46 -5.535 -2.61 **
MB 0.001 0.80 0.001 0.91
ROA -0.736 -7.23 *** -0.761 -5.86 ***
WC 0.000 1.42 0.000 1.33
DIV -0.524 -9.19 *** -0.535 -5.77 ***
MERGER 0.016 0.19 -0.002 -0.02
RESTRUCT 0.185 3.92 *** 0.177 2.90 **
LOSS 0.473 8.35 *** 0.486 5.85 ***
LEV -0.289 -3.73 *** -0.281 -3.11 **
AUDIT 0.132 2.55 ** 0.122 1.60
EM -0.091 -0.74 -0.072 -0.58
Year dummies Included Included
Industry dummies Included Included
Adjusted R2 0.470 0.466
F-Statistics 73.67 81.88
p-value <0.0001 <0.0001
N 2,543 2,543 Note: Firms with high (low) analyst following are defined as firms above (below) the median value of the full
sample on the number of analysts that follow the firms. All variables are defined in Table 4.1. ***, ** and *
indicate statistical significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted
for (a) heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using the
Petersen (2009) procedure.
308
The results in Table 5.30 show that the coefficient idiosyncratic volatility, ψ is
significantly negative for firms with low analyst following (Models 46a and 46b) but
insignificant positive for firms with high analyst following (Model 45a and 45b). This
suggests that the relationship between stock price informativeness and R&D expenditure
level of the subsequent year is stronger when analyst following is low. Easley et al.
(1998) suggest that analysts introduce more uninformed or noise trading to the stocks.
High analyst following does not seem to generate new private information but in turn
has worsened the private information content in stock prices. Therefore, firms with low
analyst coverage are able to gather higher volume of new firm-specific information from
stock prices and are more responsive in adjusting their R&D expenditure in the
subsequent year, compared to firms with high analyst following.
The sub-sample of low analyst following is further segregated into two groups of firms,
being firms with intensifying idiosyncratic volatility (Models 47a and 47b) and firms
with weakening idiosyncratic volatility (Models 48a and 48b). A stronger relationship
between a current year‟s stock price informativeness and the subsequent year‟s R&D
expenditure level is found in the sub-group of low analyst following firms with
weakening idiosyncratic volatility (Models 48a and 48b), reflected by the greater value
of coefficient idiosyncratic volatility, ψ. This suggests that firms with low analyst
coverage and decreasing stock price informativeness are more responsive to firm-
specific private information by modifying their R&D expenditure.
309
Table 5.31 demonstrates the relationship between a current year‟s stock price
informativeness and the subsequent year‟s CAPEX for firms with high and low analyst
following.
It is observed from the findings presented in Table 5.31 that the association between a
current year‟s idiosyncratic volatility and the subsequent year‟s CAPEX are
insignificant for firms with either high or low analyst following, after controlling for
heteroskedasticity (Models 49a and 50a) as well as for cross-sectional and time series
correlation (Models 49b and 50b). These findings are probably due to non-robust results
revealed in the main model for CAPEX presented in Table 5.18 in item 5.3.1.
310
Table 5.31 Effect of Stock Price Informativeness on CAPEX
– by Analyst Following (H2b)
∑
∑
∑
High Analyst Following
High Analyst Following
(Model 49a)
(Model 49b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -3.300 -12.56 *** -2.881 -6.39 ***
Ψ -0.004 -0.33 0.040 1.28
SIZE -0.102 -1.69 * -0.160 -1.56
SIZE2 0.005 1.45 0.010 1.51
AGE 0.006 0.30 0.002 0.06
StdROE -0.002 -1.77 * -0.002 -2.90 **
StdCF 0.000 3.22 0.000 .
StdRET 2.507 2.04 ** -3.408 -2.78 *
MB 0.000 0.64 0.000 .
ROA -0.576 -3.00 *** -0.485 -1.03
WC 0.000 5.52 *** 0.000 .
DIV 0.091 3.58 *** 0.071 1.49
MERGER -0.228 -7.10 *** -0.252 -7.26 ***
RESTRUCT -0.162 -7.55 *** -0.163 -5.05 ***
LOSS -0.014 -0.35 0.013 0.27
LEV 0.253 4.20 *** 0.259 2.77 *
AUDIT 0.059 2.92 *** 0.063 1.96 *
EM 0.030 0.21 -0.013 -0.17 FCF 2.535 13.20 *** 2.431 4.09 ***
Year dummies Included Included
Industry dummies Included Included
Adjusted R2 0.299 0.288
F-Statistics 97.64 112.32
p-value <0.0001 <0.0001
N 7,259 7,259
Note: Firms with high (low) analyst following are defined as firms above (below) the median value of the full
sample on the number of analysts that follow the firms. All variables are defined in Table 4.1. ***, ** and *
indicate statistical significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted
for (a) heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using the
Petersen (2009) procedure.
311
Table 5.31 Effect of Stock Price Informativeness on CAPEX
– by Analyst Following (H2b) (Continued)
∑
∑
∑
Low Analyst Following
Low Analyst Following
(Model 50a)
(Model 50b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -3.404 -13.45 *** -3.219 -11.87 ***
ψ -0.014 -1.62 0.002 0.14
SIZE 0.025 0.42 0.019 0.28
SIZE2 -0.002 -0.41 -0.002 -0.29
AGE -0.028 -1.41 -0.038 -1.43
StdROE 0.000 4.43 *** 0.000 .
StdCF 0.000 2.50 ** 0.000 2.93 **
StdRET -4.013 -4.76 *** -7.189 -5.71 ***
MB 0.000 -0.57 0.000 -0.30
ROA -0.501 -3.43 *** -0.387 -1.04
WC 0.000 -3.54 *** 0.000 .
DIV 0.128 4.80 *** 0.113 2.63 **
MERGER -0.196 -4.73 *** -0.213 -4.10 ***
RESTRUCT -0.169 -6.84 *** -0.165 -4.33 ***
LOSS -0.220 -6.57 *** -0.213 -7.73 ***
LEV 0.329 5.08 *** 0.337 3.47 **
AUDIT 0.023 0.91 0.027 0.95
EM -0.432 -3.81 *** -0.449 -5.09 ***
FCF 1.079 6.81 *** 0.957 2.82 **
Year dummies Included Included
Industry dummies Included Included
Adjusted R2 0.203 0.195
F-Statistics 62.43 61.27
p-value <0.0001 <0.0001
N 7,707 7,707
Note: Firms with high (low) analyst following are defined as firms above (below) the median value of the full
sample on the number of analysts that follow the firms. All variables are defined in Table 4.1. ***, ** and *
indicate statistical significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are
adjusted for (a) heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using
the Petersen (2009) procedure.
312
Next, Table 5.32 presents the effect of current year‟s stock price informativeness on
subsequent year‟s SGA expenditure with the following models:
a) Firms with high analyst following – Models 51a and 51b.
b) Firms with low analyst following – Models 52a and 52b.
c) Firms with low analyst following and increasing idiosyncratic volatility – Models
53a and 53b.
d) Firms with low analyst following and decreasing idiosyncratic volatility – Models
54a and 54b.
313
Table 5.32 Effect of Stock Price Informativeness on SGA Costs
– by Analyst Following (H2b)
∑
∑
∑
High Analyst Following
High Analyst Following
(Model 51a)
(Model 51b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept 1.173 4.68 *** 1.171 2.42 *
ψ 0.014 1.10 0.001 0.04
SIZE -0.779 -16.52 *** -0.787 -9.99 ***
SIZE2 0.037 12.23 *** 0.038 7.50 ***
AGE 0.136 7.96 *** 0.137 4.39 ***
StdROE 0.001 1.29 0.001 2.21 *
StdCF 0.000 -1.88 * 0.000 .
StdRET -5.204 -4.80 *** -4.291 -2.65 **
MB 0.000 0.75 0.000 .
ROA 0.193 1.87 * 0.205 1.21
WC 0.005 3.88 *** 0.005 2.85 **
DIV -0.101 -4.30 *** -0.100 -2.32 *
MERGER -0.043 -1.54 -0.033 -0.87
RESTRUCT 0.165 8.74 *** 0.165 4.23 ***
LOSS 0.065 2.10 ** 0.063 1.51
LEV -0.156 -3.10 *** -0.148 -1.83
AUDIT 0.081 4.40 *** 0.077 2.09 *
EM -0.583 -5.93 *** -0.595 -3.36 **
EMP 2.378 5.07 *** 2.319 2.46 **
Year dummies Included Included
Industry dummies Included Included
Adjusted R2 0.491 0.490
F-Statistics 202.96 224.70
p-value <0.0001 <0.0001
N 6,708 6,708
Note: Firms with high (low) analyst following are defined as firms above (below) the median value of the full
sample on the number of analysts that follow the firms. All variables are defined in Table 4.1. ***, ** and *
indicate statistical significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted
for (a) heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using the
Petersen (2009) procedure.
314
Table 5.32 Effect of Stock Price Informativeness on SGA Costs
– by Analyst Following (H2b) (Continued)
∑
∑
∑
Low Analyst Following
Low Analyst Following
(Model 52a)
(Model 52b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -0.124 -0.64 0.019 0.06
ψ -0.039 -6.24 *** -0.033 -2.81 **
SIZE -0.453 -10.61 *** -0.455 -5.47 ***
SIZE2 0.011 2.76 *** 0.011 1.38
AGE 0.104 6.49 *** 0.101 3.23 **
StdROE 0.001 3.93 *** 0.001 5.48 ***
StdCF 0.000 1.20 0.000 0.78
StdRET -1.754 -3.12 *** -2.898 -4.08 ***
MB 0.003 2.79 *** 0.003 2.23 *
ROA -0.450 -8.65 *** -0.468 -6.43 ***
WC 0.000 -3.39 *** 0.000 -3.41 **
DIV -0.143 -6.62 *** -0.146 -3.56 **
MERGER 0.072 2.19 ** 0.058 1.62
RESTRUCT 0.242 12.89 *** 0.239 6.69 ***
LOSS 0.053 2.42 ** 0.051 1.61
LEV -0.010 -0.22 0.002 0.03
AUDIT -0.019 -0.89 -0.019 -0.56
EM -0.032 -0.55 -0.033 -0.47 EMP 1.633 4.28 *** 1.608 2.88 **
Year dummies Included Included
Industry dummies Included Included
Adjusted R2 0.445 0.442
F-Statistics 177.90 204.30
p-value <0.0001 <0.0001
N 7,072 7,072
Note: Firms with high (low) analyst following are defined as firms above (below) the median value of the full
sample on the number of analysts that follow the firms. All variables are defined in Table 4.1. ***, ** and *
indicate statistical significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are
adjusted for (a) heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using
the Petersen (2009) procedure.
315
Table 5.32 Effect of Stock Price Informativeness on SGA Costs
– by Analyst Following (H2b) (Continued)
∑
∑
∑
Low Analyst Following
Low Analyst Following
Increasing ψ
Increasing ψ
(Model 53a)
(Model 53b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept 0.197 0.54 0.334 1.27
ψ -0.035 -4.00 *** -0.023 -1.28
SIZE -0.532 -7.91 *** -0.529 -6.51 ***
SIZE2 0.018 2.91 *** 0.018 2.38 *
AGE 0.073 2.81 *** 0.069 2.15 *
StdROE 0.001 3.41 *** 0.001 6.27 ***
StdCF 0.000 -0.45 0.000 -0.56
StdRET -0.866 -1.16 -2.315 -3.09 **
MB 0.003 3.15 *** 0.003 2.67 **
ROA -0.393 -5.88 *** -0.416 -4.07 ***
WC 0.000 0.48 0.000 0.47
DIV -0.128 -3.66 *** -0.131 -2.74 **
MERGER 0.087 1.76 0.054 1.26
RESTRUCT 0.221 7.86 *** 0.223 5.02 ***
LOSS 0.059 1.82 * 0.059 2.26 *
LEV 0.028 0.39 0.027 0.32
AUDIT 0.024 0.74 0.027 0.81
EM 0.057 0.72 0.050 0.45 EMP 1.539 3.38 *** 1.568 2.48 **
Year dummies Included Included
Industry dummies Included Included
Adjusted R2 0.437 0.431
F-Statistics 72.04 83.06
p-value <0.0001 <0.0001
N 2,936 2,936
Note: Firms with high (low) analyst following are defined as firms above (below) the median value of the
full sample on the number of analysts that follow the firms. All variables are defined in Table 4.1. ***, **
and * indicate statistical significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics
are adjusted for (a) heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and
year using the Petersen (2009) procedure.
316
Table 5.32 Effect of Stock Price Informativeness on SGA Costs
– by Analyst Following (H2b) (Continued)
∑
∑
∑
Low Analyst Following
Low Analyst Following
Decreasing ψ
Decreasing ψ
(Model 54a)
(Model 54b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -0.128 -0.54 -0.007 -0.02
ψ -0.064 -6.01 *** -0.061 -4.37 ***
SIZE -0.432 -7.56 *** -0.432 -4.88 ***
SIZE2 0.008 1.47 0.008 0.88
AGE 0.124 6.09 *** 0.121 3.61 **
StdROE 0.005 1.10 0.004 1.04
StdCF 0.000 1.63 0.000 1.18
StdRET -2.751 -3.10 *** -3.656 -2.85 **
MB 0.004 1.48 0.004 1.45
ROA -0.460 -5.82 *** -0.477 -4.90 ***
WC 0.000 -2.43 * -0.001 -3.31 **
DIV -0.160 -5.84 *** -0.162 -4.31 ***
MERGER 0.060 1.36 0.057 1.44
RESTRUCT 0.256 10.18 *** 0.252 5.69 ***
LOSS 0.046 1.53 0.045 1.01
LEV -0.014 -0.23 0.001 0.01
AUDIT -0.051 -1.86 * -0.054 -1.45
EM -0.118 -1.43 -0.110 -1.88 EMP 1.766 2.84 *** 1.694 2.14 **
Year dummies Included Included
Industry dummies Included Included
Adjusted R2 0.450 0.449
F-Statistics 106.89 122.07
p-value <0.0001 <0.0001
N 4,136 4,136 Note: Firms with high (low) analyst following are defined as firms above (below) the median value of the full
sample on the number of analysts that follow the firms. All variables are defined in Table 4.1. ***, ** and *
indicate statistical significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted
for (a) heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using the
Petersen (2009) procedure.
317
The findings presented in Table 5.32 highlight that the association between a current
year‟s stock price informativeness and SGA expenditure of the subsequent year is
significantly negative for firms with low analyst following (Models 52a and 52b) but
insignificantly positive for firms with high analyst following (Model 51a and 51b). The
relationship between a current year‟s stock price informativeness and the subsequent
year‟s SGA is stronger when analyst following is low. This is consistent with Chen et al.
(2007) who conclude firms with low analyst following have a higher sensitivity of
investment-to-price. As such, managerial learning from stock prices is higher for firms
with low analyst following and their firm managers are more enthusiastic in modifying
their SGA expenditure level of the subsequent year in response to stock prices, as
opposed to firms with high analyst following.
Further analyses were performed on the sub-sample of low analyst following firms in
terms of the direction of movement in firm-level idiosyncratic volatility. The coefficient
idiosyncratic volatility, ψ is larger and at higher significance level for firms with low
analyst following and weakening idiosyncratic volatility (Models 54a and 54b),
compared to low analyst following firms experiencing a strengthening idiosyncratic
volatility (Models 53a and 53b). These findings suggest that firms with low analyst
following and decreasing idiosyncratic volatility learn more and react faster to new firm-
specific information by changing their SGA expenditure level in the subsequent year to
enjoy long-term benefits from this corporate expenditure.
318
5.4.3 Bid-ask Spreads
This item demonstrates how an association between current year‟s stock price
informativeness and subsequent year‟s corporate expenditure is dependent on bid-ask
spreads. The findings displayed in Table 5.33 highlight the effect of current year‟s stock
price informativeness on R&D level for the following types of firms:
a) Firms with small bid-ask spreads (Models 55a and 55b).
b) Firms with large bid-ask spreads (Models 56a and 56b).
c) Firms with large bid-ask spreads and increasing idiosyncratic volatility (Models
57a and 57b).
d) Firms with large bid-ask spreads and decreasing idiosyncratic volatility (Models
58a and 58b).
319
Table 5.33 Effect of Stock Price Informativeness on R&D Expenditure
– by Bid-Ask Spreads (H2c)
∑
∑
∑
Small Bid-Ask Spreads
Small Bid-Ask Spreads
(Model 55a)
(Model 55b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept 2.433 6.67 *** 2.434 3.49 *
Ψ 0.019 0.79 0.001 0.02
SIZE -0.924 -10.41 *** -0.923 -4.84 ***
SIZE2 0.039 6.65 *** 0.039 3.20 **
ANALYST 0.069 22.87 *** 0.068 11.78 ***
AGE 0.024 0.82 0.025 0.42
StdROE 0.002 3.18 *** 0.002 3.43 **
StdCF 0.000 -1.77 * 0.000 .
StdRET -10.262 -5.09 *** -8.533 -3.19 **
MB 0.001 1.32 0.001 1.23
ROA -1.034 -7.32 *** -1.020 -4.71 ***
WC 0.000 8.23 *** 0.000 .
DIV -0.469 -11.33 *** -0.471 -6.77 ***
MERGER 0.016 0.32 0.019 0.37
RESTRUCT 0.143 4.22 *** 0.143 2.57 **
LOSS 0.381 7.45 *** 0.373 8.42 ***
LEV -0.492 -6.16 *** -0.493 -3.13 **
AUDIT 0.010 0.32 0.008 0.17
EM -0.066 -0.45 -0.106 -0.86
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.430 0.427
F-Statistics 101.10 133.39
p-value <0.0001 <0.0001
N 4,255 4,255 Note: Firms with high (low) bid ask spreads are defined as firms above (below) the median value of the average
bid-ask spreads of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical
significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a)
heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using the Petersen
(2009) procedure.
320
Table 5.33 Effect of Stock Price Informativeness on R&D Expenditure
– by Bid-Ask Spreads (H2c) (Continued)
∑
∑
∑
Large Bid-Ask Spreads
Large Bid-Ask Spreads
(Model 56a)
(Model 56b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -1.134 -1.81 * -0.992 -1.35
ψ -0.070 -5.97 *** -0.053 -3.28 **
SIZE -0.193 -2.45 ** -0.188 -1.74
SIZE2 -0.020 -2.87 *** -0.021 -2.22 *
ANALYST 0.074 11.94 *** 0.075 12.42 ***
AGE -0.125 -3.65 *** -0.126 -2.35 *
StdROE 0.004 4.14 *** 0.004 3.84 ***
StdCF 0.000 1.72 * 0.000 4.48 ***
StdRET 0.591 0.60 -1.427 -1.02
MB 0.000 0.79 0.000 0.76
ROA -0.540 -5.85 *** -0.542 -3.72 ***
WC 0.000 1.10 0.000 .
DIV -0.467 -9.47 *** -0.478 -4.48 ***
MERGER 0.105 1.49 0.087 1.32
RESTRUCT 0.146 3.92 *** 0.142 3.13 **
LOSS 0.515 11.28 *** 0.532 7.76 ***
LEV -0.230 -3.52 *** -0.222 -2.49 **
AUDIT 0.115 2.86 *** 0.113 1.79
EM 0.045 0.66 0.052 0.81
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.438
0.434
F-Statistics 104.66
108.77
p-value <0.0001
<0.0001
N 4,255 4,255
Note: Firms with high (low) bid ask spreads are defined as firms above (below) the median value of the average
bid-ask spreads of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical
significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a)
heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using the Petersen
(2009) procedure.
321
Table 5.33 Effect of Stock Price Informativeness on R&D Expenditure
– by Bid-Ask Spreads (H2c) (Continued)
∑
∑
∑
Large Bid-Ask Spreads
Large Bid-Ask Spreads
Increasing ψ
Increasing ψ
(Model 57a)
(Model 57b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -2.376 -5.57 *** -2.053 -4.18 ***
ψ -0.068 -4.37 *** -0.055 -2.74 **
SIZE -0.220 -1.80 * -0.206 -1.36
SIZE2 -0.021 -1.94 * -0.022 -1.73
ANALYST 0.086 9.53 *** 0.088 9.43 ***
AGE -0.163 -3.13 *** -0.170 -3.50 **
StdROE 0.003 4.23 *** 0.003 3.22 **
StdCF 0.000 1.39 0.000 2.00 *
StdRET 1.495 1.18 0.310 0.28
MB 0.002 2.85 *** 0.002 1.90
ROA -0.440 -3.52 *** -0.443 -2.08 *
WC 0.000 0.66 0.000 .
DIV -0.492 -6.25 *** -0.503 -5.10 ***
MERGER 0.086 0.80 0.060 0.65
RESTRUCT 0.147 2.59 *** 0.138 2.20 *
LOSS 0.487 7.62 *** 0.507 10.21 ***
LEV -0.262 -2.87 *** -0.257 -1.97 *
AUDIT 0.097 1.56 0.093 1.25
EM 0.260 2.28 ** 0.270 3.55 **
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.439 0.435
F-Statistics 46.13 145.77
p-value <0.0001 <0.0001
N 1,847 1,847
Note: Firms with high (low) bid ask spreads are defined as firms above (below) the median value of the average
bid-ask spreads of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical
significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a)
heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using the Petersen
(2009) procedure.
322
Table 5.33 Effect of Stock Price Informativeness on R&D Expenditure
– by Bid-Ask Spreads (H2c) (Continued)
∑
∑
∑
Large Bid-Ask Spreads
Large Bid-Ask Spreads
Decreasing ψ
Decreasing ψ
(Model 58a)
(Model 58b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -0.693 -1.04 -0.508 -1.03
ψ -0.122 -5.71 *** -0.099 -4.53 ***
SIZE -0.188 -1.84 * -0.184 -1.32
SIZE2 -0.020 -2.13 ** -0.021 -1.70
ANALYST 0.062 7.42 *** 0.062 10.05 ***
AGE -0.092 -2.03 ** -0.094 -1.68
StdROE 0.008 2.03 ** 0.009 2.67 **
StdCF 0.000 1.18 0.000 1.76
StdRET -1.132 -0.68 -4.612 -1.60
MB 0.000 0.69 0.000 0.67
ROA -0.694 -7.23 *** -0.707 -6.66 ***
WC 0.001 1.68 * 0.001 1.57
DIV -0.472 -7.52 *** -0.481 -4.16 ***
MERGER 0.123 1.29 0.110 1.85
RESTRUCT 0.124 2.54 ** 0.124 1.80
LOSS 0.494 8.59 *** 0.518 5.20 ***
LEV -0.171 -2.50 ** -0.164 -2.33 *
AUDIT 0.124 2.32 ** 0.119 1.73
EM -0.076 -0.94 -0.059 -0.86
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.444 0.440
F-Statistics 61.10 64.81
p-value <0.0001 <0.0001
N 2,408 2,408 Note: Firms with high (low) bid ask spreads are defined as firms above (below) the median value of the average
bid-ask spreads of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical
significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a)
heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using the Petersen
(2009) procedure.
323
The findings presented in Table 5.33 highlight a significantly negative relationship
between a current year‟s idiosyncratic volatility and the subsequent year‟s R&D
expenditure level for firms with high bid-ask spreads (Models 56a and 56b). However,
an insignificant association between these variables is observed for firms with low bid-
ask spreads (Models 55a and 55b). Managers of firms with high bid-ask spreads seem to
learn more from the capital markets about firm‟s new private information that managers
have yet to possess, hence they are more induced to initiate changes in R&D
expenditure.
Further analyses on firms with high bid-ask spreads disclose a stronger association
between a current year‟s stock price informativeness and the subsequent year‟s R&D
expenditure in firms with diminishing idiosyncratic volatility (Models 58a and 58b) as
compared to firms with improving informativeness in their stock prices (Models 57a and
57b). Firms with high bid-ask spreads and diminishing idiosyncratic volatility respond
swiftly in modifying their spending in R&D projects in the following year.
Table 5.34 exhibits the association between a current year‟s stock price informativeness
and CAPEX in the subsequent year of firms with small (Models 59a and 59b) and large
bid-ask spreads (Models 60a and 60b).
324
Table 5.34 Effect of Stock Price Informativeness on CAPEX – by Bid-Ask Spreads
(H2c)
∑
∑
∑
Small Bid-Ask Spreads
Small Bid-Ask Spreads
(Model 59a)
(Model 59b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -3.900 -14.43 *** -3.454 -8.87 ***
ψ -0.001 -0.10 0.059 1.72
SIZE -0.022 -0.36 -0.058 -0.63
SIZE2 0.000 0.00 0.001 0.22
ANALYST 0.006 3.25 *** 0.006 1.77
AGE 0.015 0.88 0.010 0.31
StdROE -0.003 -8.08 *** -0.003 -5.16 ***
StdCF 0.000 3.44 *** 0.000 .
StdRET 3.649 2.78 *** -4.185 -2.74 **
MB 0.001 1.90 * 0.001 2.02 *
ROA -0.456 -2.03 ** -0.354 -0.87
WC 0.000 6.32 *** 0.000 .
DIV 0.135 5.69 *** 0.123 3.20 **
MERGER -0.204 -6.82 *** -0.230 -8.87 ***
RESTRUCT -0.165 -7.98 *** -0.172 -6.69 ***
LOSS 0.037 0.91 0.059 1.04
LEV 0.253 4.23 *** 0.291 3.27 **
AUDIT 0.067 3.44 *** 0.073 2.48 **
EM -0.399 -2.73 *** -0.456 -2.88 **
FCF 2.829 12.69 *** 2.682 6.85 ***
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.285 0.271
F-Statistics 91.46 99.74
p-value <0.0001 <0.0001
N 7,481 7,481 Note: Firms with high (low) bid ask spreads are defined as firms above (below) the median value of the average
bid-ask spreads of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical
significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a)
heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using the Petersen
(2009) procedure.
325
Table 5.34 Effect of Stock Price Informativeness on CAPEX – by Bid-Ask Spreads
(H2c) (Continued)
∑
∑
∑
Large Bid-Ask Spreads
Large Bid-Ask Spreads
(Model 60a)
(Model 60b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -3.297 -10.27 *** -3.163 -8.94 ***
ψ -0.010 -1.11 0.003 0.30
SIZE 0.089 1.67 * 0.088 1.37
SIZE2 -0.009 -2.20 ** -0.010 -2.14 *
ANALYST 0.021 5.88 *** 0.021 6.79 ***
AGE -0.022 -1.05 -0.028 -0.93
StdROE 0.000 4.74 *** 0.000 .
StdCF 0.000 -0.32 0.000 .
StdRET -3.747 -4.58 *** -6.207 -4.93 ***
MB 0.000 -0.46 0.000 .
ROA -0.521 -3.69 *** -0.426 -1.09
WC 0.000 -3.50 *** 0.000 .
DIV 0.092 3.21 *** 0.075 1.54
MERGER -0.211 -4.56 *** -0.221 -4.29 ***
RESTRUCT -0.148 -5.70 *** -0.137 -4.40 ***
LOSS -0.227 -6.80 *** -0.219 -8.85 ***
LEV 0.395 5.96 *** 0.396 4.06 ***
AUDIT 0.004 0.15 0.007 0.23
EM -0.205 -1.64 -0.216 -3.14 **
FCF 1.069 6.98 *** 0.968 2.49 **
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.220
0.214
F-Statistics 64.75
67.62
p-value <0.0001
<0.0001
N 7,481 7,481
Note: Firms with high (low) bid ask spreads are defined as firms above (below) the median value of the average
bid-ask spreads of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical
significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a)
heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using the Petersen
(2009) procedure.
326
The findings reported in Table 5.34 highlight that the association between a current
year‟s stock price informativeness and the subsequent year‟s CAPEX are insignificant
for both firms with small (Models 59a and 59b) and large bid-ask spreads (Models 60a
and 60b) by applying White (1980) and Petersen (2009) procedures respectively . This is
consistent with the non-robust regression results reported in firms with varying firm
sizes (Table 5.28) and analyst followings (Table 5.31).
Table 5.35 presents the relationship between a current year‟s stock price informativeness
and SGA expenditure in the subsequent year in firms with small and large bid-ask
spreads. The following models are reported in this table:
a) Firms with small bid-ask spreads (Models 61a and 61b).
b) Firms with large bid-ask spreads (Models 62a and 62b).
c) Firms with large bid-ask spreads and increasing idiosyncratic volatility (Models
63a and 63b).
d) Firms with large bid-ask spreads and decreasing idiosyncratic volatility (Models
64a and 64b).
327
Table 5.35 Effect of Stock Price Informativeness on SGA Costs
– by Bid-Ask Spreads (H2c)
∑ ∑ ∑
Small Bid-Ask Spreads
Small Bid-Ask Spreads
(Model 61a)
(Model 61b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept 1.324 5.57 *** 1.185 2.70 **
ψ 0.061 4.46 *** 0.028 1.32
SIZE -0.830 -16.59 *** -0.823 -9.13 ***
SIZE2 0.037 11.68 *** 0.037 6.39 ***
ANALYST 0.020 12.77 *** 0.019 5.73 ***
AGE 0.150 8.98 *** 0.152 5.14 ***
StdROE 0.001 1.35 0.001 1.47
StdCF 0.000 -2.96 *** 0.000 .
StdRET -8.448 -7.25 *** -4.681 -4.35 ***
MB 0.000 0.49 0.000 0.47
ROA 0.356 2.68 *** 0.371 1.98 *
WC 0.000 -0.56 0.000 -0.56
DIV -0.104 -4.65 *** -0.104 -2.81 **
MERGER -0.007 -0.24 0.008 0.24
RESTRUCT 0.194 10.45 *** 0.200 5.38 ***
LOSS 0.077 2.33 ** 0.067 1.28
LEV 0.082 1.57 0.076 0.81
AUDIT 0.053 2.91 *** 0.047 1.56
EM -0.477 -4.82 *** -0.488 -3.93 ***
EMP 2.515 8.22 ***
2.483 3.77 ***
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.450 0.446
F-Statistics 171.99 170.06
p-value <0.0001 <0.0001
N 6,888 6,888 Note: Firms with high (low) bid ask spreads are defined as firms above (below) the median value of the average
bid-ask spreads of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical
significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a)
heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using the Petersen
(2009) procedure.
328
Table 5.35 Effect of Stock Price Informativeness on SGA Costs
– by Bid-Ask Spreads (H2c) (Continued)
∑ ∑ ∑
Large Bid-Ask Spreads
Large Bid-Ask Spreads
(Model 62a)
(Model 62b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -0.429 -1.57 -0.293 -0.82
ψ -0.039 -6.43 *** -0.032 -2.81 **
SIZE -0.443 -12.30 *** -0.449 -9.18 ***
SIZE2 0.006 1.99 ** 0.007 1.53
ANALYST 0.026 8.91 *** 0.027 3.85 ***
AGE 0.111 6.90 *** 0.107 3.81 ***
StdROE 0.001 2.93 *** 0.001 3.21 **
StdCF 0.000 3.86 *** 0.000 .
StdRET -0.816 -1.46 -2.038 -3.09 **
MB 0.000 -0.56 0.000 .
ROA -0.450 -8.99 *** -0.461 -5.61 ***
WC -0.001 -0.95 -0.001 -0.78
DIV -0.126 -5.56 *** -0.130 -2.83 **
MERGER 0.048 1.41 0.031 1.05
RESTRUCT 0.222 11.73 *** 0.221 6.60 ***
LOSS 0.075 3.47 *** 0.076 1.71
LEV 0.038 0.83 0.046 0.62
AUDIT 0.013 0.64 0.014 0.50
EM -0.026 -0.45 -0.023 -0.48 EMP 1.255 3.69 ***
1.233 2.59 **
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.505
0.503
F-Statistics 213.98
218.37
p-value <0.0001
<0.0001
N 6,889 6,889
Note: Firms with high (low) bid ask spreads are defined as firms above (below) the median value of the average
bid-ask spreads of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical
significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a)
heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using the Petersen
(2009) procedure.
329
Table 5.35 Effect of Stock Price Informativeness on SGA Costs
– by Bid-Ask Spreads (H2c) (Continued)
∑ ∑ ∑
Large Bid-Ask Spreads
Large Bid-Ask Spreads
Increasing ψ
Increasing ψ
(Model 63a)
(Model 63b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -0.806 -1.38 -0.654 -1.04
ψ -0.031 -3.63 *** -0.024 -1.50
SIZE -0.430 -7.62 *** -0.432 -6.54 ***
SIZE2 0.005 0.91 0.005 0.72
ANALYST 0.024 5.86 *** 0.025 2.96 **
AGE 0.086 3.40 *** 0.082 2.63 **
StdROE 0.001 2.72 *** 0.001 3.66 **
StdCF 0.000 3.45 *** 0.000 .
StdRET 0.027 0.04 -1.056 -1.48
MB 0.006 3.18 *** 0.006 2.11 *
ROA -0.441 -6.82 *** -0.452 -5.12 ***
WC 0.000 0.49 0.001 0.55
DIV -0.106 -2.97 *** -0.111 -2.11 *
MERGER -0.010 -0.20 -0.033 -0.75
RESTRUCT 0.215 7.65 *** 0.217 6.84 ***
LOSS 0.069 2.18 ** 0.073 1.72
LEV 0.045 0.63 0.046 0.65
AUDIT 0.081 2.59 ** 0.080 2.60 **
EM 0.101 1.27 0.105 1.76 EMP 1.222 2.88 *** 1.181 1.45
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.529 0.527
F-Statistics 99.15 99.27
p-value <0.0001 <0.0001
N 2,883 2,883
Note: Firms with high (low) bid ask spreads are defined as firms above (below) the median value of the average
bid-ask spreads of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical
significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a)
heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using the Petersen
(2009) procedure.
330
Table 5.35 Effect of Stock Price Informativeness on SGA Costs
– by Bid-Ask Spreads (H2c) (Continued)
∑ ∑ ∑
Large Bid-Ask Spreads
Large Bid-Ask Spreads
Decreasing ψ
Decreasing ψ
(Model 64a)
(Model 64b)
White-adjusted
Petersen clustering
Coefficient t-statistic Coefficient t-statistic
Intercept -0.207 -0.67 -0.075 -0.25
ψ -0.063 -6.06 *** -0.054 -3.86 ***
SIZE -0.452 -9.36 *** -0.456 -7.35 ***
SIZE2 0.007 1.65 0.008 1.32
ANALYST 0.026 6.37 *** 0.027 3.99 ***
AGE 0.127 6.19 *** 0.123 4.17 ***
StdROE 0.003 0.88 0.003 0.77
StdCF 0.000 2.46 ** 0.000 2.46 **
StdRET -1.905 -2.17 ** -3.279 -3.44 **
MB 0.000 -1.92 * 0.000 .
ROA -0.462 -5.98 *** -0.476 -4.23 ***
WC -0.014 -2.90 *** -0.014 -2.10 *
DIV -0.149 -5.13 *** -0.152 -3.24 **
MERGER 0.091 2.00 ** 0.079 2.44 *
RESTRUCT 0.228 8.96 *** 0.226 5.77 ***
LOSS 0.080 2.71 *** 0.081 1.53
LEV -0.016 -0.25 -0.006 -0.06
AUDIT -0.036 -1.30 -0.036 -0.95
EM -0.114 -1.42 -0.107 -2.21 *
EMP 1.280 2.63 *** 1.256 2.55 **
Year dummies Included
Included
Industry dummies Included
Included
Adjusted R2 0.491 0.490
F-Statistics 118.27 126.87
p-value <0.0001 <0.0001
N 4,006 4,006 Note: Firms with high (low) bid ask spreads are defined as firms above (below) the median value of the
average bid-ask spreads of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate
statistical significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for
(a) heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using the
Petersen (2009) procedure.
331
The findings reported in Table 5.35 highlight a significant negative relationship between
a current year‟s stock price informativeness and subsequent year‟s SGA costs level for
firms with large bid ask spreads (Models 62a and 62b). The coefficient idiosyncratic
volatility, ψ is significantly positive for firms with small bid-ask spreads in Model 61a
(White-adjusted) but become insignificantly positive in Model 61b after controlling for
time series and cross-sectional correlations (referred to as Petersen clustering procedure).
This shows the relationship between a current year‟s stock price informativeness and the
subsequent year‟s SGA expenditure level is generally stronger for firms possessing large
bid-ask spreads. This finding also suggests managers of these firms are guided by new
firm-specific private information they have learned from the capital markets in making
decisions in SGA expenditure. Moreover, a stronger association between a current
year‟s idiosyncratic volatility and the subsequent year‟s SGA expenditure is observed in
firms possessing high bid-ask spreads when they experience declining informativeness
in their stock prices (Models 64a and 64b). It appears that these firms react more
“aggressively” in response to firm-specific private information obtained from stock
prices by changing their spending in SGA of the subsequent year.
5.4.4 Summary of Findings – Hypotheses 2a to 2c
Hypotheses 2a to 2c of this study examine whether the association between a current
year‟s stock price informativeness and the subsequent year‟s corporate expenditure is
332
more likely to be stronger for firms with small firm size, low analyst following and with
large bid-ask spreads respectively.
The above results show a significant inverse relationship between a current year‟s stock
price informativeness and the subsequent year‟s R&D expenditure is stronger in small
firms and in firms with low analyst following as well as in firms possessing large bid-
ask spreads. It is also demonstrated in these firms that the effect of stock price
informativeness on R&D expenditure is greater when the stock price informativeness is
weakening compared to firms with strengthening informativeness of their stock prices.
The results are similar when the corporate expenditure is proxied by SGA expenditure.
As such, it is concluded that Hypotheses 2a to 2c are well supported when corporate
expenditure is represented by R&D and SGA.
The relationship between a current year‟s stock price informativeness and the
subsequent year‟s CAPEX is, however, insignificant when firms are analysed by their
firm size, analyst following and bid-ask spreads. Thus, Hypotheses 2a to 2c are not
supported when corporate expenditure is represented by CAPEX.
5.5 Additional Tests
The following additional tests are conducted to verify the robustness of the model
applied in this study.
333
5.5.1 Different Measures of Idiosyncratic Volatility
The current measure of idiosyncratic volatility is generated using the value-weighted
market return. Alternative measures of idiosyncratic volatility outlined in item 4.5.2 are
used to examine the robustness of the current measure used:
a) Use different market index
The equally-weighted market return is used to replace value-weighted market return to
generate different measures of idiosyncratic volatility (Gul et al., 2011a).
b) Use Fama & French three-factor model (Fama & French, 1993, 1995, 1996)
Instead of the one-factor model used in the current study, the Fama & French three-
factor model (Fama & French, 1993, 1995, 1996) is used to estimate idiosyncratic
volatility by obtaining the value of (1-R2) from the regression indicated in equation 5.5.
(5.5)
where:
is the daily excess return of stock i in day t, is the daily value-weighted excess
market return, is the small-minus-big size factor return, and is the high-
minus-low book-to-market factor return.
334
c) Use Brockman and Yan‟s (2009) model
In Brockman and Yan (2009) model, the idiosyncratic volatility is estimated by
regressing firms‟ daily return on contemporaneous and lagged daily market return, as
well as contemporaneous and lagged daily industry return for each firm-year
observation, as indicated in Equation 5.6.
(5.6)
where:
is the daily excess return of stock i in day t, is the contemporaneous daily value-
weighted market return, is the lagged daily value-weighted market return,
is the contemporaneous daily industry return and is the lagged daily industry
return. The industry return for a specific day is created using all firms with the same
two-digit SIC codes.
The results (untabulated) using these measures of idiosyncratic volatility are essentially
the same as those presented in Tables 5.17 to 5.35, indicating the empirical evidence
presented is robust and not methodology-specific.
5.5.2 Controlling for the Effect of Global Financial Crisis
The data of year 2008 is excluded from the sample to examine whether the results
presented are driven by the effect of global financial crisis. The results (untabulated) of
this smaller sample remain robust and significant as those presented in Tables 5.17 to
335
5.35. This indicates the empirical evidence presented is not affected by the impact of
global financial crisis in year 2008.
5.5.3 Controlling for Time-Series and Cross-Sectional Correlations
All tests are conducted using the Fama and MacBeth (1973) procedure to control for
possible correlation in the cross-sectional error structure, as well as the Newey-West
corrected Fama-Macbeth (FM-NW) procedure to mitigate time series autocorrelation.
The results (untabulated) are qualitatively similar to those reported in Tables 5.17 and
5.35.
5.6 Chapter Summary
This chapter presents the research findings and deliberates the results that were obtained.
The univariate and multivariate results reported provide insights with regards to the
association between a current year‟s stock price informativeness and the subsequent
year‟s corporate expenditure proxied by R&D expenditure, CAPEX and SGA costs.
This chapter also exhibits how this association between a current year‟s stock price
informativeness and the subsequent year‟s corporate expenditure is dependent on
information asymmetry, proxied by firm size, analyst following and bid-ask spreads.
336
The subsequent chapter (Chapter 6) presents the conclusions of this study by
summarising the major findings. It also outlines the limitations of this study and
provides the future research directions.
337
CHAPTER 6
CONCLUSION
6.1 Introduction
This study examines the relationship between stock price informativeness and corporate
expenditure and evaluates whether this relationship is dependent on information
asymmetry. The thesis consists of six chapters. Chapter 1 provides the background
information representing the foundation of this research while Chapter 2 evaluates the
extant literature on stock price informativeness and corporate expenditure. Chapter 3
provides an overview of empirical literature on the theories underpinning the current
study and also outlines the theoretical framework of the current study. Four hypotheses
are developed to predict the association between stock price informativeness and
corporate expenditure as well as to determine whether the relationship is dependent on
information asymmetry. Chapter 4 presents the research methodology adopted and
further outlines the procedures employed to examine the hypotheses developed in this
study. Chapter 5 exhibits the research findings followed by a discussion of the results of
the current study.
After providing an outline of each of the chapters (Chapters 1 to 5) in Section 6.1,
Section 6.2 presents a summary of major findings by reference to the research objectives
338
and hypotheses of this study, together with the research implication. The contributions
of this study are highlighted in Section 6.3, followed by an outline of the limitations of
the study in Section 6.4 and recommendations for future research directions in Section
6.5. Section 6.6 presents the conclusion of this thesis.
6.2 Summary of Key Research Findings
In this section, the key research findings are elaborated in relation to the two research
objectives and four hypotheses developed for this study.35
Table 6.1 provides a summary
of research objectives, corresponding hypotheses and research findings. These research
findings are based on the research model outlined in item 3.4. The implications from the
research findings are also presented accordingly.
35 Refer to item 1.3 for the two research objectives of this study as well as items 3.2 and 3.3 for the four
corresponding hypotheses.
339
Table 6.1 Summary of Research Objectives, Corresponding Hypotheses and Research Findings
Research Objectives Hypotheses Findings
R&D
Expenditure
Capital
Expenditure
SGA
Costs
1. To investigate how firm-level
corporate expenditure responds
to stock price informativeness.
H1: The stock price informativeness of
a current year is negatively
associated with corporate
expenditure in the subsequent year,
ceteris paribus.
Supported Not
supported
Supported
2. To assess whether information
asymmetry plays a role in the
relationship between stock price
informativeness and corporate
expenditure.
H2a: The negative relationship between a
current year‟s stock price
informativeness and the subsequent
year‟s corporate expenditure is
likely to be stronger for small
firms, ceteris paribus.
Supported Not
supported
Supported
H2b: The negative relationship between a
current year‟s stock price
informativeness and the subsequent
year‟s corporate expenditure is
likely to be stronger for firms with
low analyst following, ceteris
paribus.
Supported Not
supported
Supported
H2c: The negative relationship between a
current year‟s stock price
informativeness and the subsequent
year‟s corporate expenditure is
more likely to be stronger for firms
with high bid-ask spreads, ceteris
paribus.
Supported Not
supported
Supported
340
6.2.1 Stock Price Informativeness and Corporate Expenditure
The first research objective of this study concerns the association between stock price
informativeness and corporate expenditure. Specifically, this study‟s first research
objective is stated as follows:
“To investigate how firm-level corporate expenditure responds to stock price
informativeness.”
Learning theory suggests managers apply new private information gathered from their
own stock prices to make their decisions in respect of corporate resource allocation (Luo,
2005; Chen et al., 2007; Frésard, 2012). It is posited in this study that a low level of
stock price informativeness is more likely to motivate firm managers to increase their
corporate expenditure in three specific areas, namely, R&D expenditure, capital
expenditure and SGA costs. In view of the significance of these corporate expenditures
to firm performance as suggested by prior studies, firm managers intend to convey
positive signals to the capital markets about firms‟ potential cash flows and earnings,
thereby increasing investors‟ confidence in respect of firms‟ future performance. On the
other hand, it is argued that firm managers are more likely to maintain a relatively low
level of corporate expenditure when the stock price informativeness is at a high level.
This is because investors are already well informed of firms‟ future cash flows and
prospects through firms‟ current prices when their stock prices are more informative.
Moreover, high stock price informativeness indicates high efficiency of resource
341
allocation in these firms (Durnev et al., 2003). Hence, Hypothesis 1 postulates that stock
price informativeness of the current year is negatively associated with corporate
expenditure in the subsequent year, ceteris paribus.
This study finds a significant negative link between a current year‟s stock price
informativeness and the subsequent year‟s R&D expenditure and SGA costs after
considering endogeneity, providing support to Hypothesis 1 of the study. The impact of
stock price informativeness on the level of subsequent year‟s R&D expenditure and
SGA costs is greater (lower) when firms‟ idiosyncratic volatility is weakening
(strengthening) from the previous year. However, it is observed that there is no
relationship between a current year‟s stock price informativeness and CAPEX in the
subsequent year, indicating that Hypothesis 1 is not supported when corporate
expenditure is proxied by CAPEX.
To overcome endogeneity concern, a change model is used to analyse the relationship
between changes in a current year‟s stock price informativeness and changes in the
subsequent year‟s corporate expenditure. The results show that as firm-level stock price
informativeness strengthens from the previous year, a change in the current year‟s
idiosyncratic volatility is positively associated to the changes in R&D expenditure and
SGA costs in the subsequent year. This suggests that when stock price informativeness
improves, firm managers obtain positive feedback provided by the stock markets and
342
thereby increasing R&D expenditure and SGA costs in the subsequent year to better
enjoy the benefit expected from these corporate expenditures.
When stock price informativeness is weakening, there is no immediate managerial
reaction in modifying R&D expenditure and SGA costs. This asymmetric cost response
is partly due to the cost “stickiness” behaviour of firm managers. These managers may
need to evaluate whether the declining stock price informativeness is temporary or long-
term in nature, and they are thus reluctant to increase corporate expenditure when
idiosyncratic volatility worsens. It is observed that firm managers merely react when it
is crucial for them to increase R&D expenditure and SGA costs, that is, when there is a
drop in relative idiosyncratic volatility (1-R2) by 20 per cent. However, the change
model reveals an insignificant relationship between changes in idiosyncratic volatility
and changes in CAPEX in the subsequent year when stock price informativeness is
either strengthening or weakening. The finding of asymmetric cost response in R&D
and SGA expenditures is consistent with the cost “stickiness” behaviour demonstrated in
past studies such as Anderson et al. (2003), Balakrishnan, Petersen and Soderstrom
(2004), Balakrishnan and Gruca (2008) and Dalla Via and Perego (Forthcoming).
These findings on R&D and SGA are consistent with the learning theory that firm
managers learn important and new firm-specific information from their own stock prices
and they integrate this information to make appropriate corporate expenditure decisions.
These findings are robust and significant when examining using varying measures of
343
idiosyncratic volatility as well as after controlling for time series and cross-sectional
error structure correlations. The insignificant relationship between idiosyncratic
volatility and CAPEX shows that firm managers do not rely on feedback derived from
the capital markets to determine their capital investments. In this study, it is conceived
that firm managers are more likely to thoroughly plan their long-term needs by
considering other factors such as internal cash flow, return on investment and board
approval before investing in capital projects.
Thus, Hypothesis 1 is well supported when corporate expenditure is represented by
R&D expenditure and SGA costs, but not by capital expenditure.
6.2.2 The Role of Information Asymmetry
The second research objective of this study is to determine whether the relationship
between a current year‟s stock price informativeness and corporate expenditure of the
subsequent year is dependent on information asymmetry. Specifically, this study‟s
research objective is stated as follows:
“To assess whether information asymmetry plays a role in the relationship
between stock price informativeness and corporate expenditure.”
Three proxies of information asymmetry are used in this study, namely, firm size,
analyst following and bid-ask spreads. Large firms and firms with high analyst
344
following as well as firms with low bid-ask spreads are generally associated with less
severe information asymmetry problem. However, large firms produce a large amount
of public information through public announcements or financial disclosures (Atiase,
1985; Bhushan, 1989a) while analysts are more likely to rely on firm managers in
generating information (Agrawal et al., 2006). Applying ideas of the learning theory,
this information is not new as it has already been used by firm managers‟ in their past
investment decisions and is therefore unlikely to have any effect on firms‟ corporate
expenditure decisions. Further, analysts generate more industry and market-level
information (Piotroski & Roulstone, 2004) and introduce uninformed trading to the
stock markets (Easley et al., 1998). This reduces private information contents in stock
prices and discourages managerial learning as well as prompt responses of firm
managers in making their corporate decisions.
On the other hand, recent empirical research exhibits that a greater amount of private
information is produced in small firms (Chen et al., 2007; Bakke & Whited, 2010) and
in firms with low analyst following (Chen et al., 2007) as well as in firms with high bid-
ask spreads (Chan et al., 2013). This enables more intensive learning of firm managers
from the stock markets leading to more aggressive responses in adjusting firms‟
corporate expenditure.
Three separate hypotheses are formulated to examine whether the association between a
current year‟s stock price informativeness and the subsequent year‟s corporate
345
expenditure is dependent on firm size, analyst following and bid-ask spreads
respectively.
Hypothesis 2a (H2a) posits that the relationship between a current year‟s stock price
informativeness and the subsequent year‟s corporate expenditure is likely to be stronger
for small firms, ceteris paribus. The findings of this study indicate that the inverse
relationship between a current year‟s stock price informativeness and the subsequent
year‟s R&D expenditure and SGA costs are stronger in small firms. The effect of stock
price informativeness on the level of R&D expenditure and SGA costs is greater
(smaller) in small firms when their idiosyncratic volatility is weakening (strengthening).
However, no significant relationship is noted between a current year‟s stock price
informativeness and the subsequent year‟s CAPEX for both small and big firms.
Therefore, H2a hypothesizes that the association between a current year‟s stock price
informativeness and the subsequent year‟s corporate expenditure is dependent on
information asymmetry proxied by firm size, and it is well supported when corporate
expenditure is represented by R&D expenditure and SGA costs. The findings of the
results show that H2a is not supported when corporate expenditure is represented by
CAPEX.
Hypothesis 2b (H2b) posits that the relationship between a current year‟s stock price
informativeness and the subsequent year‟s corporate expenditure is likely to be stronger
346
for firms with low analyst following, ceteris paribus. The findings of this study reveal
that the inverse relationship between a current year‟s stock price informativeness and the
subsequent year‟s R&D expenditure and SGA costs are stronger in firms with low
analyst following. The sub-samples of low analyst following are further segregated into
two sub-groups, namely, increasing and decreasing idiosyncratic volatilities. It was then
found that the impact of stock price informativeness on R&D expenditure and SGA
costs is greater (lower) in firms with low analyst following when their idiosyncratic
volatility is weakening (strengthening). Nevertheless, the association between a current
year‟s idiosyncratic volatility and CAPEX of the subsequent year are insignificant for
both firms with high and low analyst following.
Consequently, H2b which posits that information asymmetry represented by analyst
following plays a role in the connection between a current year‟s stock price
informativeness and the subsequent year‟s corporate expenditure is well supported when
corporate expenditure is represented by R&D expenditure and SGA costs, but not when
it is represented by CAPEX.
Hypothesis 2c (H2c) postulates that the relationship between a current year‟s stock price
informativeness and the subsequent year‟s corporate expenditure is likely to be stronger
for firms with high bid-ask spreads, ceteris paribus. It is observed that the inverse
relationship between a current year‟s stock price informativeness and the subsequent
year‟s R&D expenditure and SGA costs are stronger in firms with high bid-ask spreads.
347
In addition, the impact of stock price informativeness on R&D expenditure and SGA
costs is greater (smaller) in these firms when their idiosyncratic volatility is weakening
(strengthening). On the other hand, the relationship between a current year‟s stock price
informativeness and the subsequent year‟s CAPEX is insignificant for both firms with
low and high bid-ask spreads.
Therefore, H2c which hypothesizes that the relationship between a current year‟s stock
price informativeness and the subsequent year‟s corporate expenditure is dependent on
information asymmetry proxied by bid-ask spreads, is well supported when corporate
expenditure is represented by either R&D expenditure or SGA costs. H2c is not
supported when corporate expenditure is proxied by CAPEX.
Small firms and firms with low analyst following as well as firms with high bid-ask
spread are expected to be linked to higher information asymmetry. However, a greater
volume of private information that is available in these firms facilitates better
managerial learning of information that managers have yet to possess from firms‟ stock
prices. Consistent with the learning theory and information asymmetry theory, managers
from these firms learn new firm-specific information from the capital markets and are
induced to respond more quickly, thereby making changes to firms‟ R&D expenditure
and SGA costs in the subsequent year when stock price informativeness of a current
year changes. Nevertheless, insignificant changes are observed in firms‟ CAPEX in the
subsequent year when the stock price informativeness of a current year changes
348
indicating non-applicability of the learning theory and information asymmetry theory
when corporate expenditure is represented by CAPEX.
6.3 Contributions of the Study
This study contributes to the body of knowledge on stock price informativeness,
corporate expenditure and information asymmetry in terms of its findings. There are
four significant contributions in this study.
First, the empirical findings of this study contribute to the literature of learning theory. It
provides evidence that a current year‟s stock price informativeness is associated with the
subsequent year‟s corporate expenditure in R&D expenditure and SGA costs. This
implies that firm managers learn from stock prices of their own firms and react by
changing R&D and SGA costs. This study also finds that a current year‟s idiosyncratic
volatility is not associated with CAPEX of the subsequent year, signifying that
managerial learning is not associated with capital decisions.
Second, unlike previous studies that examine the impact of stock price informativeness
on the sensitivity of investment-to-price (Chen et al., 2007; Foucault & Frésard, 2012)
or cash savings-to-price (Frésard, 2012), this study provides direct evidence on how a
current year‟s stock price informativeness affects the level of three types of corporate
expenditures (R&D expenditure, CAPEX and SGA costs) as well as their changes in
349
the subsequent year. It fills the gap of the extant literature by linking stock price
informativeness to corporate expenditure. Further, this research seeks to find out how
firm managers react by changing their corporate expenditure in the subsequent year
when stock price informativeness strengthens as well as when it weakens during the
current year. Hence, this study provides meaningful insights and understanding on how
firms modify their corporate expenditure in the subsequent year in response to changes
in stock price informativeness during the current year. This study also finds that
idiosyncratic volatility of a current year is not associated with CAPEX in the subsequent
year, indicating that stock price informativeness is not a key determinant of CAPEX.
Third, the current study adds to the management accounting literature by presenting
useful insights on the cost “stickiness” behaviour of firm managers in changing R&D
expenditure and SGA costs. The findings of the study reveal that when firm-level stock
price informativeness is strengthening, a change in the current year‟s idiosyncratic
volatility is positively associated to the changes in R&D expenditure and SGA costs of
the following year. However, when stock price informativeness is weakening, there is
no immediate managerial reaction in modifying R&D and SGA expenditure. This
disproportionate change in corporate expenditure arising from changes in idiosyncratic
volatility is partly caused by the cost “stickiness” behaviour of firm managers. These
managers may need to evaluate the nature of declining stock price informativeness,
hence their reluctance to increase corporate expenditure when idiosyncratic volatility
deteriorates. Firm managers are only motivated to respond when conditions become
350
crucial, that is, when the relative idiosyncratic volatility (1-R2) drops significantly by 20
per cent.
Fourth, this research contributes to the information asymmetry literature by examining
whether the relationship between a current year‟s stock price informativeness and
corporate expenditure in the subsequent year is dependent on information asymmetry.
Three proxies of information asymmetry, namely, firm size, analyst following and bid-
ask spreads are deployed in this study. Small firms, firms with low analyst following
and firms with high bid-ask spreads are expected to be associated with higher
information asymmetry. Extant empirical studies, however, show that greater amount of
private information is available in these firms (Chen et al., 2007; Bakke & Whited,
2010). This study observes that the relationship between stock price informativeness and
corporate expenditure represented by R&D expenditure and SGA costs is stronger in
small firms and firms with low analyst following as well as firms with high bid-ask
spreads. This implies that greater extent of private information facilitates better
managerial learning by observing firms‟ stock prices. Managers of these firms are
motivated to respond more “aggressively” by changing corporate expenditure when
stock price informativeness changes. This study further finds that the relationship
between stock price informativeness and corporate expenditure represented by R&D
expenditure and SGA costs of these firms are stronger (weaker) when idiosyncratic
volatility is declining (strengthening). These research findings shed some light on the
roles of both information asymmetry and the direction of idiosyncratic volatility
351
(weakening or strengthening) in determining the relationship between stock price
informativeness and corporate expenditure.
6.4 Limitations of the Study
This study has empirically assessed the relationship between stock price informativeness
and corporate expenditure as well as how this relationship is dependent on information
asymmetry. The findings of this study have contributed to the body of knowledge on
stock price informativeness, corporate expenditure and information asymmetry.
However, it is important to acknowledge some of the limitations of this study and many
of them represent opportunities for future research.
a) Generalization of result findings
The sample obtained is from US public listed companies (PLCs) for the years 2003 to
2009. Consequently, the results of this study should not be extrapolated to other time
periods due to different capital market conditions arising from market booming or
contraction, as well as varying business cycles brought about by economic expansion or
recession. Similarly, generalization of these results to other countries should be
interpreted with caution due to different institutional settings. In addition, the findings
on change model in item 5.3.2.1 should be interpreted with care as the corresponding
sample size is much smaller when sub-samples of increasing and decreasing
idiosyncratic volatilities are formed.
352
b) Measure of stock price informativeness
In this study, stock price informativeness is measured by idiosyncratic volatility, as
guided by previous theoretical and empirical literature (Morck et al., 2000; Durnev et al.,
2004; Jin & Myers, 2006; Ferreira & Laux, 2007). The strength of interpreting the
research findings by relating stock price informativeness to corporate expenditure
decisions is mainly dependent on whether idiosyncratic volatility adequately captures
private information in the stock prices. To mitigate this concern, robustness tests are
carried out by using alternative measures of idiosyncratic volatility generated using
different models.36
Nevertheless, the measure of stock price informativeness is
acknowledged as one of the major limitations of this study.
c) Probability of Informed Trading measure not used
Previous empirical research use the measure of Probability of Informed Trading (PIN)
as a proxy for stock price informativeness (Chen et al., 2007; Ferreira & Laux, 2007;
Ferreira et al., 2011). The PIN measure developed by Easley, Kiefer and O‟Hara (1996)
is found to be associated with corporate decisions (Chen et al., 2007) and corporate
governance in the form of openness to the market for corporate control (Ferreira & Laux,
2007). However, PIN measure is not applied in this study to examine the hypotheses
developed due to non-availability of data.
36 Refer item 5.5.1 for additional tests carried out using different measures of idiosyncratic volatility.
353
d) Mechanism of the learning theory
In line with the learning theory, this study attributes the association between stock price
informativeness and corporate expenditure decisions to managers learning new firm-
specific information from stock prices. Prior studies suggest firm managers may not
possess information such as growth opportunities, future demand of firms‟ products,
strategic competition, relationships with firms‟ stakeholders, as well as financing
opportunities (Dow & Gorton, 1997; Subrahmanyam & Titman, 1999). However, the
kind of information managers have exactly learned from their stock prices is
unobservable. It is also difficult to perceive from secondary data on how this
information learned from firms‟ stock prices is translated into corporate expenditure
decisions. This constitutes one of the limitations of utilising empirical methodology for
this study.
e) Endogeneity issue
A change model, lead-lag approach and two-stage least squares regressions are
conducted in this study to address endogeneity issue. Nevertheless, the inferences made
from this study are still subject to standard caveats of endogeneity due to possibilities of
omitted variables. As such, it is acknowledged that this challenge is one of the
limitations of this study.
354
6.5 Recommendations for Future Research
Given the limitations of this study, there are many avenues available for future research
directions.
a) Extension of sample data
The study covers US PLCs for the years 2003 to 2009. Future research may update the
investigation until the latest year, that is, year 2012 as the lead-lag approach adopted in
this study requires data of corporate expenditure in year 2013. In addition, a
comparative study can be carried out to examine the impact of Sarbanes-Oxley Act on
managerial learning from stock prices and corporate expenditure decisions. This can be
done by extending the period of study backward for seven years, that is from 1996 to
2002 and comparing them with the current study covering the post Sarbanes-Oxley
period. Furthermore, future studies can consider replicating the current study to other
countries with a different information environment. Emerging countries such as China
and India can be considered to provide insights on the learning theory and corporate
expenditure decisions as well as the role of information asymmetry.
b) Qualitative research
Qualitative research incorporating questionnaires and interviews could be considered for
future investigations on managerial learning from observing stock prices as well as
examining the motivation of managers when they make corporate expenditure decisions.
355
6.6 Concluding Remarks
This study provides insights into two broad areas: the connection between stock price
informativeness and corporate expenditure as well as the moderating effect of
information asymmetry on this association. A negative link between a current year‟s
stock price informativeness and the subsequent year‟s R&D expenditure and SGA costs
is observed while no relationship is found between a current year‟s stock price
informativeness and CAPEX in the subsequent year. This study also sheds some light by
using a change model to examine changes in a current year‟s stock price
informativeness and changes in the subsequent year‟s R&D expenditure and SGA costs.
Asymmetric cost behaviour of firm managers is portrayed when it comes to modifying
corporate expenditure as idiosyncratic volatility changes. These findings are consistent
with the learning theory and they enrich our understanding of how firm-level corporate
expenditure responds to stock price informativeness.
This study also finds the relationship between stock price informativeness and corporate
expenditure is dependent on information asymmetry through three proxies: firm size,
analyst following and bid-ask spreads. Consistent with the learning theory and
information asymmetry theory, managers in small firms, in firms with low analyst
following and in firms with high bid-ask spreads are induced by the greater amount of
new private information available in their firms. They are more responsive and alter
their spending in R&D expenditure and SGA costs in the subsequent year when stock
price informativeness of a current year changes.
356
Stock prices informativeness portrays efficient signals for resource allocation and thus
to the functional efficiency of the stock markets (Durnev et al., 2003). Given the
significance of stock price informativeness in the allocation of scarce resources, the
findings of this study have important implications for firms and investors. This study
further provides meaningful insights on the appropriate corporate expenditure invested
by firm managers in the following year in response to the informativeness of the stock
price as well as how the association between stock price informativeness and corporate
expenditure is dependent on information asymmetry. It is believed that this study has
made a valuable contribution to the relevant body of knowledge.
357
REFERENCES
Abarbanell, J. S. & Bushee, B. J. (1997). Fundamental analysis, future earnings, and
stock prices. Journal of Accounting Research, 35(1), 1-24.
Aboody, D. & Lev, B. (2000). Information asymmetry, R&D, and insider gains. Journal
of Finance, 55(6), 2747-2766.
Agrawal, A., Chadha, S. & Chen, Mark A. (2006). Who is afraid of Reg FD? The
behaviour and performance of sell-side analysts following the SEC‟s fair
disclosure rules. The Journal of Business, 79(6), 2811-2834.
Akerlof, G. A. (1970). The market for "lemons": Quality uncertainty and the market
mechanism. Quarterly Journal of Economics, 84(3), 488-500.
Alves, P., Peasnell, K. & Taylor, P. (2010). The use of the R2 as a measure of firm-
specific information: A cross-country critique. Journal of Business Finance &
Accounting, 37(1-2), 1-26.
Amihud, Y. & Mendelson, H. (1980). Dealership market: Market-making with
inventory. Journal of Financial Economics, 8(1), 31-53.
Amihud, Y. & Mendelson, H. (1986). Asset pricing and the bid-ask spread. Journal of
Financial Economics, 17(2), 223-249.
Amihud, Y. & Mendelson, H. (1989). The effects of beta, bid-ask spread, residual risk,
and size on stock returns. Journal of Finance, 44(2), 479-486.
Amihud, Y. (2002). Illiquidity and stock returns: Cross-section and time-series effects.
Journal of Financial Markets, 5(1), 31-56.
358
An, H. & Zhang, T. (2013). Stock price synchronicity, crash risk, and institutional
investors. Journal of Corporate Finance, 21(0), 1-15.
Anderson, M. C., Banker, R. D. & Janakiraman, S. N. (2003). Are selling, general, and
administrative costs “sticky”? Journal of Accounting Research, 41(1), 47-63.
Anderson, M. C., Banker, R. D., Huang, R. & Janakiraman, S. N. (2007). Cost
behaviour and fundamental analysis of SG&A costs. Journal of Accounting,
Auditing & Finance, 22(1), 1-28.
Anderson, S. W. & Lanen, W. N. (2009). Understanding cost management: What can
we learn from the empirical evidence on “sticky costs?” Rice University,
University of Melbourne and University of Michigan. Available at
https://research.mbs.ac.uk/accounting-
finance/Portals/0/docs/2008/UnderstandingCostManagementWhatCanWeLearnfr
omtheEmpiricalEvidenceonStickyCosts.pdf.
Armstrong, C. S., Guay, W. R. & Weber, J. P. (2010). The role of information and
financial reporting in corporate governance and debt contracting. Journal of
Accounting and Economics, 50(2-3), 179-234.
Armstrong, C. S., Balakrishnan, K. & Cohen, D. (2012). Corporate governance and the
information environment: Evidence from state anti-takeover laws. Journal of
Accounting and Economics, 53(1-2), 185-204.
Arrow, K. J. (1963). Uncertainty and the welfare economics of medical care. The
American Economic Review, 53(5), 941-973.
359
Arrow, K. J. (1985). The economics of agency. In Pratt, J. & Zeckhauser, R. (Eds.),
Principals and agents: The structure of business. Boston: Harvard Business
School Press.
Asquith, P., Mikhail, M. B. & Au, A. S. (2005). Information content of equity analyst
reports. Journal of Financial Economics, 75(2), 245-282.
Atiase, R. K. (1985). Pre-disclosure information, firm capitalization, and security price
behaviour around earnings announcements. Journal of Accounting Research,
23(1), 21-36.
Audretsch, D. B. & Feldman, M. P. (1996). R&D spillovers and the geography of
innovation and production. The American Economic Review, 86(3), 630-640.
Ayers, B. & Freeman, R. (2003). Evidence that analyst following and institutional
ownership accelerate the pricing of future earnings. Review of Accounting
Studies, 8(1), 47-67.
Bagehot, W. (1971). The only game in town. Financial Analysts Journal, 27(2), 12-14
& 22.
Bakke, T.-E. & Whited, T. M. (2010). Which firms follow the market? An analysis of
corporate investment decisions. Review of Financial Studies, 23(5), 1941-1980.
Balakrishnan, R. & Soderstrom, N. S. (2000). The cost of system congestion: Evidence
from the healthcare sector. Journal of Management Accounting Research, 12,
97-114.
360
Balakrishnan, R., Petersen, M. J. & Soderstrom, N. S. (2004). Does capacity utilization
affect the "stickiness" of cost? Journal of Accounting, Auditing & Finance, 19(3),
283-299.
Balakrishnan, R. & Gruca, T. S. (2008). Cost stickiness and core competency: A note.
Contemporary Accounting Research, 25(4), 993-1006.
Balakrishnan, R., Labro, E. & Soderstrom, N. S. (2011). Cost structure and sticky costs.
The University of Iowa, University of North Carolina and University of
Colorado. Available at SSRN: http://ssrn.com/abstract=1562726
Banker, R. D. & Chen, L. (2006a). Predicting earnings using a model based on cost
variability and cost stickiness. Accounting Review, 81(2), 285-307.
Banker, R. D. & Chen, L. (2006b). Labour market characteristics and cross-country
differences in cost stickiness. Temple University and Georgia State University.
Available at
http://64.156.29.76/MAS/MASPAPERS2007/cost_behavior/Banker%20and%20
Chen.pdf.
Banker, R. D., Huang, R. & Natarajan, R. (2006). Does SG&A expenditure create a
long-lived asset? Temple University, City University of New York, The
University of Texas at Dallas
Banker, R. D., Byzalov, D. & Plehn-Dujowich, J. M. (2011a). Sticky cost behavior:
Theory and evidence. Temple University. Available at
http://astro.temple.edu/~dbyzalov/sticky.pdf.
361
Banker, R. D., Huang, R. & Natarajan, R. (2011b). Equity incentives and long-term
value created by SG&A expenditure. Contemporary Accounting Research, 28(3),
794-830.
Banker, R. D., Basu, S., Byzalov, D. & Chen, J. Y. S. (2012). Asymmetric timeliness of
earnings: Conservatism or sticky costs? Paper presented at the AAA 2013
Management Accounting Section (MAS) Meeting paper. Available at SSRN:
http://ssrn.com/abstract=213089.
Banker, R. D., Byzalov, D., Ciftci, M. & Mashruwala, R. (2012). The moderating effect
of prior sales changes on asymmetric cost behaviour. Temple University, SUNY
at Binghamton, University of Illinois at Chicago. Available at SSRN:
http://ssrn.com/abstract=902546
Banker, R. D., Byzalov, D. & Chen, L. (2013). Employment protection legislation,
adjustment costs and cross-country differences in cost behaviour. Journal of
Accounting and Economics, 55(1), 111-127.
Baptista, R. & Swann, P. (1998). Do firms in clusters innovate more? Research Policy,
27(5), 525-540.
Barker, V. L., III & Mueller, G. C. (2002). CEO characteristics and firm R&D spending.
Management Science, 48(6), 782-801.
Barth, M. E. (1994). Fair value accounting: Evidence from investment securities and the
market valuation of banks. The Accounting Review, 69(1), 1-25.
Basu, S. (1997). The conservatism principle and the asymmetric timeliness of earnings.
Journal of Accounting and Economics, 24(1), 3-37.
362
Baumgarten, D., Bonenkamp, U. & Homburg, C. (2010). The information content of the
SG&A ratio. Journal of Management Accounting Research, 22(1), 1-22.
Baysinger, B. & Hoskisson, R. E. (1989). Diversification strategy and R&D intensity in
multiproduct firms. The Academy of Management Journal, 32(2), 310-332.
Baysinger, B. & Hoskisson, R. E. (1990). The composition of boards of directors and
strategic control: Effects on corporate strategy. The Academy of Management
Review, 15(1), 72-87.
Baysinger, B., Kosnik, R. D. & Turk, T. A. (1991). Effects of board and ownership
structure on corporate R&D strategy. The Academy of Management Journal,
34(1), 205-214.
Beaver, W. H. (1968). The information content of annual earnings announcements.
Journal of Accounting Research, 6(3), 67-92.
Benartzi, S. & Thaler, R. H. (1995). Myopic loss aversion and the equity premium
puzzle. The Quarterly Journal of Economics, 110(1), 73-92.
Berk, J. B., Green, R. C. & Naik, V. (2004). Valuation and return dynamics of new
ventures. Review of Financial Studies, 17(1), 1-35.
Beuselinck, C., Joos, P., Khurana, I. K. & Van der Meulen, S. (2009). Mandatory IFRS
reporting and stock price informativeness. Tilburg University and University of
Missouri at Columbia. Available at SSRN: http://ssrn.com/abstract=1381242
Bhagat, S. & Bolton, B. (2008). Corporate governance and firm performance. Journal of
Corporate Finance, 14(3), 257-273.
363
Bhattacharya, U. & Spiegel, M. (1991). Insiders, outsiders, and market breakdowns.
Review of Financial Studies, 4(2), 255-282.
Bhushan, R. (1989a). Collection of information about publicly traded firms: Theory and
evidence. Journal of Accounting and Economics, 11(2–3), 183-206.
Bhushan, R. (1989b). Firm characteristics and analyst following. Journal of Accounting
and Economics, 11(2–3), 255-274.
Bourgeois, L. J. (1981). On the measurement of organizational slack. Academy of
Management Review, 6(1), 29-39.
Brennan, M. J., Jegadeesh, N. & Swaminathan, B. (1993). Investment analysis and the
adjustment of stock prices to common information. Review of Financial Studies,
6(4), 799-824.
Brennan, M. J. & Subrahmanyam, A. (1995). Investment analysis and price formation in
securities markets. Journal of Financial Economics, 38(3), 361-381.
Brennan, M. J. & Tamarowski, C. (2000). Investor relations, liquidity, and stock prices.
Journal of Applied Corporate Finance, 12(4), 26-37.
Brockman, P. & Yan, X. (2009). Block ownership and firm-specific information.
Journal of Banking & Finance, 33(2), 308-316.
Brockman, P., Liebenberg, I. & Schutte, M. (2010). Co-movement, information
production, and the business cycle. Journal of Financial Economics, 97(1), 107-
129.
Brown, S. & Hillegeist, S. (2007). How disclosure quality affects the level of
information asymmetry. Review of Accounting Studies, 12(2-3), 443-477.
364
Bublitz, B. & Ettredge, M. (1989). The information in discretionary outlays: Advertising,
research, and development. The Accounting Review, 64(1), 108-124.
Bushman, R. M. & Smith, A. J. (2001). Financial accounting information and corporate
governance. Journal of Accounting and Economics, 32(1–3), 237-333.
Calleja, K., Steliaros, M. & Thomas, D. C. (2006). A note on cost stickiness: Some
international comparisons. Management Accounting Research, 17(2), 127-140.
Cameron, A. C., Gelbach, J. B. & Miller, D. L. (2011). Robust inference with multiway
clustering. Journal of Business & Economic Statistics, 29(2), 238-249.
Campbell, J. Y., Lettau, M., Malkiel, B. G. & Xu, Y. (2001). Have individual stocks
become more volatile? An empirical exploration of idiosyncratic risk. The
Journal of Finance, 56(1), 1-43.
Capon, N., Farley, J. U. & Hoenig, S. (1990). Determinants of financial performance: A
meta-analysis. Management Science, 36(10), 1143-1159.
Carpenter, M. A. (2000). The price of change: The role of CEO compensation in
strategic variation and deviation from industry strategy norms. Journal of
Management, 26(6), 1179.
Carpenter, R. E. & Guariglia, A. (2008). Cash flow, investment, and investment
opportunities: New tests using UK panel data. Journal of Banking & Finance,
32(9), 1894-1906.
Cazier, R. A. (2011). Measuring R&D curtailment among short-horizon CEOs. Journal
of Corporate Finance, 17(3), 584-594.
365
Chambers, D., Jennings, R. & Thompson, I. I. R. B. (2002). Excess returns to R&D-
intensive firms. Review of Accounting Studies, 7(2-3), 133-158.
Chan, K. & Hameed, A. (2006). Stock price synchronicity and analyst coverage in
emerging markets. Journal of Financial Economics, 80(1), 115-147.
Chan, K., Hameed, A. & Kang, W. (2013). Stock price synchronicity and liquidity.
Journal of Financial Markets, 16(3), 414-438.
Chan, L. K. C., Lakonishok, J. & Sougiannis, T. (2001). The stock market valuation of
research and development expenditures. The Journal of Finance, 56(6), 2431-
2456.
Chauvin, K. W. & Hirschey, M. (1993). Advertising, R&D expenditures and the market
value of the firm. Financial Management, 22(4), 128-140.
Chen, C., Huang, A. G. & Jha, R. (2012a). Idiosyncratic return volatility and the
information quality underlying managerial discretion. Journal of Financial &
Quantitative Analysis, 47(4), 873-899.
Chen, C. X., Lu, H. & Sougiannis, T. (2012b). The agency problem, corporate
governance, and the asymmetrical behaviour of selling, general, and
administrative costs. Contemporary Accounting Research, 29(1), 252-282.
Chen, Q., Goldstein, I. & Jiang, W. (2007). Price informativeness and investment
sensitivity to stock price. The Review of Financial Studies, 20(3), 619-650.
Chen, S.-S. (2006). The economic impact of corporate capital expenditures: Focused
firms versus diversified firms. The Journal of Financial and Quantitative
Analysis, 41(2), 341-355.
366
Chung, K. H. & Jo, H. (1996). The impact of security analysts' monitoring and
marketing functions on the market value of firms. The Journal of Financial and
Quantitative Analysis, 31(4), 493-512.
Chung, K. H., Wright, P. & Charoenwong, C. (1998). Investment opportunities and
market reaction to capital expenditure decisions. Journal of Banking & Finance,
22(1), 41-60.
Chung, K. H., Elder, J. & Kim, J.-C. (2010). Corporate governance and liquidity.
Journal of Financial & Quantitative Analysis, 45(2), 265-291.
Ciftci, M. & Cready, W. M. (2011). Scale effects of R&D as reflected in earnings and
returns. Journal of Accounting and Economics, 52(1), 62-80.
Ciftci, M., Lev, B. & Radhakrishnan, S. (2011). Is research and development mispriced
or properly risk adjusted? Journal of Accounting, Auditing & Finance, 26(1), 81-
116.
Cody, R. (2011). SAS statistics by example. Cary, North Carolina: SAS Institute Inc.
Cohen, W. M. & Levinthal, D. A. (1989). Innovation and learning: The two faces of
R&D. The Economic Journal, 99(397), 569-596.
Cohen, W. M. & Kleppler, S. (1996). A reprise of size and R&D. Economic Journal,
106(437), 925-951.
Coles, J. L., Daniel, N. D. & Naveen, L. (2008). Boards: Does one size fit all. Journal of
Financial Economics, 87(2), 329-356.
367
Coles, J. L., Lemmon, M. L. & Meschke, J. F. (2012). Structural models and
endogeneity in corporate finance: The link between managerial ownership and
corporate performance. Journal of Financial Economics, 103(1), 149-168.
Collins, D. W., Kothari, S. P. & Rayburn, J. D. (1987). Firm size and the information
content of prices with respect to earnings. Journal of Accounting and Economics,
9(2), 111-138.
Connolly, R. A. & Hirschey, M. (2005). Firm size and the effect of R&D on Tobin's q.
R&D Management, 35(2), 217-223.
Cooper, R. & Kaplan, R. S. (1988). The design of cost management systems: Text, cases
and readings. Upper Saddle River, N.J: Prentice Hall.
Copeland, T. E. & Galai, D. (1983). Information effects on the bid-ask spread. The
Journal of Finance, 38(5), 1457-1469.
Crawford, S. S., Roulstone, D. T. & So, E. C. (2012). Analyst initiations of coverage
and stock return synchronicity. The Accounting Review, 87(5), 1527-1553.
Crotty, M. (1998). The foundations of social research. Sydney: Allen & Unwin.
Dalla Via, N. & Perego, P. (Forthcoming). Sticky cost behaviour: Evidence from small
and medium sized companies. Accounting & Finance, available at
doi:10.1111/acfi.12020.
Daouk, H., Lee, C. M. C. & Ng, D. (2006). Capital market governance: How do security
laws affect market performance? Journal of Corporate Finance, 12(3), 560-593.
368
Dasgupta, S., Gan, J. & Gao, N. (2010). Transparency, price informativeness, and stock
return synchronicity: Theory and evidence. Journal of Financial & Quantitative
Analysis, 45(5), 1189-1220.
Dechow, P. M. & Sloan, R. G. (1991). Executive incentives and the horizon problem:
An empirical investigation. Journal of Accounting and Economics, 14(1), 51-89.
Dempsey, S. J. (1989). Pre-disclosure information search incentives, analyst following,
and earnings announcement price response. The Accounting Review, 64(4), 748-
757.
Demsetz, H. (1968). The cost of transacting. The Quarterly Journal of Economics, 82(1),
33-53.
Demsetz, H. & Lehn, K. (1985). The structure of corporate ownership: Causes and
consequences. Journal of Political Economy, 93(6), 1155-1177.
Devereux, M. & Schiantarelli, F. (1990). Investment, financial factors, and cash flow:
Evidence from U.K. panel data. In Hubbard, R. G. (Ed.), Asymmetric
information, corporate finance, and investment (pp. 279-306). Chicago:
University of Chicago Press.
Diamond, D. & Verrecchia, R. (1991). Disclosure, liquidity, and the cost of capital.
Journal of Finance 46(4), 1325-1359.
Ding, R., Hou, W., Kuo, J.-M. & Lee, E. (2013). Fund ownership and stock price
informativeness of Chinese listed firms. Journal of Multinational Financial
Management, 23(3), 166-185.
369
Dopuch, N. & Simunic, D. (1982). Competition in auditing: An assessment. Paper
presented at the Fourth Symposium on Auditing Research, Univeristy of Illinois.
Dornbusch, R. & Fischer, S. (1992). Macroeconomics. New York: McGraw-Hill.
Douma, S. & Schreuder, H. (2008). Economic approaches to organizations (4th ed.).
England: Pearson Education Limited.
Dow, J. & Gorton, G. (1997). Stock market efficiency and economic efficiency: Is there
a connection? Journal of Finance, 52(3), 1087-1129.
Dow, J. & Rahi, R. (2003). Informed trading, investment, and welfare. The Journal of
Business, 76(3), 439-454.
Durnev, A., Morck, R., Yeung, B. & Zarowin, P. (2003). Does greater firm-specific
return variation mean more or less informed stock pricing? Journal of
Accounting Research, 41(5), 797-836.
Durnev, A., Morck, R. & Yeung, B. (2004). Value-enhancing capital budgeting and
firm-specific stock return variation. The Journal of Finance, 59(1), 65-105.
Dye, R. A. & Sridhar, S. (2002). Resource allocation effects of price reactions to
disclosures. Contemporary Accounting Research, 19(3), 385-410.
Easley, D., Kiefer, N. M. & O'Hara, M. (1996). Cream-skimming or profit-sharing? The
curious role of purchased order flow. The Journal of Finance, 51(3), 811-833.
Easley, D., O'Hara, M. & Paperman, J. (1998). Financial analysts and information-based
trade. Journal of Financial Markets, 1(2), 175-201.
370
Eberhart, A. C., Maxwell, W. F. & Siddique, A. R. (2004). An examination of long-term
abnormal stock returns and operating performance following R&D increases.
The Journal of Finance, 59(2), 623-650.
Eberhart, A. C., Maxwell, W. F. & Siddique, A. R. (2008). A re-examination of the
tradeoff between the future benefit and riskiness of R&D increases. Journal of
Accounting Research, 46(1), 27-52.
Erickson, G. & Jacobson, R. (1992). Gaining comparative advantage through
discretionary expenditures: The returns to R&D and advertising. Management
Science, 38(9), 1264-1279.
Eriksson, P. & Kovalainen, A. (2008). Research philosophy. London: SAGE
Publications Ltd.
Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work.
The Journal of Finance, 25(2), 383-417.
Fama, E. F. & MacBeth, J. D. (1973). Risk, return, and equilibrium: Empirical tests.
Journal of Political Economy, 81(3), 607-636.
Fama, E. F. & Jensen, M. C. (1983). Agency problems and residual claims. Journal of
Law and Economics, 26(2), 327-349.
Fama, E. F. & French, K. R. (1993). Common risk factors in the returns on stocks and
bonds. Journal of Financial Economics, 33(1), 3-56.
Fama, E. F. & French, K. R. (1995). Size and book-to-market factors in earnings and
returns. The Journal of Finance, 50(1), 131-155.
371
Fama, E. F. & French, K. R. (1996). Multifactor explanations of asset pricing anomalies.
The Journal of Finance, 51(1), 55-84.
Fazzari, S. M., Hubbard, R. G. & Petersen, B. C. (1988). Financing constraints and
corporate investment. Brookings Papers on Economic Activity, 1, 141-195.
Fernandes, N. & Ferreira, M. A. (2008). Does international cross-listing improve the
information environment? Journal of Financial Economics, 88(2), 216-244.
Fernandes, N. & Ferreira, M. A. (2009). Insider trading laws and stock price
informativeness. Review of Financial Studies, 22(5), 1845-1887.
Ferreira, D., Ferreira, M. A. & Raposo, C. C. (2011). Board structure and price
informativeness. Journal of Financial Economics, 99(3), 523-545.
Ferreira, M. A. & Laux, P. A. (2007). Corporate governance, idiosyncratic risk, and
information flow. The Journal of Finance, 62(2), 951-989.
Field, A. (2009). Discovering statistics using SPSS (3rd ed.). London: SAGE
Publication Ltd.
Finkelstein, S. & Hambrick, D. C. (1990). Top-management-team tenure and
organizational outcomes: The moderating role of managerial discretion.
Administrative Science Quarterly, 35(3), 484-503.
Finkelstein, S. & Hambrick, D. C. (1996). Strategic Leadership: Top executives and
their effects on organizations. St Paul, MN: West.
Fischer, S. & Merton, R. C. (1984). Macroeconomics and finance: The role of the stock
market. Carnegie-Rochester Conference Series on Public Policy, 21, 57-108.
372
Foucault, T. & Gehrig, T. (2008). Stock price informativeness, cross-listings, and
investment decisions. Journal of Financial Economics, 88(1), 146-168.
Foucault, T. & Frésard, L. (2012). Cross-listing, investment sensitivity to stock price,
and the learning hypothesis. Review of Financial Studies, 25(11), 3305-3350.
Francis, J. & Soffer, L. (1997). The relative informativeness of analysts' stock
recommendations and earnings forecast revisions. Journal of Accounting
Research, 35(2), 193-211.
Frankel, R. & Li, X. (2004). Characteristics of a firm's information environment and the
information asymmetry between insiders and outsiders. Journal of Accounting
and Economics, 37(2), 229-259.
Frankel, R., Kothari, S. P. & Weber, J. (2006). Determinants of the informativeness of
analyst research. Journal of Accounting and Economics, 41(1–2), 29-54.
Freeman, R. N. (1987). The association between accounting earnings and security
returns for large and small firms. Journal of Accounting and Economics, 9(2),
195-228.
French, K. R. & Roll, R. (1986). Stock return variances: The arrival of information and
the reaction of traders. Journal of Financial Economics, 17(1), 5-26.
Frésard, L. (2012). Cash savings and stock price informativeness. Review of Finance,
16(4), 985-1012.
Fried, D. & Givoly, D. (1982). Financial analysts' forecasts of earnings: A better
surrogate for market expectations. Journal of Accounting and Economics, 4(2),
85-107.
373
Gaspar, J. M. & Massa, M. (2006). Idiosyncratic volatility and product market
competition. The Journal of Business, 79(6), 3125-3152.
Giammarino, R., Heinkel, R., Hollifield, B. & Li, K. (2004). Corporate decisions,
information and prices: Do managers move prices or do prices move managers?
Economic Notes, 33(1), 83-110.
Gillan, S. L., Hartzell, J. C. & Starks, L. T. (2003). Explaining corporate governance:
Boards, bylaws, and charter provisions. Weinberg Center for Corporate
Governance Working Paper No. 2003-03. Available at SSRN:
http://ssrn.com/abstract=442740 or doi:10.2139.
Givoly, D. & Lakonishok, J. (1979). The information content of financial analysts'
forecasts of earnings: Some evidence on semi-strong inefficiency. Journal of
Accounting and Economics, 1(3), 165-185.
Glosten, L. R. & Milgrom, P. R. (1985). Bid, ask and transaction prices in a specialist
market with heterogeneously informed traders. Journal of Financial Economics,
14(1), 71-100.
Goldstein, I. & Guembel, A. (2008). Manipulation and the allocational role of prices.
The Review of Economic Studies, 75(1), 133-164.
Gompers, P., Ishii, J. & Metrick, A. (2003). Corporate governance and equity prices.
Quarterly Journal of Economics, 118(1), 107-155.
Gordon, L. A. & Iyengar, R. J. (1996). Return on investment and corporate capital
expenditures: Empirical evidence. Journal of Accounting and Public Policy,
15(4), 305-325.
374
Gow, I. D., Ormazabal, G. & Taylor, D. J. (2010). Correcting for cross-sectional and
time-series dependence in accounting research. Accounting Review, 85(2), 483-
512.
Goyal, A. & Santa-Clara, P. (2003). Idiosyncratic risk matters! The Journal of Finance,
58(3), 975-1007.
Grabowski, H. G. (1968). The determinants of industrial research and development: A
study of the chemical, drug, and petroleum industries. Journal of Political
Economy, 76(2), 292-306.
Grant, E. B. (1980). Market implications of differential amounts of interim information.
Journal of Accounting Research, 18(1), 255-268.
Graves, S. B. (1988). Institutional ownership and corporate R&D in the computer
industry. The Academy of Management Journal, 31(2), 417-428.
Griliches, Z. (1981). Market value, R&D, and patents. Economics Letters, 7(2), 183-187.
Griliches, Z. (1998). The search for R&D spillovers. R&D and productivity: The
econometric evidence (pp. 251-268): University of Chicago Press.
Grimm, C. M. & Smith, K. G. (1991). Management and organizational change: A note
on the railroad industry. Strategic Management Journal, 12(7), 557-562.
Griner, E. H. & Gordon, L. A. (1995). Internal cash flow, insider ownership, and capital
expenditures: A test of pecking order and managerial hypotheses. Journal of
Business Finance & Accounting, 22(2), 179-199.
Grossman, G. & Helpman, E. (1991). Innovation and growth in the global economy.
Cambridge: MIT Press.
375
Grossman, S. (1976). On the efficiency of competitive stock markets where trades have
diverse information. The Journal of Finance, 31(2), 573-585.
Grossman, S. J. & Stiglitz, J. E. (1980). On the impossibility of informationally efficient
markets. The American Economic Review, 70(3), 393-408.
Guba, E. G. (1990). The alternative paradigm dialog In Guba, E. G. (Ed.), The paradigm
dialog (pp. 17-27): Sage Publications.
Gul, F. A. & Tsui, J. S. L. (1998). A test of the free cash flow and debt monitoring
hypotheses: Evidence from audit pricing. Journal of Accounting and Economics,
24(2), 219-237.
Gul, F. A. (2001). Free cash flow, debt-monitoring and managers' LIFO/FIFO policy
choice. Journal of Corporate Finance, 7(4), 475-492.
Gul, F. A., Fung, S. Y. K. & Jaggi, B. (2009). Earnings quality: Some evidence on the
role of auditor tenure and auditors' industry expertise. Journal of Accounting &
Economics, 47(3), 265-287.
Gul, F. A., Kim, J.-B. & Qiu, A. A. (2010). Ownership concentration, foreign
shareholding, audit quality, and stock price synchronicity: Evidence from China.
Journal of Financial Economics, 95(3), 425-442.
Gul, F. A., Cheng, L. T. W. & Leung, T. Y. (2011a). Perks and the informativeness of
stock prices in the Chinese market. Journal of Corporate Finance, 17(5), 1410-
1429.
376
Gul, F. A., Srinidhi, B. & Ng, A. C. (2011b). Does board gender diversity improve the
informativeness of stock prices? Journal of Accounting & Economics, 51(3),
314-338.
Gupta, A. K. (1987). SBU strategies, corporate-SBU relations, and SBU effectiveness in
strategy implementation. Academy of Management Journal, 30(3), 477-500.
Haggard, K. S., Martin, X. & Pereira, R. (2008). Does voluntary disclosure improve
stock price informativeness? Financial Management, 37(4), 747-768.
Hambrick, D. C. & Fukutomi, G. D. S. (1991). The seasons of a CEO's tenure. The
Academy of Management Review, 16(4), 719-742.
Hamermesh, D. S. (2000). The craft of labourmetrics. Industrial and Labor Relations
Review, 53(3), 363-380.
Hamilton, J. L. (1978). Marketplace organization and marketability: NASDAQ, the
stock exchange, and the national market system. The Journal of Finance, 33(2),
487-503.
Hansen, G. S. & Hill, C. W. L. (1991). Are institutional investors myopic? A time-series
study of four technology-driven industries. Strategic Management Journal, 12(1),
1-16.
Harford, J., Mansi, S. A. & Maxwell, W. F. (2008). Corporate governance and firm cash
holdings in the US. Journal of Financial Economics, 87(3), 535-555.
Hayek, F. A. (1945). The use of knowledge in society. The American Economic Review,
35(4), 519-530.
377
Haynes, K. T. & Hillman, A. (2010). The effect of board capital and CEO power on
strategic change. Strategic Management Journal, 31(11), 1145-1163.
He, W., Li, D., Shen, J. & Zhang, B. (2013). Large foreign ownership and stock price
informativeness around the world. Journal of International Money and Finance,
36, 211-230.
Healy, P. M., Hutton, A. P. & Palepu, K. G. (1999). Stock performance and
intermediation changes surrounding sustained increases in disclosure.
Contemporary Accounting Research, 16(3), 485-520.
Healy, P. M. & Palepu, K. G. (2001). Information asymmetry, corporate disclosure, and
the capital markets: A review of the empirical disclosure literature. Journal of
Accounting and Economics, 31(1-3), 405-440.
Henderson, R. & Cockburn, I. (1996). Scale, scope, and spillovers: The determinants of
research productivity in drug discovery. The RAND Journal of Economics, 27(1),
32-59.
Hill, C. W. L. & Snell, S. A. (1988). External control, corporate strategy, and firm
performance in research-intensive industries. Strategic Management Journal,
9(6), 577-590.
Himmelberg, C. P. & Petersen, B. C. (1994). R & D and internal finance: A panel study
of small firms in high-tech industries. The Review of Economics and Statistics,
76(1), 38-51.
Hirschey, M. (1985). Market structure and market value. The Journal of Business, 58(1),
89-98.
378
Ho, Y. K., Xu, Z. & Yap, C. M. (2004). R&D investment and systematic risk.
Accounting & Finance, 44(3), 393-418.
Ho, Y. K., Tjahjapranata, M. & Yap, C. M. (2006). Size, leverage, concentration, and
R&D investment in generating growth opportunities. Journal of Business, 79(2),
851-876.
Holmström, B. & Tirole, J. (1993). Market liquidity and performance monitoring.
Journal of Political Economy, 101(4), 678-709.
Hölmstrom, B. (1979). Moral hazard and observability. The Bell Journal of Economics,
10(1), 74-91.
Hong, H., Lim, T. & Stein, J. C. (2000). Bad news travels slowly: Size, analyst coverage,
and the profitability of momentum strategies. The Journal of Finance, 55(1),
265-295.
Hou, W., Kuo, J.-M. & Lee, E. (2012). The impact of state ownership on share price
informativeness: The case of the Split Share Structure Reform in China. The
British Accounting Review, 44(4), 248-261.
Hsin, C.-W. & Tseng, P.-W. (2012). Stock price synchronicities and speculative trading
in emerging markets. Journal of Multinational Financial Management, 22(3),
82-109.
Huang, R. D. & Stoll, H. R. (1996). Dealer versus auction markets: A paired comparison
of execution costs on NASDAQ and the NYSE. Journal of Financial Economics,
41(3), 313-357.
379
Hutton, A. P., Marcus, A. J. & Tehranian, H. (2009). Opaque financial reports, R2, and
crash risk. Journal of Financial Economics, 94(1), 67-86.
Inci, A. C., Lee, B. S. & Suh, J. (2009). Capital investment and earnings: International
evidence. Corporate Governance: An International Review, 17(5), 526-545.
Irvine, P. J. & Pontiff, J. (2009). Idiosyncratic return volatility, cash flows, and product
market competition. Review of Financial Studies, 22(3), 1149-1177.
Jaffe, A. B. (1986). Technological opportunity and spillovers of R&D: Evidence from
firms' patents, profits and market value. American Economic Review, 76(5), 984-
999.
Janakiraman, S. (2010). Discussion of "The information content of the SG&A ratio".
Journal of Management Accounting Research, 22(1), 23-30.
Jensen, M. C. & Meckling, W. H. (1976). Theory of the firm: Managerial behavior,
agency costs and ownership structure. Journal of Financial Economics, 3(4),
305-360.
Jensen, M. C. (1986). Agency costs of free cash flow, corporate finance, and takeovers.
American Economic Review, 76(2), 323-329.
Jensen, M. C. (2005). Agency costs of overvalued equity. Financial Management, 34(1),
5-19.
Jiang, G. J., Xu, D. & Tong, Y. (2009). The information content of idiosyncratic
volatility. Journal of Financial & Quantitative Analysis, 44(1), 1-28.
Jin, L. & Myers, S. C. (2006). R2 around the world: New theory and new tests. Journal
of Financial Economics, 79(2), 257-292.
380
Johnson, S., La Porta, R., Lopez-de-Silanes, F. & Shleifer, A. (2000). Tunneling.
American Economic Review, 90(2), 22-27.
Jones, J. J. (1991). Earnings management during import relief investigations. Journal of
Accounting Research, 29(2), 193-228.
Kama, I. & Weiss, D. (2013). Do earnings targets and managerial incentives affect
sticky costs? Journal of Accounting Research, 51(1), 201-224.
Kamien, M. I. & Schwartz, N. L. (1978). Self-financing of an R&D Project. The
American Economic Review, 68(3), 252-261.
Kamien, M. I. & Schwartz, N. L. (1982). Market structure and innovation. New York:
Cambridge University Press.
Karpoff, J. M. (1986). A theory of trading volume. The Journal of Finance, 41(5), 1069-
1087.
Kelly, P. J. (2005). Information efficiency and firm-specific return variation. SSRN
eLibrary. University of South Florida,. EFA 2005 Moscow Meetings Paper.
Available at http://ssrn.com/paper=676636.
Kerstein, J. & Kim, S. (1995). The incremental information content of capital
expenditures. The Accounting Review, 70(3), 513-526.
Khanna, T. & Thomas, C. (2009). Synchronicity and firm interlocks in an emerging
market. Journal of Financial Economics, 92(2), 182-204.
Kim, J.-B. & Shi, H. (2010). Voluntary IFRS adoption and stock price synchronicity:
Do analyst following and institutional infrastructure matter? City University of
381
Hong Kong & Fudan University. Available at SSRN:
http://ssrn.com/abstract=1586657.
Kor, Y. Y. (2006). Direct and interaction effects of top management team and board
compositions on R&D investment strategy. Strategic Management Journal,
27(11), 1081-1099.
Kothari, S. P. (2001). Capital markets research in accounting. Journal of Accounting
and Economics, 31(1-3), 105-231.
Kothari, S. P., Laguerre, T. E. & Leone, A. J. (2002). Capitalization versus expensing:
Evidence on the uncertainty of future earnings from capital expenditures versus
R&D outlays. Review of Accounting Studies, 7(4), 355-382.
Kuhn, T. S. (1962). The structure of scientific revolutions. Illinois: University of
Chicago Press.
Kyle, A. S. (1985). Continuous auctions and insider trading. Econometrica, 53(6), 1315-
1335.
Lang, L. H. P., Stulz, R. & Walkling, R. A. (1991). A test of the free cash flow
hypothesis: The case of bidder returns. Journal of Financial Economics, 29(2),
315-335.
Larcker, D. F. & Rusticus, T. O. (2010). On the use of instrumental variables in
accounting research. Journal of Accounting and Economics, 49(3), 186-205.
Lee, D. W. & Liu, M. H. (2011). Does more information in stock price lead to greater or
smaller idiosyncratic return volatility? Journal of Banking & Finance, 35(6),
1563-1580.
382
Lehmann, B. N. (1990). Residual risk revisited. Journal of Econometrics, 45(1–2), 71-
97.
Leuz, C. & Verrecchia, R. E. (2000). The economic consequences of increased
disclosure. Journal of Accounting Research, 38(3), 91-124.
Lev, B. (1988). Toward a theory of equitable and efficient accounting policy. The
Accounting Review, 63(1), 1-22.
Lev, B. & Thiagarajan, S. R. (1993). Fundamental information analysis. Journal of
Accounting Research, 31(2), 190-215.
Lev, B. & Sougiannis, T. (1996). The capitalization, amortization, and value-relevance
of R&D. Journal of Accounting and Economics, 21(1), 107-138.
Lev, B. (1999). R&D and capital markets. Journal of Applied Corporate Finance, 11(4),
21-35.
Levine, R. (1997). Financial development and economic growth: Views and agenda.
Journal of Economic Literature, 35(2), 688-726.
Li, K., Morck, R., Yang, F. & Yeung, B. (2004). Firm-specific variation and openness in
emerging markets. The Review of Economics and Statistics, 86(3), 658-669.
Linck, J. S., Netter, J. M. & Yang, T. (2008). The determinants of board structure.
Journal of Financial Economics, 87(2), 308-328.
Luo, X. & de Jong, P. (2012). Does advertising spending really work? The intermediate
role of analysts in the impact of advertising on firm value. Journal of the
Academy of Marketing Science, 40(4), 605-624.
383
Luo, Y. (2005). Do insiders learn from outsiders? Evidence from mergers and
acquisitions. The Journal of Finance, 60(4), 1951-1982.
Lys, T. & Sohn, S. (1990). The association between revisions of financial analysts'
earnings forecasts and security-price changes. Journal of Accounting and
Economics, 13(4), 341-363.
McAlister, L., Srinivasan, R. & Kim, M. (2007). Advertising, research and development,
and systematic risk of the firm. Journal of Marketing, 71(1), 35-48.
McConnell, J. J. & Muscarella, C. J. (1985). Corporate capital expenditure decisions and
the market value of the firm. Journal of Financial Economics, 14(3), 399-422.
McEachern, W. A. & Romeo, A. A. (1978). Stockholder control, uncertainty and the
allocation of resources to research and development. The Journal of Industrial
Economics, 26(4), 349-361.
Merton, R. C. (1987). A simple model of capital market equilibrium with incomplete
information. Journal of Finance, 42(3), 483-510.
Mizik, N. & Jacobson, R. (2003). Trading-off between value creation and value
appropriation: The financial implications of shifts in strategic emphasis. Journal
of Marketing, 67(1), 63-76.
Morck, R., Shleifer, A., Vishny, R. W., Shapiro, M. & Poterba, J. M. (1990). The stock
market and investment: Is the market a sideshow? Brookings Papers on
Economic Activity, 1990(2), 157-215.
384
Morck, R., Yeung, B. & Yu, W. (2000). The information content of stock markets: Why
do emerging markets have synchronous stock price movements? Journal of
Financial Economics, 58(1-2), 215-260.
Moyer, R. C., Chatfield, R. E. & Sisneros, P. M. (1989). Security analyst monitoring
activity: Agency costs and information demands. The Journal of Financial and
Quantitative Analysis, 24(4), 503-512.
Myers, S. C. (1984). The capital structure puzzle. Journal of Finance, 39(3), 574-592.
Myers, S. C. & Majluf, N. S. (1984). Corporate financing and investment decisions
when firms have information that investors do not have. Journal of Financial
Economics, 13(2), 187-221.
Nadiri, M. I. & Prucha, I. R. (1996). Estimation of the depreciation rate of physical and
R&D capital in the U.S. total manufacturing sector. Economic Inquiry, 34(1), 43.
Newey, W. K. & West, K. D. (1987). A simple, positive semi-definite,
heteroskedasticity and autocorrelation consistent covariance matrix.
Econometrica, 55(3), 703-708.
Noreen, E. (1991). Conditions under which activity-based cost systems provide relevant
costs. Journal of Management Accounting Research, 3, 159-168.
Noreen, E. & Soderstrom, N. (1994). Are overhead costs strictly proportional to
activity?: Evidence from hospital departments. Journal of Accounting and
Economics, 17(1–2), 255-278.
385
Noreen, E. & Soderstrom, N. (1997). The accuracy of proportional cost models:
Evidence from hospital service departments. Review of Accounting Studies, 2(1),
89-114.
O'Rourke, N., Hatcher, L. & Stepanski, E. J. (2009). A step-by-step approach to using
SAS for univariate and multivariate statistics (2nd ed.). Cary, North Carolina:
SAS Institute Inc.
Pakes, A. (1985). On patents, R&D, and the stock market rate of return. Journal of
Political Economy, 93(2), 390-409.
Palepu, K. (1985). Diversification strategy, profit performance and the entropy measure.
Strategic Management Journal, 6(3), 239-255.
Pauly, M. V. (1974). Over-insurance and public provision of insurance: The roles of
moral hazard and adverse selection. The Quarterly Journal of Economics, 88(1),
44-62.
Penman, S. H. & Zhang, X. J. (2002). Accounting conservatism, the quality of earnings,
and stock returns. The Accounting Review, 77(2), 237-264.
Penrose, E., Penrose, E. T. & Pitelis, C. (2009). The theory of the growth of the firm (4th
ed.). London: Oxford University Press.
Peress, J. (2010). Product market competition, insider trading, and stock market
efficiency. The Journal of Finance, 65(1), 1-43.
Petersen, C. & Plenborg, T. (2006). Voluntary disclosure and information asymmetry in
Denmark. Journal of International Accounting, Auditing and Taxation, 15(2),
127-149.
386
Petersen, M. A. (2009). Estimating standard errors in finance panel data sets: Comparing
approaches. Review of Financial Studies, 22(1), 435-480.
Pierce, J. R. & Aguinis, H. (2013). The too-much-of-a-good-thing effect in management.
Journal of Management, 39(2), 313-338.
Piotroski, J. D. & Roulstone, B. T. (2004). The influence of analysts, institutional
investors, and insiders on the incorporation of market, industry, and firm-specific
information into stock prices. The Accounting Review, 79(4), 1119-1151.
Ponterotto, J. G. (2005). Qualitative reserach in counseling psychology: A primer on
reserach paradigms and philosophy of science. Journal of Counseling
Psychology, 52(2), 126-136.
Porter, M. E. (1990). The competitive advantage of nations: With a new introduction.
New York: Free Press.
Rajagopalan, N. & Spreitzer, G. M. (1997). Toward a theory of strategic change: A
multi-lens perspective and integrative framework. The Academy of Management
Review, 22(1), 48-79.
Rajgopal, S. & Venkatachalam, M. (2011). Financial reporting quality and idiosyncratic
return volatility. Journal of Accounting and Economics, 51(1-2), 1-20.
Richardson, V. J. (2000). Information asymmetry and earnings management: Some
evidence. Review of Quantitative Finance and Accounting, 15(4), 325-347.
Roberts, P. W. (2001). Innovation and firm-level persistent profitability: A
Schumpeterian framework. Managerial and Decision Economics, 22(4-5), 239-
250.
387
Roll, R. (1988). R2. The Journal of Finance, 43(3), 541-566.
Rothwell, R. (1984). The role of small firms in the emergence of new technologies.
Omega, 12(1), 19-29.
Scherer, F. M. (1984). Innovation and growth: Schumpeterian perspectives. Cambridge:
MIT Press.
Schumpeter, J. A. (1942). Capitalism, socialism and democracy. New York: Harper and
Row.
Shadab, H. B. (2008). Innovation and corporate governance: The impact of Sarbanes-
Oxley. University of Pennsylvania Journal of Business and Employment Law, 10,
955-1008.
Shi, C. (2003). On the trade-off between the future benefits and riskiness of R&D: A
bondholders‟ perspective. Journal of Accounting and Economics, 35(2), 227-254.
Sougiannis, T. (1994). The accounting based valuation of corporate R&D. The
Accounting Review, 69(1), 44-68.
Spiegel, M. I. & Wang, X. (2005). Cross-sectional variation in stock returns: Liquidity
and idiosyncratic risk. Available at SSRN: http://ssrn.com/abstract=709781.
Yale ICF Working Paper No. 05-13; EFA 2005 Moscow Meetings Paper.
Srinivasan, R., Lilien, G. L. & Sridhar, S. (2011). Should firms spend more on research
and development and advertising during recessions? Journal of Marketing, 75(3),
49-65.
Stoll, H. R. (2000). Presidential address: Friction. The Journal of Finance, 55(4), 1479-
1514.
388
Stowe, J. D. & Xing, X. (2011). R2: Does it matter for firm valuation? Financial Review,
46(2), 233-250.
Strong, J. S. & Meyer, J. R. (1990). Sustaining investment, discretionary investment,
and valuation: A residual funds study of the paper industry. In Hubbard, R. G.
(Ed.), Asymmetric information, corporate finance, and investment (pp. 127-148).
Chicago: University of Chicago Press.
Subrahmanyam, A. & Titman, S. (1999). The going-public decision and the
development of financial markets. The Journal of Finance, 54(3), 1045-1082.
Sundaram, R. K. & Yermack, D. L. (2007). Pay me later: Inside debt and its role in
managerial compensation. The Journal of Finance, 62(4), 1551-1588.
Thompson, S. B. (2011). Simple formulas for standard errors that cluster by both firm
and time. Journal of Financial Economics, 99(1), 1-10.
Titman, S., Wei, K. C. J. & Xie, F. (2004). Capital investments and stock returns. The
Journal of Financial and Quantitative Analysis, 39(4), 677-700.
Tobin, J. (1984). On the efficiency of the financial system. Lloyds Bank Review, 153, 1-
15.
Verrecchia, R. E. (2001). Essays on disclosure. Journal of Accounting and Economics,
32(1–3), 97-180.
Vogt, S. C. (1994). The cash flow/investment relationship: Evidence from U.S.
manufacturing firms. Financial Management, 23(2), 3-20.
Vogt, S. C. (1997). Cash flow and capital spending: Evidence from capital expenditure
announcements. Financial Management, 26(2), 44-57.
389
Weidenmier, M. L. & Subramaniam, C. (2003). Additional evidence on the sticky
behavior of costs. Texas Christian University. Available at SSRN:
http://ssrn.com/abstract=36994.
Weiss, D. (2010). Cost behaviour and analysts‟ earnings forecasts. The Accounting
Review, 85(4), 1441-1471.
Welker, M. (1995). Disclosure policy, information asymmetry, and liquidity in equity
markets. Contemporary Accounting Research, 11(2), 801-827.
White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a
direct test for heteroskedasticity. Econometrica, 48(4), 817-838.
White, H. (1984). Asymptotic theory for econometricians. Orlando, FL: Academic Press,
Harcourt Brace Jovanovich.
Wiersema, M. F. & Bowen, H. P. (2008). Corporate diversification: The impact of
foreign competition, industry globalization, and product diversification. Strategic
Management Journal, 29(2), 115-132.
Wooldridge, J. M. (2009). Introductory econometrics: A modern approach (4th ed.).
Mason, Ohio: South Western Cengage Learning.
Wurgler, J. (2000). Financial markets and the allocation of capital. Journal of Financial
Economics, 58(1-2), 187-214.
Xu, N., Chan, K. C., Jiang, X. & Yi, Z. (2013). Do star analysts know more firm-
specific information? Evidence from China. Journal of Banking & Finance,
37(1), 89-102.
390
Xu, Y. & Malkiel, Burton G. (2003). Investigating the behaviour of idiosyncratic
volatility. The Journal of Business, 76(4), 613-645.
Zhang, Y. & Rajagopalan, N. (2010). Once an outsider, always an outsider? CEO origin,
strategic change, and firm performance. Strategic Management Journal, 31(3),
334-346.