1

Chapter 1

INTRODUCTION1. 1 General Background

From the past decades, the financial market has been suffering from the unforeseen and

sudden economic turbulences that have been directly or indirectly contributing for stock

returns movements. Identifying the factors affecting stock returns is not an easy task for

the financial economists, academicians and practitioners. To grasp some ideas through the

systematic procedure, the financial community felt the need of separate discipline so that

the new discipline can solely deals with the management of financial assets. The

investment management and the portfolio theories are the outcomes of such efforts. The

evolution of investment management and the portfolio theory have long history. The

development of investment management can be traced chronologically through three

different phases (Francis, 1986). The first phase could be characterized as the speculative

phase before 1929. During the 1930s investment management entered in its second phase,

a phase of professionalism. Then, the investment industry began the process of upgrading

its ethics, establishing standard practices and generating a good public image. As a result,

the investment markets became safer places and the ordinary people also began to invest.

Investors began to analyze the securities seriously before undertaking investments. Then,

the investment community entered into its third phase, the scientific phase after

Markowitz’s study in 1952.

Markowitz (1952) which is a single-period model and attempted to quantify the risk and it

showed that the risk in investment could be reduced through proper diversification of

investment which required the creation of a portfolio. The Markowitz study was extended

to CAPM in 1960s by Sharpe (1964), Lintner (1965) and Black (1972). The CAPM

explains the overall market performance that determines the stock returns. Then, the

assets valuation models became the most popular area of study in Finance in developed,

developing and the transitional economies. In other words, the history of development of

the portfolio theories and its practices enter into the professionalism and scientific phase.

The empirical evidences of Stattman (1980), Chan, et.al (1991), Brav, et.al (2000), Daniel

and Titman (2006) among others documented the book-to-market equity effects on stock

returns; earnings-to-price effects by Basu (1977), earning effects by Jafee, et.al (1989),

Fama and French (1995) and La Porta (1996) among others; Banz (1981), Vassalou and

Xing (2004) and Fama and French (2008) depicted the size effects, similarly, cash flows

2

effects by Berk, et.al (1999) and Vuolteenaho (2002) among others are the major studies

that documented the firm specific accounting variables are the major sources of stock

returns changes. Whereas in the later period, more focus was given towards the

behavioral aspects like investors’ characteristics and behavioral issues and market

behavior, news effects, media effects, etc. In sum, the recent focus has shifted towards the

intangibles rather than the fundamental effects on stock returns. The studies on human

psychology and behavioral issues, Einhorn, et al. (1978) documented that people have

great confidence in their fallible judgment. Similarly, Einhorn (1980) conformed that the

overconfidence in judgment showed that the contribution of behavioral factors in stock

returns. Ikenberry, et.al (1995), Odean (1999), Kaniel, et.al (2008), Foucault, et.al (2011),

and Doskeland and Hvide (2011), among others, are the major studies documented that

investor behavior is the major aspect of stock returns movements. With these evidences,

the general learning in the investment community is that the event that burst out

expectedly or unexpectedly that has significant impact on investors’ mindset so that such

information plays crucial roles in individual investment decisions making, in totality on

investment performance.

After the evolution of the assets valuation models, there has been the considerable shift of

the literature towards predicting returns and developing the forecasting tools and

techniques. But, there is lack of consensus upon single model, tools and procedures. For

instance, Fama (1972) divided the stock returns into selectivity and risk, changes in

expected future dividends or expected future returns (Campbell, 1991), cash-flow news

effect (Vuolteenaho, 2002) and Daniel and Titman (2006) proved that stock return is a

function of tangible and intangible return. These empirical evidences focused towards the

stock return decomposition which helps to identify the dimensions of returns. Nowadays,

stock returns forecasting became the central issue in Finance and the numerous studies

have been articulated to scan the manifestations of returns. Moreover, the volatile

economic environment also helps to justify these efforts. In the behavioral studies, De

Long, et.al (1990) depicted that the overreaction of prices is due to news, price bubbles

and expectations; sophisticated investors can earn superior returns by taking advantage of

under-reaction and overreaction without bearing extra risk (Barbaris, et al., 1998) and

asset prices are influenced by investor overconfidence (Daniel and Titman, 2000).

Further, the analysis of intangible information is made by Sun and Wei (2011)

documented that investors are overly sensitive to intangible information when they need

to make more subjective judgments. Similarly, many investors consider purchasing only

3

stocks that have first caught their attention (Odean, 2008). These evidences suggest that

the investment decisions are more than models and numbers so that the importance of

financial theories and behavior of the decision makers have been raised significantly. On

top of the behavioral evidences, number of studies revealed that the existence of

relationship of stock returns with, for instance, earnings, cash flows, dividends, returns

itself, market equity (size), book-to-market equity, leverage, etc. Size and book-to-market

equity provide a simple and powerful characterization of the cross-section of average

stock returns (Fama and French, 1992, Daniel and Titman, 1997) and on the contrary,

Kothari, et al., (1995) documented the relationship between book-to-market equity and

stock returns is weaker and less consistent.

Similarly, under the branches of intangibles, the media and news events also effects on

stock returns. The major evidences are: the media coverage, public relations and other

marketing activities could play an important causal role in creating and sustaining

speculative bubbles and fads among investors (Merton, 1987), similarly, Tetlock (2007)

showed the media pessimism effect on stock trading, and Engelberg and Parsons (2011),

among the others are major studies in media effects on stock returns. With the same

fashion, the news events also affect the stock returns (Campbell and Hentschel (1992),

Boyd, et.al (2005), Zhang (2006) and Hirshleifer, et.al (2009), among others).

Apart from the voluminous studies in the developed and western economies, limited

studies have been conducted in the developing and transitional economies like Nepal. The

positive relation between stock returns and size where as inverse relation between returns

and market-to-book value (Pradhan, 1993), the positive relation of stock returns with

earning yield and size whereas negative relation with book-to-market ratio and cash flow

yield and book-to-market value (Pradhan and Balampaki, 2004). These studies provided

the evidences that book-to-market equity and size are the major determinant of stock

returns even if the capital market is inefficient in nature.

The study of the stock returns and market information occupies an important place in

financial management. It has received much attention in recent years for identifying the

market signals to achieve relatively higher stock returns. The evidences on the stock

returns and market information indicate that this area is useful for financial decision

making process. More specifically, the insight from the analysis of stock returns and

market information are useful to achieve the short-run stock returns while the market

became more volatile due to various influences like the news effect, political effect, the

4

fundamental information disclosure effect, etc. The momentum and trend in varying

circumstances, magnitude and directions help to pretend the future development of stock

market. In general, the market signals provide in-depth knowledge about the effects and

ranges of market information in different period of time. Thus, the market deserved the

need for extensive studies on market information and stock returns thus became an

important area of study in recent years. With this perspective, the study devoted to market

information and stock returns may be a rewarding one both for the academicians and

practitioners.

1.2 Statement of the problem

Financial economists and investors have spent considerable time searching for investment

strategies that could help to yield sustainably above the average returns but, the reliable

one is yet to be found. Several studies have been attempted to identify the most important

and the consistent firm specific fundamental variables which help to explain the specific

effects on stock returns and price movements. For instance, earning yield effect of Basu

(1977), size effect of Banz (1981), leverage effect of Bhandari (1988), book-to-market

effect of Stattman (1980 ), joint effect of beta, size, leverage, book-to-market equity and

earning yield of Fama and French (1992), book-to-market equity and cash flow yield

effect of Chan, et.al (1991) and the price-scaled variables: sales to price, cash flow to

price, earning to price and book to price ratios of Daniel and Titman (2006) are some of

the major studies.

Beyond the firm specific accounting variables as market information and its effect on

stock returns, the intangible information effect like; the media effect, news event effect,

political party led government, lag variable effects, past performance, stock market

behavior and investors’ sentiments, etc have also been contributing for the market price

movements. Some field evidences showed that news events lead some investors to react

more quickly. Among others, past long-term losers have outperformed past long-term

winners (“long-term reversal,” De Bondt and Thaler (1985)), past short-term winners

have outperformed past short-term losers (“momentum,” Jagadeesh and Titman (1993)),

high book-to-market-equity firms (“book-to-market anomaly,” Rosenberg, et.al (1985)),

controlling for other characteristics, firms with higher profitability have earned higher

average stock returns (Haugen and Baker (1996)), high-leverage firms have historically

outperformed low-leverage firms low-leverage firms (Bhandari’s (1988) “leverage

effect”), are some major evidences. From the different stand point in Finance literature,

financial economists have puzzled over the two observations. First, over the long

5

horizons, future stock returns are inversely related to the past performance. Second, stock

returns are positively related to price-scaled variables; earning yield, cash flow yield,

book-to-market equity, etc.

The De Bondt and Thaler (1985, 1987) and, LSV (1994) studies argued that the reversal

and book-to-market effects are a result of investor overreaction to the firm’ past

performance. In contrast, Fama and French (1995, 1996) argued that, since past

performance is likely to be negatively associated with changes in systematic risk, high

book-to-market firms are likely to be riskier and hence require higher expected returns. In

former studies, investors overreact to the information contained in accounting growth

rates, and later studies shows that the increased risk and return of high book-to-market

firm is the result of distress brought about by poor past performance. Thus, the study

initiates to explain the differences that exist in the previous studies.

The sound returns on investments visibly attract the initial investment. The returns

comprise the dividend plus the capital gains. The future prospects and the market

opportunities also help to determine the level of investment either in terms of equity or

debts. The relationship developed by Van Rooij, et al. (2007) is a significant association

between financial literacy and investment decisions. Even though, the financial crisis

occurred in 2008 it has heightened the institutional as well as individual investors’

awareness in the field of financial decision making. The literacy and the technological

advancement contribute to score the timely and quality information. The evidence

suggested that there is an association between stockholding and computer and Internet use

(Bogan, 2006). On the other hand, Lusardi and Mitchell (2006) revealed that the negative

association between planning for retirement and financial education. These evidences also

suggest that the additional factors – investor awareness, financial education and the

financial literacy that contributes to the stock market movement also play as market

reactors.

Now, it is important to realize that stock return is a function of multiple interacting factors

in the capital market. It has been gradually influencing by the defined and undefined

factors. The information available in the market could be disseminated by the

management or could be developed through the end of invisible sources. The magnitudes

of the information that incorporated in stock prices are determined by the nature and form

of the capital market. Along with the information effect, the variation in stock prices can

also be affected by the future prospects and the other unseen factors. Thus, the study

6

helps to enhance the knowledge by decomposing the stock returns and market

information. There are a number of ways to decompose the information that influence the

stock prices. For instance, Fama (1972) segregate the stock returns into selectivity and

risk; Campbell (1991) decompose the stock returns into a component that reflects

information about cash flows, and a second component that reflects information about

discount rates; Daniel and Titman (2006) decompose the returns into tangible and

intangible returns. Similarly, the study decomposes the information into two components;

the first one is firm’s past and current performance that is described in its financial

statements are treated as tangible information which is relatively concrete and, which is

by definition orthogonal to the tangible information is refer to as intangible information.

More specifically, the financial indicators that can be generated from the financial

statement of the enterprises are categorized into tangible parts and the other information

which is not tangible and orthogonal to the tangible information is categorized into

intangible parts. In light of the separation of market information into two components, the

study also decompose the stock returns into tangible return –which is associated with past

performance or supported by the tangible information and intangible return – which is

unrelated to past performance of the firm itself or backed by the intangible information.

The decomposition results might be a useful procedure to grasp the far sights in the

capital market so that one can perform well than others.

The study deals with the following issues:

o What is the relationship between past tangible information and future returns?

o Is there relationship between past intangible returns and future returns?

o Is there association between the fundamentals to price scaled variables with the future

returns?

o Do the stock prices overreact to the past performance?

o What is the most predictable fundamental accounting growth measure in stock

exchange?

o How long the past fundamentals help to predict the market returns?

o What are the news effects on stock returns? What is the bad news effect? What is the

good news effect? and what is the informational news effect?

o Does the political leadership influence on Nepalese stock market? What are the

effects of NC led government? CPN-UML led government? King led government?

and, UCPN(M) led government?

7

o What are the opinions of Nepalese stock investors on investment alternatives, decision

making, market prices and stock returns?

o What are the factors affecting investment decision making in equity investment?, and,

o What are the opinions of stock investors on various issues like stock returns,

fundamental measures, mutual funds, central depository system, portfolio

management services, credit rating agencies, sources of investment funds, rate of

interest, the trading behavior on different conditions, and on the various emerging

issues in stock market performance?

1.3 Objectives of the study

The basic objective of the study is to analyze the market information and stock returns in

Nepalese stock market. The following are the specific objectives of the study:

o To evaluate the relationship between stock returns and fundamental measures.

o To determine the news effects – bad news, good news and informational news, on

stock returns.

o To examine the political leadership effects on stock returns.

o To determine the factors affecting stock investment in Nepalese stock market.

o To examine the investor opinions on various such as investor education and

personality type, preferences, trading behavior and practices, sources of funds for

investment, risk perception, level of investor awareness, investor reactions and

judgments on previous findings of the similar studies.

1.4 Hypotheses

In order to achieve the above objectives, the study attempts to test the following

hypotheses related to the market information and stock returns in Nepal.

o There is significant relationship between the past tangible returns and future returns.

o There is significant relationship between the past intangible returns and future returns.

o The firm specific variables have strong relationship with its stock returns.

o There is significant relationship between stock returns and news coverage.

o There is significant relationship between political leadership and stock market returns.

1.5 Organization of the study

The study is organized in five chapters. The overall background of the study, statement of

the problem, issues of the study, basic and specific objectives, and the hypotheses have

8

been included in first chapter. The conceptual framework and review of some major

studies in the field of market information and stock returns have been summarized in

chapter two. The review of literature section has been divided into nine categories

excluding review of Nepalese context. These nine parts has been organized as per

separate variable in chronological order and the concluding remarks of the review section

have been presented at the end of the chapter. Subsequently, research methodology of the

study has been presented in third chapter which describes the research design, nature and

sources of data, selection of the sample enterprises, sector-wise distribution of the listed

enterprises, population and sample for primary and secondary database, the methods of

data analysis are broadly divided into two subsections – secondary data analysis and

primary data analysis. Besides the descriptive statistics, correlation matrix analysis,

portfolio formation, regression analysis, Kolmogorov-Smirnov test, stock returns

decomposition, the test of significance, etc, are the major analysis under the section of the

secondary data analysis. Further, the primary data analysis includes the descriptive

statistics of demographic variables along with percentage, frequency distribution, simple

and cross table analysis, mean score analysis, the test of association – chi-square test, and

the factor analysis which includes: Cronbach’s Alfa test, the correlation matrix analysis,

anti-image correlation matrix – the measure of sampling adequacy (MSA), Kaiser-Meyer-

Olkin and Bartlett's Test, the initial and rotated solution for factor analysis, and the scree

plot are used. The concluding remarks have been shown in the final sector of chapter

four. Finally, in chapter five, the summary and the conclusions of the study along with the

recommendations for the stakeholders of Nepalese capital market have been presented.

9

Chapter 2

REVIEW OF LITERATURE

The chapter contains the review of literature on market information and stock returns

which is organized into four sections. The conceptual framework has been presented in

the first sector. The second part includes the review of major empirical studies on stock

returns and market information. The related studies in Nepalese context have been

presented in the third section. Finally, the forth section has been devoted for concluding

remarks.

2.1 Conceptual framework

Early from the twentieth century, the financial literature focuses towards the assets

valuation which intends to identify the real values of the assets. The valuation works for

the assets like tangible and intangible financial assets, and for the liabilities. The market

information which is considered as a weapon for market volatility influences the

valuations models significantly time and again. For many reasons in finance, assets

valuation is at the heart of financial economics and especially for the corporate finance.

Thus, the market information is considered as one the most important factor that

incorporates many things at the same time and that influence the securities prices

regularly. Among the financial securities, common stock is a most popular and the most

practiced financial assets in most of the economies. The exchange of equity is possible at

the organized stock exchanges or over-the-counter market which is based on the free flow

of demand and supply assuming that the equilibrium market price.

The security prices depend upon number of interacting factors. Some of them are

measurable by nature. For instance, the firm specific accounting variables, the macro-

economic indicators, etc and the other qualitative factors are difficult to measure such as

investors’ psychology, selective investment behavior, attitude and perception, the politics,

etc. The classification of these interacting variables in terms of their measurement can be

termed as tangible and intangible variables, respectively. Regarding the investment in

financial securities like common stocks, bonds, and the financial derivatives: options,

swap, futures, forward, etc, the tangible and intangible variables plays the crucial roles in

decision making process as well as for the investment performance.

ProductivityEfficiencyFinancial Statements

Relative Business Strength of the Firm

Stock returns

Demand and SupplyMarket Competition

Figure 2.1 Conceptual framework of stock returns

10

The investment is the postponement of current spending for the future purpose with the

expectation of financial benefits as the compensations for the investor’s sacrifice. In

general, to satisfy the long-term commitment of the funds, the stock returns should be

acceptable or at least equal to market compensation. If it is deviated from the benchmark

level, there would be a problem of withdrawal of long-term commitment of funds or that

could create a problem of mispricing of the securities. The expectation of stock returns

might be different, among the other factors, one of them could be the expected future

dividends or it could be the expected future returns (dividends plus capital gain). In

practice, the framework of stock return can be conceptualized as follows:

The stock returns framework in Figure 2.1 indicates that the financial statements replicate

the demand and supply, and the market competition as well as the productivity and

efficiency on the other hand. The financial statements strengthen the business strength of

the firm so as the direct relationship with stock returns. On top of that, the change of

dividend news; the change in expected returns with the change in expected dividends; an

innovation in the expected return today might have the implications for distance future;

and also shocks in expected future dividends might have been the correlational effect on

share prices . Thus, the stock prices have the significant effects of quantitative or the

tangible as well as qualitative or the intangible information, which is popularly known as

market information.

In finance, the basic question that has been stimulated voluminous research and became a

heated debate is: what moves the stock price or the stock returns? Some studies have been

Total Return

Figure 2.2 - Graphical presentation shows the breakdown of a firm’s past return into tangible and intangible returns.

Log(Pt-5)

t-5 t

Log (Pt)

Log (Pˆ)

Log (Pt-5)

Intangible Return

Tangible Return

11

trying to identify the factors by using stock returns decomposition. In case of the stock

returns decomposition it is the process of splitting the total stock returns into different

parts for instance, returns for selectivity and returns for timing, expected future dividends

and expected future returns, cash flow expectations and discount rates, tangible and

intangible returns, etc. Some other studies use the fundamental variables to determine the

factors affecting stock returns. Moreover, the risk and return trade-off is a primitive

theory of stock return movements.

The study based on the quantitative and qualitative approach, use some accounting

variables: stock price, annual yield, numbers of common stock outstanding, book value of

equity, earnings, sales and cash-flow for the analysis. Further, the news headlines –

positive news, negative news and informational news, and political leadership effects is

proposed for the proxies of intangible information for stock returns. It is also assumed

that the log price-per-share is equal to the log returns i.e. as price increases, return

increases. A cross-sectional regression of 5-year log returns on firm specific growth

measures – book value, earnings, cash-flow, and sales growth or all of these will perform

to calculate the expected log (Pt). For a given firm at a given point in time, the expected

log (Pt) is the summation of the firm’s expected log price at t conditional on log (Pt-5) and

its unanticipated fundamental growth between t-5 and t. The study defined that a given

firm’s tangible return illustrated by the dashed line in the figure, and its intangible return

is the residual. In other words, the tangible return as the past 5-year stock return that

would be expected based solely on the past fundamental growth measures. The intangible

return is then the part of the past return that remains unexplained and presumably is the

result of an investors’ response to information which is not contained in the accounting

growth measures. The framework is presented in Figure 2.2 as follows:

Tangible Information Intangible Information

Market Information

Total Stock Returns

Tangible Returns Intangible Returns

LE

AD

S

Figure 2.3 Conceptual Framework of market information and stock returns (the broader perspective)

Tangible / Quantitative InformationMarket Information

B/M Equity

Market BehaviorMarket Reaction

Earnings

Cash Flow

12

After the description of the variables used, if it is assumed in a close system, the

interaction of tangible and intangible information with the tangible returns and intangible

returns can be conceptualized and presented in Figure 2.3 as follows:

The market information comprises the tangible information and the intangibles. Similarly

the total stock return is made up of the tangible returns and the intangible return, whereas

there is direct relationship between tangible components and the intangible components.

In sum, the total stock returns moves as on the market information within the close

system.

More specifically, the relationship of the tangibles and intangibles can also be presented

with its interacting components. The cluster as well as the interaction of these

components can be presented in Figure 2.4 as below:

13

Since, the tangibles are the quantifiable variables such as book-to-market equity, cash

flow, earnings, size or the number of common stock outstanding, lagged stock returns, etc

which has the direct linkage with the tangible stock return and a major contribution for

the total market information so as to the stock price movements. Secondly, the qualitative

or the intangible variables generate the intangible information which has the direct

relation with intangible returns. The components of intangibles include behavioral issues

and it is very difficult the grasp the consistent signals of such components, such as: social

and individual values, individual and group psychology, sentiments, overconfidence,

overreactions and under-reactions, news effects (events), media effects, market reactions,

market behaviors, investors behavior, etc. These components contribute the remaining

part of the market information that has uncovered by the tangibles.

With these stated interrelationship between and among the firm specific variables and the

external variables, it is clear that there is direct relationship between the tangible

Irregular Information Flow (b) & (c)

Risk

Return

Rf

Figure 2.6 (b) Bad News

Bad News Events

Risk

Return

Rf

Good News Events

Regular Information Flow (a)

RiskR

eturn

Rf

Figure 2.5 (a) Normal

14

information and tangible return, intangible information and intangible return, further, the

market information comprises the tangible and intangible information, similarly, the total

stock returns made up of the tangible returns and the intangible returns. Thus, there is the

interrelationship between the market information and total stock returns in a close system

approach.

The most popular traditional relationship between risk and return can be explained by the

risk-return trade-off principles. With the same notion, when the news effect is

incorporated with this primitive approach, it can be described as: regular information flow

in Figure 2.5 (a) and its effect on risk and returns, and irregular information flow and its

effect on risk and returns is presented in Figure 2.6 (b) and Figure 2.7 (c) respectively.

More specifically, the irregular news flow can be divided into good news and bad news.

The relationship between news events, risk and returns can be presented as follows:

The traditional risk-return trade-off describes the positive relation between risk and return

i.e. as risk increases, return also increases and vis-à-vis. When introducing the news

effects, assuming that risk is constant, bad news serve as a negative stimulus to the

market so as the market perceive it negatively whereas good news contribute as positive

stimulus to the market so as the market take it positively. Therefore, the conceptual

relationship between risk and return with news events indicate that bad news leads to

market slash and inversely - the good news leads the market growth.

2.2 Review of Major Empirical Studies

The review of major empirical studies on market information and stock returns has been

organized into nine categories excluding the Nepalese studies. The studies are categorized

Figure 2.7 (c) Good News

15

based on their qualitative and quantitative nature. The grouping under each category is

managed as per the time period and based on their similarities. The major classifications

of the selected studies are as follows:

a) Fundamental effects on stock returns

i) Book-to-market effects

ii) Cash flow and earnings effects

iii) Size effects

b) Stock return analysis, return decomposition and methodology effects

i) Stock return analysis

ii) Stock return decomposition and methodology effects

c) Investors’ behavior studies

i) Studies before 2000

ii) Studies during 2000s

d) Studies related to initial public offerings (IPOs)

e) Studies on stock market behavior

i) Studies prior 1990

ii) Studies 1990 onwards

f) Market reactions to tangible and intangible information

i) Studies on intangible information till 2000

ii) Studies on intangible information between 2000 and 2010

iii) Studies on tangible information before 1990

iv) Studies on tangible information during 1990s

v) Studies on tangible information 2000 onwards

g) Studies related to media effects

h) News events effects on stock returns

i) Studies related to investors overconfidence

i) Studies before 2000

iii) Studies 2000 onwards

a) Review of major studies on fundamental effects on stock returns

16

The review of fundamental effects on stock returns is organized into four sub-section

comprising – book-to-market effects, cash flow effects, earning effects and size effects.

The review covers the major studies on respective variables during 1981 to 2008.

i) Review of major studies on book-to-market effects

The book-to-market effects on stock returns from the period 1991 to 2006 have been

presented in the Table 2.1 below:

Table 2.1: Review of major studies on book-to-market effectsStudy Major findingsChan, et.al (1991) The book to market ratio and cash flow yield has the most significant positive

impact on expected returns.

Davis (1994) Book-to-market equity, earnings yield, and cash flow yield have significant explanatory power with respect to the cross-section of realized stock returns and, there was a strong January seasonal in the explanatory power of book-to-market equity, earning yield and cash flow yield.

Brav, et.al (2000) Underperformance is concentrated primarily in small issuing firms with low book-to-market ratios.

Daniel and Titman (2006)

Book-to-market equity ratio, a good proxy for intangible return forecasts returns. A composite equity issuance measure, also as an intangible information independently forecasts the future returns.

The study analyzed the cross-sectional differences of Japanese stocks returns to the

underlying behavior of the variables: earnings yield, size, book-to-market ratio, and cash

flow yield, Chan, et.al (1991). Seemingly Unrelated Regression (SUR) model and Fama-

Mac-Beth (1973) methodology are applied on comprehensive, high-quality monthly data

set of stocks listed on the Tokyo Stock Exchange (TSE) that extends from 1971 to 1988.

The sample includes both manufacturing and nonmanufacturing firms, companies from

both sections of the Tokyo Stock Exchange, and also delisted securities. The findings

revealed the univariate analysis of stock returns and fundamental variables indicated that

high earnings yield stocks outperform low earnings yield stocks; small stocks achieved

substantially higher returns than large stocks; the firms with large positive book-to-

market equity ratio earned high premium than firms with low; and positive book-to-

market equity. Further, cash flows yield is found to have positive relation with stock

returns. However regression analysis produced striking results: the earning yield effect

was not significant across the different regressions models and it was not even significant

when earning yield was the only independent variables. Firm size, in general, is

significant with an unexpected sign meaning that large companies in Japan tend to

outperform small companies. The performance of book-to-market equity is statistically

and economically the most important among the four variables investigated. Although,

the study confirmed the existence of size effect after adjusting for market risk and other

17

fundamental variables, the statistical significant of the size variable is sensitive to the

specification of the model. Of the four variables investigated, though, it is hardest to

disentangle the effect of earnings yield variable. In sum, the book to market ratio and cash

flow yield has the most significant positive impact on expected returns.

The 100 firms listed in the Moody’s Industrial Manual which is free from the

survivorship bias and four fundamental variables: book-to-market equity, cash flow yield,

earning yield and historical sales growth as primary focus of the study (Davis, 1994).

Stock returns, stock prices and market values of equity were derived from the CRSP

monthly file. The study uses the Fama-MacBeth (1973) cross-sectional regression model

to determine the explanatory power of realized returns from 1940 to the early 1960s, the

pre-COMPUSTAT era. The findings of the study includes: significant relationship

between book-to-market equity and subsequent returns, cash flow yield has explanatory

power with respect to subsequent realized returns when book-to-market equity and

historical sales growth are held constant, earning yield has also explanatory power to

predict subsequent returns, insignificant explanatory power for beta to predict returns,

weak relationship between sales growth and returns, and there is significance of log book-

to-market, earning to price, and cash flow to price with returns mostly in January. Thus,

the study concluded that book-to-market equity, earnings yield, and cash flow yield have

significant explanatory power with respect to the cross-section of realized stock returns

and there was a strong January seasonal in the explanatory power of book-to-market

equity, earning yield and cash flow yield.

Brav, et.al (2000) examined whether a distinct equity issuer underperformance anomaly

exists is the major focus of the study. Sample of initial public offering (IPO) and seasoned

equity offering (SEO) of the firms from 1975 to 1992 derived from CRSP for NYSE,

ASE and NASDAQ. The sample included 4526 offerings made by 2772 firms. The study

found that underperformance is concentrated primarily in small issuing firms with low

book-to-market ratios. SEO firms that underperform these standard benchmarks have

time series returns that covary with factor returns constructed from non-issuing firms. The

study concluded that the stock returns following equity issues reflect a more pervasive

return pattern in broader set of publicly traded companies.

Book-to-market equity ratio forecasts stock returns because it is a good proxy for

intangible returns. Further, composite equity issuance measure, which is related to

intangible returns, independently forecasts returns (Daniel and Titman, 2006). The book-

18

to-market effect is often interpreted as evidence of high expected returns on stocks of

distressed firms with poor past performance. The study also found that while a stock’s

future return is unrelated to the firm’s past accounting-based performance, it is strongly

negatively related to the intangible return, the component of its past return that is

orthogonal to the firm’s past performance. Other findings of the study are: stock returns

over a relatively long horizon (5 years) should be closely linked to concurrent

fundamental performance; there is a strong positive relation between intangible returns

and future fundamental performance measures i.e. a firm’s intangible returns reflects, at

least partial information to its future growth prospects, there is no evidence of any link

between past tangible information and future return; there is strong negative relation

between past tangible returns and future returns; future returns are unrelated to internally

funded growth in sales; future returns are strongly negatively associated with growth that

is financed by the share issuance; composite share issuance variable is significantly

negatively related to future returns; the strong intangible return and issuance effects that

cannot explain the existence of mispricing; low book-to-market firms have both higher

future accounting growth rates and lower future returns; negative correlation between the

lagged book-to-market ratio and book-return; and the composite share issuance measure

is strongly negatively related to future returns.

ii) Review of major studies on cash flow and earnings effects

The cash flow is considered as the fundamental variable and the variation in cash flow

might be the causes of changes in the stock returns. The major previous studies on cash

flow from 1999 to 2002 have been presented in Table 2.2 as follows:

An analysis on optimal investment, growth options and security returns is conducted by

Berk, et.al (1999). The interest of the study is the individual firm. The random evolution

of the firm’s collection of projects determines how its risk and return change over time. In

the study, the partial equilibrium model gives the tractability to focus on the dynamics for

the relative risks of individual firms. The study found that as a consequence of optimal

investment choices, a firm’s assets and growth options change in predictable ways. In the

study, the dynamic model imparts predictability to changes in a firm’s systematic risk,

and its expected returns. Simulations showed that the model

Table 2.2: Review of major studies on cash flow and earnings effectsStudy Major findingsBerk, et.al (1999) The valuation of the cash flows that result from the investment decision making by

the individual firms, along with the firm’s opinions to grow in the future, leads to dynamics for conditional expected returns.

19

Vuolteenaho (2002)

Firm-level stock returns are mainly driven by cash-flow news.

Jafee, et.al (1989) A significant relation between returns and earnings only in the month of January and, the size effect was negative only in January.

Fama and French (1995)

There are market, size, and BE/ME factors in earnings like those in returns.

La Porta (1996) Earnings growth is the only variable with the significant explanatory power in explaining stock returns.

simultaneously reproduces: the time-series relation between the book-to-market ratio and

asset returns; the cross-sectional relation between book-to-market ratio, market value, and

returns, contrarian effects at short horizons; momentum effects at longer horizons and the

inverse relation between interest rates and the market risk premium. The study simulated

20,000 months of data for 50 firms and restrict the attention to firms that have reached a

steady state distribution for the number of ongoing projects by dropping the first 200

observations. In addition to dynamic and simulation models, FM regression models,

varying types of frequency distributions are used for the analysis. The findings of the

study concluded that the valuation of the cash flows that result from the investment

decision making by the individual firms, along with the firm’s opinions to grow in the

future, leads to dynamics for conditional expected returns. The model of expected returns

in the study helps explain a number of the important features of the cross-sectional and

time-series behavior of stock returns, and the biases that might be induced by the model

that ignores these dynamics. On the other hand, the simulation results showed that the

model can reproduce simultaneously several important cross-sectional and time-series

behaviors that studies have documented for stock returns, including the explanatory

power of book-to-market value, and interest rates, and the success of contrarian and

momentum strategies at different horizons.

Vuolteenaho (2002) conducted a study on firm-level returns, where author use a vector

autoregressive model (VAR) to decompose an individual firm’s stock returns into two

components: changes in cash-flow expectations i.e. cash-flow news and changes in

discount rates i.e. expected-return news. By definition, a firm’s stock returns are driven

by shocks to expected cash flows (cash-flow news) and/or shocks to discount rates

(expected-return news). Substantial studies have been done to measure the relative

importance of cash-flow and expected-return news for aggregate portfolio returns, but

virtually no evidence on the relative importance of these components at the firm level.

The basic data for the study derived from the CRSP-COMPUSTAT intersection, from

1954 to 1996. CRSP monthly stock file contains the data of monthly prices, shares

outstanding, dividends, and returns for NYSE, AMEX, and NASDAQ stocks,

20

COMPUSTAT contains the relevant accounting information for the most publicly traded

U.S. stocks and the study, in addition, used rolled-over one month Treasury-bill returns as

risk-free rate. Based on the VAR and Campbell’s (1991) return-decomposition

framework enable the study to decompose the firm-level stock returns into cash-flow and

expected-return news and to estimate how important these two sources of stock variation

are for an individual firm. In addition, the study measure whether positive cash-flow news

is typically associated with an increase or decrease in expected returns. The findings of

the study includes - the information about future cash flows is the dominant factor driving

firm-level stock returns, cash-flow news is positively correlated with expected returns for

a typical stock. Finally, it is appeared that while cash-flow information is largely firm

specific, expected-return information is predominantly driven by systematic,

macroeconomic components. In sum, VAR yields three main results. First, firm-level

stock returns are mainly driven by cash-flow news. For a typical stock, the variance of

cash-flow news is more than twice that of the expected-return news. Second, the expected

returns and cash flows are positively correlated for a typical small stock. Third, expected-

return-news series are highly correlated across firms, while cash-flow news can largely be

diversified away in aggregate portfolios.

The study uses the CRSP monthly stock return data for relatively a longer period from

1951 to 1986 and from the “back data” versions from 1950-1966 periods. Jafee, et.al

(1989) evaluated the relation between size and earnings yield effects on stock returns.

Over the entire period, the study reported a significant relationship earnings and stock

returns only in the month of January, while it is observed a significant relation during all

months of the sub-period 1969-1986. Conversely, the size effect is found significantly

negative only in January in the overall period and in both sub-periods.

Fama and French (1995) analyzed whether the behavior of stock prices in relation to size

and book-to market-equity (BE/ME) reflects the behavior of earnings. The study focused

on six portfolios, formed yearly from a simple sort of firms in to two group on market

equity and another simple sort into three groups on book-to-market equity. Further, it is

study attempted to provide an economic foundation for empirical relations between

average stock returns and size, and average stock returns and book-to-market equity

observed in Fama and French (1992). Consistent with rational pricing, high BE/ME

signals persistent poor earnings and low BE/ME signals strong earnings. Moreover, stock

prices forecast the reversion of earnings growth observed after firms are ranked on size

and BE/ME. The evidence that size and book to market equity proxy for sensitivity to risk

21

factors in returns is consistent with a rational pricing story for the role of size and BE/ME

in average returns. Specifically, the analysis of whether the behavior of stock prices, in

relation to size and book-to-market equity, is consistent with the behavior of earnings. In

a nutshell, low BE/ME, a high stock price relative to book value, is typical of firms with

high average returns on capital (growth stocks), whereas high BE/ME is typical of firms

that are relatively distressed. Size is also related to profitability, controlling for BE/ME,

small stocks tend to have lower earnings on book equity than do big stocks. The tests

center on six portfolios formed on ranked values of size and BE/ME for individual stocks

i.e. profitability, earnings, profitability in chronological time, earnings/price ratios,

earnings growth rates, and stock returns. Then, the overall analysis examines the links

between returns and these common factors in earnings and established that the level of

earnings is related to size and BE/ME. The study is based on the data from 1963 to 1992

of NYSE, AMEX and NADSAQ. Information was abstracted from the CRSP. Groups are

formed based on the breakpoints for the bottom 30 percent (Low), middle 40 percent

(Medium), and top 30 percent (High) of the ranked values of BE/ME for NYSE stocks

and do not consider the negative BE firms. Thus, the overall relationship of variables

among the portfolios, analysis of regression results suggest that there are market, size, and

BE/ME factors in earnings like those in returns.

Further, La Porta (1996) examined whether investors make the systematic mistakes that

are consistence with the errors in expectation hypothesis when growth in earnings. The

study employed CRSP monthly returns files of the listed companies of NYSE and

AMEX. Annual portfolio returns are constructed by compounding monthly returns. The

regression results reported that earnings growth as the only variable with the significant

explanatory power. The study revealed that the earnings growth is the only significant

variable in multivariate regression when it is combined with size, book-to-market equity,

and cash flow to price ratio. The regression results confirmed the role of the expected rate

of earnings growth in explaining stock returns. The findings are based on multivariate

regression models which reported the negative relation of expected returns with book-to-

market equity, size and earnings growth and positive relation with cash-flow yield. When

stock were sorted by expected growth rate in earnings, it is shown that low earnings

growth stock beat high earnings growth stock by twenty percentage points. The study

further documented that there is no evidence that low earnings growth stocks are more

risky than high earnings growth stocks. When portfolios were formed on the basis of

22

expected growth rate in earnings, the results indicated that low earnings growth stocks

have significantly lower standard deviations and betas than high earning growth stocks.

iii) Review of major studies on size effects

The size is defined as the market value of common stock outstanding and it is also called as fundamental variable for stock returns. The major studies including some seminal works have been presented in Table 2.3 below. The review of size effect on stock returns covers the period 1981 to 2008.

Table 2.3: Review of major studies on size effectsStudy Major findingsBanz (1981) Small firms, on average, have significantly larger risks adjusted returns than large

firms.Fama and French (1992)

Size (ME) and book-to-market equity (BE/ME) provide a simple and powerful characterization of the cross-section of average stock returns.

Fama and French (1993)

Portfolios constructed to mimic risk factors related to size and BE/ME add substantially to the variation in stock returns explained by a market portfolio.

Daniel and Titman (1997)

There is no evidence of a separate distress factor and, it is characteristics (size & book-to-market) rather than factor loadings that determine expected returns.

Daniel, et.al (2001)

In equilibrium, there is ability of fundamental/price ratios and market value to forecast stock returns, and the domination of beta by these variables.

Vassalou and Xing (2004)

Both the size and book-to-market effects can be views as default effects which are in sum the case of size effect.

Fama and French (2008)

The anomalous returns associated with net stock issues, accruals, and momentum are pervasive; they show up in all size groups (micro, small, and big) in cross-section, and they are also strong in sorts, at least in the extremes.

The relationship between total market value of equity and common stock returns is

examined by Banz (1981). The study covered the observations from 1926 to 1975, and

included all common stocks listed in the NYSE. Data were derived from monthly returns

file of the CRSP, University of Chicago. Using pooled cross-sectional and time series

regression, the study reported that small NYSE firms, on average, have significantly

larger risks adjusted returns than large NYSE firms. The evidence suggested that the

CAPM is not correctly specified. However, the size is not linear in the market protection

but is most pronounced for the smallest firms in the sample. The effect is not very stable

through time. An analysis of the ten year sub-period showed substantial differences in the

magnitude of the coefficient of the size factor. Finally, the study concluded that there is

no theoretical foundation for such an effect, and it is not confirmed whether the factor is

size itself or whether size is just a proxy for one or more true but unknown factors

correlated with size. Therefore, the study reasoned that it is possible, however to offer

some conjectures and even discuss some factors for which size is suspected to proxy.

The observations starting from July 1963 to December 1990, Fama and French (1992)

conducted a analysis on the cross-section of expected stock returns. In area of portfolio

23

management several studies have been undertaken to specify the characteristics of stock

returns. Among the others, CAPM is the most popular model uses a single factor, beta, to

compare a portfolio with the market as a whole. Then, some research findings showed

contradictory results in the literature of finance with CAPM. The motivation of this

research is guided by such evidences. The purpose of the study is to evaluate joint roles of

market beta, size, earning yield, leverage, and Book to Market Equity in the cross section

of average stock returns on NYSE, AMEX, and NASDAQ stocks. The study capture the

cross-sectional variation in average stock returns associated with size (ME) and book-to-

market equity. Sample included are all non-financial firms listed in NYSE, AMEX and

NASDAQ and accounting information were collected from CRSP and COMPUSTAT

database. Fama and MacBeth (1973) regression is used for the analysis. The study

revealed strong relationship between the average stock returns and size, but there was no

reliable relation between average returns and beta. When the stock returns is sorted based

on earnings yield, a familiar U-shape relation is observed. The relation between average

returns and book-to-market equity is strongly positive. The FM regressions also

confirmed the importance of book-to-market equity in explaining the cross section of

average stock returns. This book to market equity relation is found stronger than the size

effects when both size and book-to-market equity were included in multivariate

regressions. The author reported book-to-market equity is consistently the most powerful

factor explaining the cross-section of average stock returns, whereas size effect was found

weaker. Based on the regression results and analysis of portfolios, the study concluded

that size and book-to-market equity provide a simple and powerful characterization of the

cross-section of average stock returns.

The common five risk factors for stocks and bonds returns are idenified by Fama and

French (1993). Three factors: an overall market factors, factor related to firm size and

book-to-market equity are the stock market related factors. For instance if the portfilos

constructed on mimic risk factors related to size and BE/ME, capture a strong common

variation in returns as the evidence that size and book-to-market equity indeed proxy for

sensitivity to common risk factors in stock returns. Other two bond market factors: default

risks, and factor related to maturity, are the bond risk factors. Stock returns have shared

variation due to the stock market factors, and which are linked to the bond returns through

shared variation in the bond market factors. Mostly the bond market factors capture the

common variation in bond returns, except for low-grade corporates. Most importantly,

these common risk factors seem to explain average returns on stocks and bonds. On the

24

other hand, variables that have no special standing in asset pricing theory shows reliable

power to explain the cross-section of average returns. The list of empirically determined

average stock returns variables includes size (ME, stock price times number of shares),

leverage, earning price ratio (E/P), and book-to-market equity (the ratio of the book value

of a firm’s common stock, BE to its market value, ME). The study employed the time-

series regression approach of Black, et.al (1972). Monthly returns on stocks and bonds are

regressed on the returns to a market portfolio of stocks and mimicking portfolios for size,

book-to-market equity, and term-structure risk factors in returns. The time-series

regression slopes are factor loadings that are unlike size or book-to-market equity have a

clear interpretation as risk-factor sensitivities for bonds as well as for stocks. Thus, Fama

and French (1993) confirm that portfolios constructed to mimic risk factors related to size

and BE/ME add substantially to the variation in stock returns explained by a market

portfolio. Moreover, a three-factor asset-pricing model that includes a market factor and

risk factors related to size and BE/ME seems to capture the cross-section of average

returns on U.S. stocks.

There is now considerable evidence that the cross-sectional pattern of stock returns can be

explained by characteristics such as size, leverage, past returns, dividend-yield, earnings-

to-price ratios, and book-to-market ratios (Fama and French, 1993). The study argued that

the association between these characteristics and returns arise because the characteristics

are proxies for non-diversifiable factor risk. Whereas, Fama and French (1992, 1996)

examine all of these variables simultaneously and concluded that with the exception of

the momentum strategy described by Jegadeesh and Titman (1993) the cross-sectional

variation in expected returns can be explained by only two of these characteristics, size

and book-to-market. Firm sizes and book-to-market ratios are both highly correlated with

the average returns of common stocks. In contrast, the evidence of the study indicates that

the return premia on small capitalization and high book-to-market stocks does not arise

because of the co-movements of these stocks with pervasive factors. It is the

characteristics rather than the covariance structure of returns that appear to explain the

cross-sectional variations in stock returns. The study focus on the factor portfolios

suggested by Fama and French (1993) and draw the conclusion that factor loadings

measured with respect to the various macro factors used by Chan, et.al (1985), Chen, et.al

(1986), and Jagannathan and Wang (1996) also failed to explain the stock returns once

characteristics are taken into account. Thus, implying different forms of regression

models, portfolios analysis and analysis of factor loadings, Daniel and Titman (1997)

25

demonstrated two major things: First, there is no evidence of a separate distress factor.

Most of the co-movement of high book-to-market stocks is not due to distressed stocks

being exposed to a unique distress factor, but rather, because stocks with similar factor

sensitivities tend to become distressed at the same time. Second evidence suggests that it

is characteristics (size & book-to-market) rather than factor loadings that determine

expected returns. It shows that factor loadings do not explain the high returns associated

with small and high book-to-market stocks beyond the extent to which they act as proxies

for these characteristics.

Daniel, et.al (2001) offered a model in which asset prices reflect both covariance risk and

misperceptions of firms’ prospects, and in which arbitrageurs’ trade against mispricing.

The classical theory of security market equilibrium is based on the interaction of fully

rational optimizing investors. Several important studies have been explored alternatives to

the premise of full rationality in recent years. One approach model market misevaluation

as a consequence of noise or positive feedback trades. Another approach analyzes how

individuals form mistaken beliefs or optimize incorrectly, and derives the resulting trades

and misevaluation. The objective of the study is to offer a theory of asset pricing in which

the cross section of expected security returns is determined by risk and investor

misevaluation. In equilibrium, expected returns are linearly related to both risk and

mispricing measures e.g., fundamental/price ratios. With many securities, mispricing of

idiosyncratic value components diminishes but systematic mispricing does not. The

theory offer untested empirical implications about volume, volatility, fundamental/price

ratios and mean returns which is consistent with several empirical findings. Thus, the

study included that the ability of fundamental/price ratios and market value to forecast

stock returns, and the domination of beta by these variables.

A firm is said to be default when it fails to service its debt obligations. Therefore, default

risk induces lenders to require from borrowers a spread over the risk-free rate of interest.

This spread is an increasing function of the probability of default of the individual firm.

Vassalou and Xing (2004) estimated the default likelihood indicators (DLI) for individual

firms using equity beta. The main purpose of the study is to address the issue that

investors are still known very little about how default risk affects equity returns. The DLI

are nonlinear functions of the default probabilities of the individual firms and are

calculated using the contingent claims methodology of Black and Scholes (1973) and

Merton (1974). The study used the COMPUSTAT file of all firms for the analysis starting

from January 1971 to December 1999. The major findings of the study are: the measure

26

of default risk contains very different information from the commonly used aggregate

default spreads which is default risk, intimately related to the size and book-to-market

characteristics of a firm. It shows that both effects are intimately related to default risk.

Small firms earn higher returns than big firms, only if they also have higher default risk.

Similarly, value stocks earn higher returns than growth stocks, if their risk of default is

high. In addition, high-default-risk firms earn higher returns than low default risk firms,

only if they are small in size and/or high book-to-market equity. In all other cases, there is

no significant difference in the returns of high and low default risk stocks. With these

findings the study concluded that both the size and book-to-market effects can be views

as default effects which is in sum the case of size effect.

In a study of dissecting anomalies, Fama and French (2008) considered the patterns of

average stock returns which do not explained by CAPM. Two approaches were used to

identify anomalies: sorts of returns on anomaly variables, and regressions, in the spirit of

Fama and MacBeth (1973) to explain the cross-section of average returns. The data

collection started from at the end of each June 1963 to end with 2005. NYSE, Amex, and

NASDAQ stocks were allocated into three size groups - microcaps (tiny), small stocks,

and big stocks. The breakpoints are the 20th and 50th percentiles of end-of-June market

cap for NYSE stocks. The findings of the study includes: as the previous work found that

net stock issues, accruals, momentum, profitability, and asset growth are associated with

anomalous average returns. Smilarly, the study explored the pervasiveness of these return

anomalies via sorts and cross-section regressions estimated separately on microcaps,

small stocks, and big stocks. The book-to-market ratio, net stock issues, accruals, and

profitability all produce average regression slopes that are indistinguishable across size

groups. The measured net of the effects of size and B/M, the equal- and value-weight

abnormal hedge portfolio returns associated with momentum, net stock issues, and

accruals are strong for all size groups (and thus pervasive). There is a more serious stain

on the net stock issues anomaly. The regression results showed that, at least for 1963 to

2005, each of the anomaly variables seems to have unique information about future

returns. All the anomaly variables are at least rough proxies for expected cash flows.

Finally, the study commonly interprets the average returns associated with anomaly

variables as evidence of market inefficiency. In sum, the anomalous returns associated

with net stock issues, accruals, and momentum are pervasive; they show up in all size

groups (micro, small, and big) in cross-section regressions, and they are also strong in

sorts, at least in the extremes. The asset growth and profitability anomalies are less

27

robust. There is an asset growth anomaly in average returns on microcaps and small

stocks, but it is absent for big stocks. Among profitable firms, higher profitability tends to

be associated with abnormally high returns, but there is little evidence that unprofitable

firms have unusually low returns.

b) Review of major studies on stock returns analysis, return decomposition and methodology effects

The financial investment focus towards the returns in terms of shareholders wealth

maximization or simply, on financial returns. The returns on investment is not an isolated

terms, it is relative and interrelated with multiple factors including its own behavior. This

section includes the major studies on stock returns which help to analyze the stock returns

in depth. The stock return analysis along with its decomposition and the methodological

effects for the period 1972 to 2008 have been presented herein first and second sub-

section respectively.

i) Review of major studies on stock returns

The level of market efficiency is formed based on the speed of adjustment of new

information. Among the others, the market information is one that causes the stock

returns. The market would be consistent if there is strong form of efficiency but the strong

form of efficiency is imaginary so that the stock returns moves ups and downs as per the

information as well as on the basis of time being. Table 2.4 shows the major studies on

stock returns analysis.

Rendleman et.al (1982) aimed to reexamine the previous study (Reinganum's study)

which indicates that abnormal returns could not be earned unexpected quarterly earnings

information, and documented precisely the response of stock prices to earnings

announcements. The study used a very large sample of stocks and daily returns which

represents the most complete and detailed analysis of quarterly earnings. The major

findings of the study is contrary to those of the earlier study and showed that abnormal

returns could have been earned almost any time during the 1970's. The analysis also

indicated that risk adjustments matter little in this type of work. Finally, the study found

roughly 50 percent of the adjustment of stock returns to unexpected quarterly earnings

occurs over a 90-day period after the earnings are announced.

Table 2.4: Review of major studies on stock returnsStudy Major findingsRendleman, et.al. (1982)

Abnormal returns could have been earned almost any time. The analysis also indicated that risk adjustments matter little in this type of work.

28

Poterba and Summers (1988)

Positive autocorrelation in returns over short horizons and negative autocorrelation over longer horizons, although random-walk price behavior cannot be rejected at conventional statistical levels. With this, the conclusion is substantial movements in required returns are needed to account for the correlation patterns.

Kothari, et.al (1995)

The relationship between book-to-market equity and returns is weaker and less consistent.

Fama and French (1996b)

Except for the continuation of short-term returns, the anomalies largely disappear.

Fama and French (1996a)

Survivor bias does not explain the relation between book-to-market equity and average returns and, beta alone cannot explain average returns.

Fama and French (1997)

The costs of equity for industries are imprecise.

Devas, et.al (2000)

The value premium in average stock returns in US is robust.

Asness et.al (2000)

Within-industry momentum has predictive power for the firm’s stock return beyond that captured by across-industry momentum.

Fama and French (2002)

The average stock return on the last half-century is a lot higher than expected in US.

Malmendier and Tate (2008)

Overconfident CEOs over-estimate their ability to generate returns.

The transitory components in stock prices are investigated by Poterba and Summers

(1988). After showing that statistical tests have little power to detect persistent deviations

between market prices and fundamental values, the study considered whether prices are

mean-reverting. The study is based on the data from the United States and 17 other

countries. The point estimates of the empirical work explain the positive autocorrelation

in returns over short horizons and negative autocorrelation over longer horizons,

although random-walk price behavior cannot be rejected at conventional statistical levels.

The authorities indicated that substantial movements in required returns are needed to

account for the correlation patterns. The study also discussed with persistent, but

transitory, disparities between prices and fundamental values.

A study by Kothari, et.al (1995) examined whether beta explains cross-sectional variance

in average returns over the post-1940 periods as well as the longer post-1926 period, and

whether book-to-market equity captures cross-sectional variations in average returns over

a longer 1947 to 1987 period. The authors noted that the relationship between book-to-

market equity and returns is weaker and less consistent than that in Fama and French

(1992). They claimed that past book-to-market results using COMPUSTAT data are

affected by a selection bias and provide indirect evidence. Using an alternative data

sources from standard poor’s industry level from 1947 to 1987, the authors have noted

that book-to-market is at best weakly related to average stocks returns. The study

presented evidence that average returns do indeed reflects sustainable compensation for

beta risk, provided that betas are measured at the annual interval. Finally, the authors

claimed that the failure of a significant relation between book-to-market equity and

29

returns to emerge the standards poor’s industry portfolios poses a serious challenge to

book-to-market equity “empirical asset pricing model”.

The study is based on previous work that average returns on common stocks are related to

firm characteristics like size, earnings/price, cash flow/price, book-to-market equity, past

sales growth, long-term past returns, and short-term past returns, Fama and French

(1996b). Because these patterns on average returns apparently are not explained by the

CAPM, they are called anomalies. The three-factor time series regression models in

Fama and French (1993), the 25 Fama and French (1993) Size-BE/ME Portfolios of

value-weighted NYSE, AMEX and NASD stocks, excess return portfolios were formed

based on Lakonishok, et.al (LSV 1994) using COMPUSTAT accounting data, LSV

double-sort portfolios, portfolios formed on past returns, one-factor CAPM excess-return

regressions and alike rigorous models and procedures were used for the analysis for the

30 years of data covering 1964 to 1993. Fama and French (1993) found that the three-

factor risk-return relation is a good model for the returns on portfolios formed on size and

book-to-market equity. The study that also explained the strong patterns in returns

observed when portfolios are formed on earnings/price, cash flow/price, and sales growth,

variables recommended by Lakonishok, et.al (1994) and others. The three-factor risk-

return relation also captures the reversal of long-term returns documented by DeBondt

and Thaler (1985). Thus, portfolios formed on E/P, C/P, sales growth, and long-term past

returns do not uncover dimensions of risk and expected return beyond those required to

explain the returns on portfolios formed on size and BE/ME. The three-factor risk-return

relation is, however, just a model. It surely does not explain expected returns on all

securities and portfolios. The study found that cannot explain the continuation of short-

term returns documented by Jegadeesh and Titman (1993) and Asness (1994). Thus, the

study concluded, except for the continuation of short-term returns, the anomalies largely

disappear in a three-factor model. The results are consistent with rational ICAPM or APT

asset pricing, but also consider irrational pricing and data problems as possible

explanations.

Fama and French (1996a) revealed that survivor bias does not explain the relation

between book-to-market equity and average returns. The study used COMPUSTAT data

from the period 1928 to 1993. The portfolios in June of each year were formed using

betas on the NYSE value-weight market portfolio estimated with two to five years of past

monthly returns. The result showed that the average monthly and annual post formations

returns initially increased with post formation betas, but relation between average returns

30

and beta was rather flat from fourth to tenth beta deciles. However, the authors have also

explained that univariate beta regressions leave an unexplained size effect. In the

portfolios formed on size and beta, the average beta premiums form univariate

regressions of return on beta underestimated the positive relation between beta and

average returns produced by size sort and overestimated the relation between beta and

average returns produced by beta sort. Therefore, result suggested that beta alone cannot

explain average returns.

The study estimated that costs of equity for industries are imprecise, Fama and French

(1997). The standard errors of more than three percent per year are typical for both the

CAPM and the three-factor model of Fama and French (1993). The study found that

these large standard errors are the result of uncertainty about true factor risk premiums

and imprecise estimates of the loadings of industries on the risk factors. Thus, the

estimates of the cost of equity for firms and projects are surely even less precise.

The study documented that the value premium in U.S. stock returns is robust (Devas,

et.al, 2000). The positive relationship between average returns and book-to-market equity

and the three-factor risk model explains the value premium better than the hypothesis that

the book-to-market characteristic is compensated irrespective of risk loadings. The study

is based on data from 1929 to 1997, derived from Moody’s industrial manuals and

COMPUSTAT. Sample firms were selected from the NYSE, AMEX and NASDAQ

industrials and non-industrials. Fama and French (1993) three-factor asset pricing model

and characteristics model are employed. The findings showed that the value premium in

average stock returns is robust. The three-factor model explains the value premium better

than the characteristics model. Finally, when portfolios are formed from independent

sorts of stocks on size and BE/ME, the three-factor model is rejected. Based on these

results, the study concluded that the three-factor model is just a model and thus an

incomplete description of expected returns.

Within-industry momentum has predictive power for the firm’s stock returns beyond that

captured by across-industry momentum and a significant short-term (one month) industry

momentum effect which remains strongly significant when restrict the sample to only the

most liquid firms (Asness, et.al 2000). The study considered the sample of all firms listed

on the NYSE, AMEX, and NASDAQ stock exchanges from July 1963 through

December 1998 and the necessary financial data were retrieved from COMPUSTAT

database. Fama-MacBeth regression model and its modified models along with two-way

31

sorts of portfolios and descriptive statistics are employed for the analysis. Originally

established by Fama and French (1997), sample firms are categorized into 48 industries.

To explore the better proxies for the information about future returns contained in firm

characteristics such as size, book-to-market equity, cash flow-to-price, percent change in

employees, and various past returns measure were obtained by breaking these

explanatory variables into two industry-related components. The first component is the

difference between firms’ own characteristics and the average characteristics of their

industries i.e. within-industry variables and, the second is average characteristics of

firms’ industries i.e. across-industry variables. In conclusion, the study provided the

better way of sorting stocks and primarily, within-industry and across-industry variables

are better able to explain the cross-section of expected stock returns than risk proxies in

the more common market-wide form.

A study is designed to estimate the equity premium using dividend and earnings growth

rates to measure the expected rate of capital gain, Fama and French (2002). The equity

premium is the difference between the expected returns on the market portfolio of

common stocks and the risk-free interest rate. Dividends and earnings are used to estimate

the expected stock returns. The explanation of the model used is: the average stock return

is the average dividend yield plus the average rate of capital gain. The CRSP value-

weighted portfolio of NYSE, AMEX and NASDAQ stocks from 1951 to 2000 are used

for the analysis. The results estimates the dividend growth rates for 1951 to 2000, 2.55

percent and earnings growth rates 4.32 percent, are much lower than the equity premium

produced by the average stock return, 7.43 percent. The evidence suggests that the high

average return for 1951 to 2000 is due to a decline in discount rates that produces a large

unexpected capital gain. Thus, the main conclusion is that the average stock returns on the

last half-century is a lot higher than expected.

A study analyzes the top level overconfidence on acquisition and its impact on market or

the market reaction. Does CEO’s overconfidence help to explain merger decisions? is the

focus of Malmendier and Tate (2008). Generally, overconfident CEOs over-estimate

their ability to generate returns. As a result, they overpay for target companies and

undertake value-destroying mergers. The effects are strongest if they have access to

internal financing. The study tests these predictions using two proxies for

overconfidence: CEOs’ personal over-investment in their company and their press

portrayal. The result shows that the odds of making an acquisition are 65percent higher if

the CEO is classified as overconfident. The effect is largest if the merger is diversifying

32

and does not require external financing. The market reaction at merger announcement is

significantly more negative than for non-overconfident CEOs. The study considered

alternative interpretations including inside information, signaling, and risk tolerance

while analyzing the relationship.

ii) Review of major studies on stock return decomposition and methodology effects

In principle, decomposition is to make a complex problem into simple. It helps to get the

thinking straight into simpler way with a logical reasoning and come out with a potential

solution for the complex issue. The decomposition approach in other words, is an attempt

to obtain relatively simple interpretation for the complex issues. With the decomposition

principle, one can identify the factors affecting stock returns. Apart from identifying the

factors contributing for stock returns, the methodology used for the study is also a major

contributor for stock return anomalies. Table 2.5 shows the major studies in stock returns

decomposition and the methodology effects as follows:

Table 2.5: Review of major studies on stock returns decomposition & methodology effects

Study Major findingsFama (1972) Return on a portfolio can be subdivided into two parts: the return from security

selection (selectivity) and the return from bearing risk (risk).

Campbell (1991) Unexpected stock returns associated with changes in expected future dividends or expected future returns.

Fama (1998) Anomalies can be due to methodology, most long-term return anomalies tend to disappear with reasonable changes in technique used for the analysis and the anomaly is stronger for small stocks.

The evaluation of the investment performance is the crucial issue in investment

management. Number of studies has been conducted on the similar topics. Among others,

Fama (1972) suggested the methods for evaluating investment performance. The previous

works are concerned with measuring performance into two dimensions, return and risk.

The study suggested somewhat finer breakdowns of the investment performance. The

goal of the performance measure itself is just to test how good the portfolio manager is at

security analysis. That is, does the portfolio manager show any ability to uncover

information about individual securities that is not already implicit in their prices? The

basic notion underlying the methods of performance evaluation is presented, and the

returns on managed portfolios can be judged relative to those of "naively selected"

portfolios with similar levels of risk. Both the measure of risk and the definition of a

naively selected portfolio were obtained from modern capital market theory. The

conclusions of the study are: the stock returns on a portfolio can be subdivided into two

33

parts: the return from security selection (selectivity) and the return from bearing risk

(risk). The return from selectivity is defined as the difference between the return on the

managed portfolio and the return on a naively selected portfolio with the same level of

market risk.

What moves the stock returns? To get the ideas on this voluminous research question and

the heated debate issue, Campbell (1991) conducted a study on variance decomposition

for stock returns. The study present a simple way to break stock market movements into

two components; one which is associated with changes in rational expectations of future

returns is "news about future returns", and one which is not is called the "news about

future dividends". The approaches and tools used for the analysis are; arbitrary correlation

approach between the two components which is important in practice, regression analysis

to describe the evolution through time of the forecasting variables, vector autoregressive

(VAR) system used to calculate the impact that an innovation in the expected return will

have on the stock price, contemporaneous regression approach regresses stock returns on

contemporaneous innovations to variables which might plausibly affect the stock market,

univariate time-series approach studies the autocorrelation function of stock returns. The

study shows that unexpected stock returns must be associated with changes in expected

future dividends or expected future returns. A vector autoregressive method is used to

breakdown the unexpected stock returns into two components. In U.S. monthly data of

NYSE retrieved from CRSP from 1927 to 1988, one-third of the variance of unexpected

returns is attributed to the variance of changing expected dividends, one-third to the

variance of changing expected returns, and one-third to the covariance of the two

components. Changing expected returns have a large effect on stock prices because they

are persistent: a 1 percent innovation in the expected return is associated with a 4 or 5

percent capital loss. Changes in expected returns are negatively correlated with changes

in expected dividends, increasing the stock market reaction to dividend news. In the

period 1952-88, changing expected returns account for a larger fraction of stock return

variation than they do in the period 1927-51.

Consistent with the market efficiency hypothesis that the anomalies are chance results,

apparent overreaction to information is about as common as underreaction, and post-

event continuation of pre-event abnormal returns is about as frequent as post-event

reversal (Fama, 1998). Most important, consistent with the market efficiency prediction

that apparent anomalies can be due to methodology, most long-term return anomalies

tend to disappear with reasonable changes in technique used for the analysis. The three-

34

factor model of Fama and French (1993) is employed to estimate the portfolios abnormal

returns, it showed that the three-factor model is not a perfect story for average returns

and considered as the bad-model. The bad-model problem can produce spurious

anomalies in event studies. All methods for estimating abnormal returns are subject to

bad-model problems, and no method is likely to minimize bad-model problems for all

classes of events. The study provides the important general message from the initial

public offerings and seasoned equity offerings results is one caution: two approaches that

seem closely related i.e. both attempt to control for variation in average returns related to

size and BE/ME, can produce much different estimates of long-term abnormal returns.

The anomalies are largely limited to small stocks because small stocks always pose

problems in tests of asset pricing models, so that they are prime candidates for bad-model

problems in tests of market efficiency on long-term returns. Thus, the anomaly is

stronger for small stocks.

c) Review of major studies on investor behavior

The heated issue in financial literature is the behavioral effects on stock returns. The

financial literature explained that there are numerous qualitative factors that contribute for

stock market movements. The quantitative factors that can be measured but their

significance is questionable because of historic nature. The behavioral factors on the other

hand, significantly influence the stock movements. At the same time, it is very difficult to

articulate the level of its influences. The major studies on investor behavior have been

presented in this section. The study period range from 1994 to 2011.

i) Review of major studies on investor behavior before 2000

This sub-section focuses on the review of value strategies versus glamour strategies with

investor behavior, information processing, news and events responses, etc. Table 2.6

presents the review of major studies on investor behavior before 2000 as follows:

Lakonishok, et.al (1994) conducted a study on the most debatable, value strategies,

glamour strategies, investors’ extrapolation and risk which have attracted academic

attention as well. The value strategies call for buying stocks that have low prices relative

to earnings, dividends, book assets, or other measures of fundamental value. For many

years, scholars and investment professionals have argued that value strategies outperform

the market. While there are some agreements that value strategies produce higher returns,

but the interpretation of why they do so is more controversial. The objective of the study

is to shed further light on the two potential dimensions for why value strategies work.

35

Table 2.6: Review of major studies on investor behavior before 2000

Study Major findingsLakonishok, et.al (1994)

Value strategies yield higher returns than glamour strategies because these strategies exploit the suboptimal behavior of the typical investor and not because these strategies are fundamentally riskier.

Ikenberry et al. (1995)

The market responds mistakenly in initial phase of information and appeared to ignore much of the information conveyed through repurchase announcement.

Barberis, et.al (1998)

In a variety of markets, sophisticated investors can earn superior returns by taking advantage of under-reaction and overreaction without bearing extra risk.

Klibanoff, et.al (1998)

News events lead some investors to react more quickly.

Odean (1999)

The trading volume of a particular class of investors, those with discount brokerage accounts, is excessive. These investors trade excessively in the sense that their returns are, on average, reduced through trading.

First, the study examines more closely the predictions of the contrarian model. Second,

value strategies that bet against those investors who extrapolate past performance too far

into the future produce superior returns. Variables employed for the study are: past

performance is measured using information on past growth in sales, earnings, and cash

flow, and expected performance is measured by multiples of price to current earnings,

and cash flows. The sample period covered from the end of April 1963 to the end of April

1990. The sources of data are CRSP and COMPUSTAT, of NYSE and AMEX firms. The

results could potentially suffer from the Look-ahead or survivorship bias (Banz and

Breen, 1986) and Kothari, et.al, 1992) but methodology used is different from those in

other recent studies in ways that should mitigate this bias by First, do not use those

returns to evaluate strategies which appear such bias. Second, study only NYSE and

AMEX firms. Finally, report results for the largest 50 percent of firms on the NYSE and

AMEX. The selection bias is less serious among these larger firms (La Porta, 1993).

Couple of simple statistical tools; average, percentage, standard deviation along with

rigorous portfolio analysis and FM regression models is used for the analysis. The study

provides that value strategies (high B/M) yield higher returns because these strategies

exploit the suboptimal behavior of the typical investor and not because these strategies

are fundamentally riskier.

A total of 1239 open market share repurchases announced between January 1980 and

December 1990 by firms whose shares traded on the NYSE, ASE, or NASDAQ is

considered as the sample of the study, (Ikenberry et.al, 1995). For the performance

measurement, the study used the CAR approach and the buy-and-hold approach. The

long-run firm performance following open market share repurchase announcement

indicated that the average abnormal four year buy-and-hold return measured after the

36

initial announcement is 12.1 percent where as the average market response to the

announcement of an open market share repurchase is 3.5 percent. For value stocks,

companies more likely to be repurchasing shares because of undervaluation, the average

abnormal return is 45.3 percent. For repurchases announced by glamour stocks, where

undervaluation is less likely to be an important motive, no positive drift in abnormal

return is observed. Thus, at least with respect to value stocks, the market errs in its initial

response and appears to ignore much of the information conveyed through repurchase

announcement.

The motivation of the study is the recent empirical researches in Finance which have been

uncovered two families of pervasive regularities: underreaction of stock prices to news

such as earnings announcements, and overreaction of stock prices to a series of good or

bad news. For example, the underreaction evidence shows that over horizons of perhaps

one to twelve months security prices underreact to news. In an effort to fill this gap,

Barberis, et.al (1998) propose a model of investor sentiment. As a consequence, news is

incorporated only slowly into prices, which tend to exhibit positive autocorrelations over

these horizons. A related way to make this point is to say that current good news has

power in predicting positive returns in the future. The overreaction evidence shows that

over longer horizons of perhaps three to five years, security prices overreact to consistent

patterns of news pointing in the same direction. That is, securities that have had a long

record of good news tend to become overpriced and have low average returns afterwards.

Put differently, securities with strings of good performance, however measured, receive

extremely high valuations. This effort presents a parsimonious model of investor

sentiment, or of how investors form beliefs, which is consistent with the empirical

findings. The model is based on psychological evidence and produces both under-reaction

and overreaction for a wide range of parameter values. The existence of this model

challenge to the efficient markets theory because it suggests that in a variety of markets,

sophisticated investors can earn superior returns by taking advantage of under-reaction

and overreaction without bearing extra risk.

In an effort to investigate the investors’ reactions to salient news, Klibanoff, et.al (1998)

conducted a study on ‘investor reaction to salient news in closed-end country funds.’

Panel data on prices and net asset values are used to test whether dramatic country-

specific news affects the response of closed-end country fund prices to asset value.

Authors believe that anomalous empirical regularities in financial returns can be

explained by investor underreaction or overreaction to news. The study is focused on a

37

particular form of cognitive error as a source of investor over- and underreaction, and

examined the hypothesis that individual investor assign more importance to more

prominent news and assign less importance to less prominent news, even if the two pieces

of news have the same effect on fundamental value. The objective of the study is to use

financial market data to detect the salience effect on investor reaction in a non-laboratory

setting. Major news events on the front page of The New York Times (NYT) were

collected and correlate with the degree of reaction in financial asset prices. Sample of

country funds consists of the 39 single-country publicly traded funds from January 1986

through March 1994 and that have at least twelve months of price and net asset value

data, and weekly data on funds represent 25 countries. Ordinary least square regressions

were used for the analysis. The results showed that in a typical week, prices underreact to

changes in fundamentals; the (short-run) elasticity of price with respect to asset value is

significantly less than one. In weeks with news appearing on the front page of NYT,

prices react much more; the elasticity of price with respect to asset value is closer to one.

Thus, the findings of the study are consistent with the hypothesis that news events lead

some investors to react more quickly.

Based on the issue - trading volume on the financial market seems high, perhaps higher

than can be explained by models of rational markets. The study demonstrates that the

trading volume of a particular class of investors, those with discount brokerage accounts,

is excessive, Odean (1999). These investors trade excessively in the sense that their

returns are, on average, reduced through trading. Thus, the study tests the hypothesis that

investors trade excessively because they are overconfident. Overconfident investors may

trade even when their expected gains through trading are not enough to offset trading

costs. In fact, even when trading costs are ignored, these investors actually lower their

returns through trading. The study examines return patterns before and after the purchases

and sales made by these investors. The investors tend to buy securities that have risen or

fallen more over the previous six months than the securities they sell. They sell securities

that have, on average, risen rapidly in recent weeks. The study suggest that these patterns

can be explained by the difficulty of evaluating the large number of securities available

for investors to buy, by investors’ tendency to let their attention be directed by outside

sources such as the financial media, by the disposition effect, and by investors' reluctance

to sell short.

ii) Review of major studies on investor behavior during 2000s

38

After 2000, the major behavioral studies on investor behavior have been presented in

Table 2.7 in this sub-section. The studies includes investors response to public and private

signals, buying and selling behavior, behavior of men and women investors, etc as below:

There is a large amount of evidences that stock prices are predictable and that stock

returns exhibit reversal at weekly and three-to-five year intervals and drift over 12 month

periods. Chan (2003) aimed to deepen the understanding of how information flows drive

anomalies. Using a comprehensive database of headlines about individual companies, the

study examines monthly returns after two sources of stimuli. The first is public news,

which is identifiable from headlines and extreme concurrent monthly returns. The second

is large price movements unaccompanied by any identifiable news. The study, then,

examines monthly returns following public news and compares them to stocks with

similar returns but no identifiable public news. The study presents that there is a

difference between the two sets. Thus, the study concluded that there is strong drift after

bad news and investors seem to react slowly to bad news. The results is also depicted that

stock returns reversal after extreme price movements unaccompanied by public news.

Table 2.7: Review of major studies on investor behavior during 2000sStudy Major findingsChan (2003) Investors are appeared to underreact to public signals and overreact to perceived

private signals. For instance, Investors tend to react slowly to the bad news information.

Biais et.al. (2005)

Miscalibration reduces and self-monitoring enhances trading performance. The effect of the psychological variables is strong for men but non-existent for women.

Barber and Odean (2008)

Individual investors display attention-driven buying behavior, they are net buyers on high-volume days, following both extremely negative and extremely positive one-day returns, and when stocks are in the news. On the other hand, the institutional investors - especially the value-strategy investors - do not display attention-driven buying.

Kaniel, et.al (2008)

The trading of individual investors provides two important results. First, net individual trading is positively related to future short-horizon returns: Prices go up in the month after intense buying by individuals and go down after intense selling by individuals. Second, the predictive ability of net individual trading with respect to returns is not subsumed by volume or the return reversal phenomenon.

Foucault, et.al (2011)

The effect of retail trading on volatility is positive, the positive effect is consistent with the view that some retail investors behave as noise traders.

Sun and Wei (2011)

Analysts make more judgment-intensive decisions, such as issuing stock recommendations; they overweight intangible information, leading to overreaction to intangible information. On the contrary, when analysts make less judgment-intensive decisions, such as earnings per share (EPS) forecasts, there is no such evidence of overreaction. The study supports the hypothesis that investors are overly sensitive to intangible information when they need to make more subjective judgments.

Doskeland and Hvide (2011)

Individuals with a comparative advantage in collecting information can obtain asymmetric information and earn abnormal returns. On the other hands, investors have a preference for professionally close stocks even if such holdings generate negative abnormal returns.

39

The degree of overconfidence in judgment in the form of miscalibration is measured by

Biais, et.al. (2005). The tendency to overestimate the precision of one's information and

self-monitoring, a form of attentiveness to social cues of 245 participants and also

observe their behavior in an experimental financial market under asymmetric information.

Miscalibrated traders, underestimating the conditional uncertainty about the asset value,

are expected to be especially vulnerable to the winner's curse. High self-monitors are

expected to behave strategically and achieve superior results. Thus, the study concluded

that miscalibration reduces and self-monitoring enhances the trading performance. The

effect of the psychological variables is strong for men but non-existent for women.

The hypothesis that individual investors are net buyers of attention grabbing stocks, e.g.,

stocks in the news, stocks experiencing high abnormal trading volume, and stocks with

extreme one-day returns is evaluated and confirmed by Barber and Odean (2008).

Attention-driven buying results from the difficulty that investors have searching the

thousands of stocks they can potentially buy. Individual investors do not face the same

search problem when selling because they tend to sell only stocks they already own. The

study hypothesize that many investors consider purchasing only stocks that have first

caught their attention. Thus, preferences determine choices after attention has determined

the choice set. The study collected the required the data from four sources: a large

discount brokerage - records for the investments of 78,000 households from January 1991

through December 1996, a small discount brokerage - daily trading records from January

1996 through 15 June 1999 and 14,667 accounts for individual investors, a large full-

service brokerage - investments of households for the 30 months ending in June 1999,

and the Plexus Group—a consulting firm that tracks the trading of professional money

managers for institutional clients, provide the daily trading records for 43 institutional

money managers and span the period January 1993 through March 1996. During the

sample period, only 7194297 common stock trades are included for the analysis out of 10

million total trades: 3,974,998 purchases with a mean value of $15,209 and 3,219,299

sales with a mean value of $21,169. In sum, consistent with the predictions, the study

concluded that individual investors display attention-driven buying behavior, they are net

buyers on high-volume days, following both extremely negative and extremely positive

one-day returns, and when stocks are in the news. Attention-driven buying is similar for

large capitalization stocks and for small stocks. On the other hand, the institutional

investors - especially the value-strategy investors - do not display attention-driven buying.

40

For a variety of reasons, financial economists tend to view individuals and institutions

differently. In particular, while institutions are viewed as informed investors, individuals

are believed to have psychological biases and are often thought of as the proverbial noise

traders. Kaniel, et.al (2008) investigates the dynamic relation between net individual

investor trading and short-horizon returns for a large cross-section of NYSE stocks. The

sample contained all common domestic stocks that were traded on the NYSE any time

between January 1, 2000, and December 31, 2003. The analysis of the trading of

individual investors on the NYSE provides two important results. First, net individual

trading is positively related to future short-horizon returns: Prices go up in the month after

intense buying by individuals and go down after intense selling by individuals. Second,

the predictive ability of net individual trading with respect to returns is not subsumed by

volume or the return reversal phenomenon.

Foucault, et.al (2011) studied on retail trading activities that have a positive effect on the

volatility of stock returns, which suggests that retail investors behave as noise traders.

Anything that changes the amount or character of noise trading will change the volatility

of price (Black, 1986). The study focuses on the issue: whether retail trading has a

positive effect on volatility which is yet to be answered. The study used the database

which provides the daily returns and daily trading volumes for each stock listed on the

French stock market from September 1998 to September 2002. The sample for the study

is 678 stocks in the control group with standard deviation 55 and 155 stocks in the treated

group with standard deviation 5 in each month. The study analyzed the reform of the

French stock market to assess the effect of retail investors on volatility. The reform makes

trading relatively more costly for retail investors in a subset of listed stocks and triggers a

drop in retail trading for these stocks relative to stocks unaffected by the reform. The

study found that the volatility of the stocks affected by the reform declines after the

implementation of the reform, relative to other stocks, which means that the effect of

retail trading on volatility is positive. The argument is that the positive effect is consistent

with the view that some retail investors behave as noise traders. In support of this claim,

the evidence showed that the reform also triggers a drop in the size of price reversals and

the price impact of trades for the stocks affected by the reform.

In order to relate the intangible information and analyst behavior, Sun and Wei (2011)

documented the direct evidence that when analysts make more judgment-intensive

decisions, such as issuing stock recommendations, they overweight intangible

information, leading to overreaction to intangible information. On the contrary, when

41

analysts make less judgment-intensive decisions, such as earnings per share (EPS)

forecasts, there is no such evidence of overreaction. More specifically, analyst

recommendations are much more sensitive to intangible information, while EPS forecasts

are more sensitive to tangible information. The sensitivity of long-term growth forecasts

to intangible and tangible information fall in between. The study also test and found that

the overconfidence bias in analyst recommendations contributes to the market

overreaction to intangible information. The results are consistent with the overconfidence

hypothesis which suggests that investors should be overly sensitive to intangible

information when they need to make more subjective judgments.

As time goes on, there are more and more convincing evidence that the right method in

investments is to put fairly large sums into enterprises which one thinks one knows

something about and in management of which one thoroughly believes (J.M. Keynes).

Based on this statement, Doskeland and Hvide (2011) hypothesize that professional

proximity is a route through which individuals can have a comparative advantage in

collecting information. The proximity can give a false feeling of competence on the one

hand or the access to value-relevant information that can lead to abnormally high returns

on the other. Popular belief suggests that some individuals have asymmetric information

and can gain from being undiversified (Merton (1987)). For instance, Warren Buffet who

has been generating strong investment performance using this approach over a 30-year

period (e.g., Martin and Puthenpurackal, 2008) advises “Invest within your circle of

competence. It’s not how big the circle is that counts, it’s how well you define the

parameters” (Fortune, November 11, 1993). The study employed the common stock

transactions of all Norwegian individual investors at the Oslo Stock Exchange (OSE) over

a 10-year period. The data set combined the full trade records of each individual with

exceptionally detailed socio-demographic information at a yearly level over a 20-year

period i.e. yearly panel of work history for each individual, including the industry and

ticker code of their employer, where investors live, geographically proximate, etc.

Further, the study defined the “expertise” stocks which SIC code matches the two-digit

SIC code of the individual’s employer. The findings of the study suggest that individuals

overweigh their holdings in expertise stocks. Individuals spend much of their time

building and maintaining their professional career, and thus they gain a considerable

amount of industry-specific experience. Accordingly, the study conjectured that

professional proximity is a route through which individuals can obtain a comparative

advantage in acquiring value-relevant information and hence realize abnormal stock

42

market returns. Professionally close investments is a particularly fitting environment to

detect abnormal returns following conventional portfolio theory, since investors should

invest in professionally close investments only if they are informed. These findings

provide clear evidence of a behavioral bias in individuals’ investment choices.

Overconfidence seems to be the most likely explanation for why individuals trade in

professionally close stocks. The lack of any evidence of abnormal returns for a very

plausible candidate suggests that individual investors are not able to profit from

asymmetric information. Another take-away of the results is to provide guidance to

individual investors themselves. Conventional portfolio theory recommends that investors

shy away from professionally close stocks unless they have superior information, since

such stocks carry extra risk. The study also found that investors have a preference for

professionally close stocks even if such holdings generate negative abnormal returns. It

thus seems that individual investors themselves are not aware of their poor investment

choices.

d) Review of major studies on initial public offerings

The study on initial public offerings shows the different reasons that the firm do not want

to issue IPOs when it is viewed from firms’ point of view. Whereas, taking the individual

perspectives, some studies focuses that investing in IPOs is riskier than investing in

secondary market. Further some studies position that the maintenance of target debt-

equity ratio while issuing IPOs as well as while making the repurchase decisions. Table

2.8 shows the major findings of some prominent studies on IPO issues. The study period

covers 1995 to 2006 as follows:

Table 2.8: Review of major studies on IPOs Study Major findingsLoughran and Ritter (1995)

Investing in firms issuing stock is hazardous to wealth.

Armen et.al. (2001)

The deviation between the actual and the target debt ratios plays a more important role in the repurchase decision than in the issuance decision and, when firms adjust their capital structure; they tend to move toward a target debt ratio.

Brau and Fawcett (2006)

The main reason for remaining private or do not issuing IPO is to preserve decision-making control and ownership.

The study on the new issues puzzle, Loughran and Ritter (1995) covered the sample

period from 1970 to 1990. The sample of 4753 companies going public in US stock

exchanges is analyzed. The required data were collected from CRSP and listed in Nasdaq

or Amex and NYSE daily tapes. It is shown that companies issuing stocks whether an

43

initial public offering or a seasoned equity offering, significantly underperform relative to

non-issuing firms for five years after the offering data. During the five years after the

issue, investors have received average returns of only 5 percent per year for companies

going public and only 7 percent per year for companies conducting a seasoned equity

offer. The study followed the same pattern as previous studies which evidence that firms

going public subsequently underperform and the same pattern holds for firms conducting

SEOs is new. The magnitude of the underperformance is economically important: based

upon the realized returns, an investor would have had to invest 44 percent more money in

the issuers than in non-issuers of the same size to have the same wealth five years after

the offering date. Surprisingly, this number is the same for both IPOs and SEOs. The

study also found only a modest portion of the underperformance of issuing firms can be

explained as a manifestation of book-to-market effects. Another finding is that extreme

winners that do not issue equity dramatically outperform extreme winners that do issue.

Also documented that the degree to which issuing firms underperform varies over time:

firms issuing during years when there is little issuing activity do not underperform much

at all, whereas firms selling stock during high-volume periods severely underperform. For

the analysis of data, three different procedures were used. First, calculates t-statistics

using annual holding-period returns on issuing firms relative to non-issuing firms.

Second, calculates t-statistics using a time series of cross-sectional regressions on

monthly individual firm returns and the third procedure was to calculate t-statistics using

3-factor time-series regressions of monthly returns for portfolios of issuing and non-

issuing firms. All three procedures result in rejection of the null hypothesis of no

underperformance at high degrees of statistical significance. Thus, the study concluded

that investing in firms issuing stock is hazardous to wealth.

Armen, et.al. (2001) analyzes the debt-equity choice and documented that when firms

adjust their capital structures, they tend to move toward a target debt ratio that is

consistent with theories based on tradeoffs between the costs and benefits of debt. In

contrast to previous empirical works, the study explicitly account for the fact that firms

may face impediments to movements toward their target ratio, and that the target ratio

may change over time as the firm's profitability and stock price changes. The study

conducted a separate analysis of the size of the issue and repurchase transactions suggests

that the deviation between the actual and the target ratio plays a more important role in

the repurchase decision than in the issuance decision.

44

A survey on Initial Public Offerings, Brau and Fawcett (2006) analyze the responses of

336 chief financial officers (CFOs) out of 1266 valid contact information of the selected

1500 nonfinancial private firms based on 2002 revenues, to compare practice to theory in

the areas of IPO motivation, timing, underwriter selection, under-pricing, signaling, and

the decision to remain private. The study found that the primary motivation for going

public is to facilitate acquisitions. Further, CFOs base IPO timing on overall market

conditions are well informed regarding expected under-pricing, and feel under-pricing

compensates investors for taking risk. The sample was selected based on information

from January to December 2002 and the mailed survey was conducted on May 5, 2003,

June 11, 2003 and September 12, 2003. The overall response rate for the study is 18.8

percent. The most important positive signal is past historical earnings, followed by

underwriter certification. CFOs have divergent opinions about the IPO process depending

on firm-specific characteristics. Finally, the study documented that the main reason for

remaining private is to preserve decision-making control and ownership.

e) Review of major studies on market behavior

The review of literature which is concern towards the market behavior comprises: market

predictability, investing in a certain day in a week or the weekly cycle, the seasonal

patterns, market reversal, etc have been organized in this section. The review of major

studies starts from 1965 and end with 2002. These previous studies are broadly classified

into two sub-sections based on prior 1990 and 1990 onwards.

i) Review of major studies on market behavior prior 1990

Table 2.9 presents some major contribution on market behavior prior 1990. Many people

in the investment community still believe on the market cycle, some others empirical

evidences indicates that the existence of seasonal patterns as well as weekly and daily

which are considered an essential prerequisite for investment performance. The major

findings of the studies on market behavior prior 1990 have been presented as follows.

Table 2.9: Review of major studies on market behavior prior 1990

Study Major findings

Fama (1965) The behavior of stock prices is not predictable as it follow the random walk. The chart reading through perhaps an interesting pastime but there is no real value to stock market investors.

French (1980) The study proposed the negative investing strategy - buying Monday and selling them in Friday which generates the profit even in negative daily returns of Monday, most importantly inconsistent with the general perception of positive daily returns for Monday the study documented that Monday stock returns is negative due to the weekend effect.

45

Brown and Warner (1985)

Using the simulation procedures with actual daily data, the major conclusion drawn from the study is: tests which assume non-zero cross-sectional dependence are only about half as powerful and usually no better specified than those employed assuming independence.

Ritter (1988) The ratio of stock purchases to sales by individual investors displays a seasonal pattern, with individuals having a below-normal buy/sell ratio in late December and an above-normal ratio in early January.

Schwert (1989) The average level of volatility much higher during recession, weak evidence that macroeconomic volatility helps to predict stock and bond return volatility, somewhat stronger evidence that financial asset volatility helps to predict future macroeconomic volatility, financial leverage affects stock volatility, but the effect is small, and the positive relation between trading activity and stock volatility.

For many years, the following question has been a source of continuing controversy in

both academic and business circle: to what extent can the past history of a common

stock's price be used to make meaningful predictions concerning the future price of the

stock? Fama (1965) analyzed whether the history repeats itself in that patterns of past

price behavior will tend to recur in the future or follow the random walk – the future path

of the price level of a security is no more predictable than the path of a series of

cumulated random numbers. The purpose of the study is to discuss first in more detail the

theory underlying the random-walk model and then to test the model's empirical validity.

The data consist of daily prices for each of the thirty stocks of the Dow-Jones Industrial

Average (DJIA). The time periods vary from stock to stock but usually run from about the

end of 1957 to September 26, 1962. The final date is the same for all stocks, but the initial

date varies from January, 1956 to April, 1958 so that there are thirty samples with about

1,200-1,700 observations per sample. Using frequency distribution, normal curve graphs,

portfolio formation and variance comparison, the study concluded that the behavior of

stock prices is not predictable as it follow the random walk. The chart reading through

perhaps an interesting pastime but there is no real value to stock market investors.

The process generating stock returns has been one of the most popular topics of research

in finance since Bachelier’s pioneering article, published in 1900. French (1980)

examines the process of generating stock returns by comparing the returns of different

days of the week. Ignoring holidays, the returns reported for Monday represent a three-

calendar-day investment, from the close of trading Friday to the close of trading Monday,

while the returns for other days reflect a one-day investment Therefore, if the expected

return is a linear function of the period of investment, measured in calendar time, the

mean return for Monday will be three times the mean for the other days of the week. The

findings of tests using the daily returns to the Standard and Poor’s (S&P) composite

portfolio, consisting 500 the largest firms and the total observations 6024 on NYSE from

46

1953 to 1977, are surprising. Inconsistent with the calendar and trading time models, the

mean return for Monday is significantly negative in each of five five-year sub-periods, as

well as over the full period. Thus, the study proposed the negative investing strategy -

buying Monday and selling them in Friday which generates the profit even in negative

daily returns of Monday, most importantly inconsistent with the general perception of

positive daily returns for Monday, the study documented that Monday’s stock returns is

negative due to the weekend effects.

The properties of daily stock returns and how the particular characteristics of these data

affect event study methodologies for assessing the share price impact on firm-specific

events is examined by Brown and Warner (1985). The study characterized number of

potentially important problems while using daily data like: non-normality, non-

synchronous trading and market model parameter estimation, variance estimation issues,

etc. Two hundred and fifty samples of 50 securities are selected for the analysis, the

securities were selected at random and with replacement, and the daily return data were

abstracted from CRSP files. The sample period covered on each trading days from July 2,

1962 through December 31, 1979. Using the simulation procedures with actual daily data,

the major conclusion drawn from the study is: tests which assume non-zero cross-

sectional dependence are only about half as powerful and usually no better specified than

those employed assuming independence.

In recent years, numbers of anomalies have been discovered in stock returns, among them

the turn-of-the-year effect have been generating the greatest interest. The average returns

on low-capitalization stocks are unusually high relative to those on large-capitalization

stocks in early January, a phenomenon is known as the turn-of-the-year effect. Ritter

(1988) found that the ratio of stock purchases to sales by individual investors displays a

seasonal pattern, with individuals having a below-normal buy/sell ratio in late December

and an above-normal ratio in early January. Year-to-year variation in the early January

buy/sell ratio explains forty-six percent of the year-to-year variation in the turn-of-the-

year effect during 1971-1985. The types of data employed for the study are: daily buy/sell

ratios of the cash account customers of the Merrill Lynch, Pierce, Fenner and Smith and

use the ratio of purchases and sales as a measure of the net buying activity of individual

investors. The ratios, five-scale descriptive statistics, mean difference in daily returns,

portfolio, histogram and the Ordinary Least Square (OLS) regression models are used for

the analysis. The study concluded that December’s net selling abruptly switches to net

buying at the turn of the year as well as explained why small stocks do well at the turn of

47

the year which validate the earlier finding that small stock outperform the large stock at

the end of the year.

The study analyzed the relation of stock volatility with real and nominal macroeconomic

volatility, economic activities, financial leverage, and stock trading activity, Schwert

(1989). The study employed the monthly data from 1857 to 1987. The estimates of the

standard deviation of monthly stock returns vary from two to twenty percent per month,

test for whether differences this large could be attributable to estimation error strongly

reject the hypothesis of constant variance. Regression analysis with autoregressive

moving average (ARMA), autoregressive conditional heteroscedasticity (ARCH),

distributed lag model and vector autoregressive model (VAR) are employed for the

analysis. The major conclusions of the study are: many economic series were more

volatile during depression (1929-39) particularly financial asset and industrial production;

the average level of volatility is much higher during recession; there is weak evidence that

macroeconomic volatility helps to predict stock and bond return volatility; there is

somewhat stronger evidence that financial asset volatility helps to predict future

macroeconomic volatility; financial leverage affects stock volatility but the effect is

small; and finally, the positive relationship between trading activity (both trading days

and volume) and stock volatility.

ii) Review of major studies on market behavior 1990 onwards

The relationship of market returns with factors like news, price bubbles and expectations,

the impact of research and development expenditure, market timing, etc are covered in

Table 2.10 as follows:

The rational speculation, usually presume that it dampens fluctuations caused by noise

traders. De Long, et al (1990) tried to explore on what effect do rational speculators have

on assets price? Speculators who destabilize asset prices do so by, on average, buying

when prices are high and selling when prices are low; such destabilizing speculators are

quickly eliminated from the market. By contrast, speculators who earn positive profits do

so by trading against the less rational investors who move prices away from

fundamentals. Such speculators rationally counter the deviations of prices from

fundamentals and so stabilize them. This is not necessarily the case if noise traders follow

positive feedback strategies - buy when prices rise and sell when prices fall, such

behavior common in financial markets. Theoretical arguments for the efficiency of

financial markets rely crucially on the stabilizing powers of rational speculation. Rational

48

speculators "buck the trend" and by doing so bring prices closer to fundamental values.

But, less rational investors, in another way, may pay to jump on the bandwagon and

purchase ahead of noise demand. Although, the key point to understand is that part of the

price rise is rational, part of it results from rational speculators' anticipatory trades and

from positive feedback traders' reaction to such trades. If rational speculators' early

buying triggers positive-feedback trading, then an increase in the number of forward

looking speculators can increase volatility about fundamentals. The findings of the study

generate a positive correlation of stock returns at short horizons, as positive feedback

traders respond to past price increases by flowing into the market, and negative

correlations of stock returns at long horizons, as prices eventually return to fundamentals.

Also, the study predicts that the stock market overreacts to news because such news

triggers positive feedback trading. In sum, the conclusion of the study is consistent with a

number of empirical observations about the correlation of asset returns, the overreaction

of prices to news, price bubbles, and expectations.

Table 2.10: Review of major studies on market behavior 1990 onwards

Study Major findings

De Long, et al (1990)

There is correlation of asset returns with the overreaction of prices to news, price bubbles, and expectations.

Hasbrouck (1991)

A trade's full price impact arrives only with a protracted lag; the impact is a positive and concave function of the trade size; large trades cause the spread to widen; trades occurring in the face of wide spreads have larger price impacts; and, information asymmetries are more significant for smaller firms.

Jegadeesh and Titman (1993)

The strategies which buy stocks that have performed well in the past and sell stocks that have performed poorly in the past generate significant positive returns over 3 to 12 month holding periods.

Chan, et.al (2001)

The stock price incorporates investors’ unbiased beliefs about the value of R&D expenditure.

Hirshleifer (2001)

The purely rational approach is being subsumed by a broader approach based upon the psychology of investors. In this approach, security expected returns are determined by both risk and misevaluation.

Baker and Wurgler (2002)

Market timing is an important aspect of real financing decisions.

Central to the analysis of market microstructure is the notion that in a market with

asymmetrically informed agents, trades convey information and therefore cause a

persistent impact on the security price. The magnitude of the price effect for a given trade

size is generally held to be a positive function of the proportion of potentially informed

traders in the population. Hasbrouck (1991) suggests that the interactions of security

trades and quote revisions be modeled as a vector autoregressive system. Within this

framework, a trade's information effect may be meaningfully measured as the ultimate

49

price impact of the trade innovation. The sample contained 20 firms, in each quartile

which had at least 500 transactions and the data abstracted from Institute for the Study of

Security Markets (ISSM) over the 62 trading days in the first quarter of 1989. Estimates

for a sample of NYSE issues suggest: a trade's full price impact arrives only with a

protracted lag; the impact is a positive and concave function of the trade size; large trades

cause the spread to widen; trades occurring in the face of wide spreads have larger price

impacts; and, information asymmetries are more significant for smaller firms.

A Popular view held by many journalists, psychologists, and economists is that

individuals tend to overreact to information. Jegadeesh and Titman (1993) documented

that strategies which buy stocks which performed well in the past and sell stocks which

performed poorly in the past generate significant positive returns over 3 to 12 month

holding periods. It is also found that the probability of these strategies is not due to their

systematic risk or to delayed stock price reactions to common factors. However, part of

the abnormal returns generated in the first year after portfolio formation dissipates in the

following two years. A similar pattern of returns around the earnings announcements of

past winners and losers is also documented. The study analyzed the NYSE and AMEX

stocks from 1965 to 1989 abstracted from the CRSP daily returns database. Relative

strength portfolio, one-factor model and the multi-factor models, covariance analysis,

lead-lag effects, events analysis as well as the regression models were used for the

analysis.

Whether stock prices fully value firms’ intangible assets, specially research and

development (R&D) expenditures is examined by Chan, et.al (2001). The market value of

a firm’s shares ultimately reflects the value of all its new assets. When, most of the assets

are physical, the link between asset values and stock prices is relatively apparent. But, in

modern economics, a large part of a firm’s value may reflect its intangible assets and

these are not reported in firms’ financial statements and treated as a current expenditure.

When a firm has large amounts of such intangibles, the lack of accounting information

generally complicates the task of equity valuation is the issue of the study. In particular,

R&D activity, one type of intangible asset has been the subject of much attention, is the

focus of the study. The evidence of the study does not support a direct link between R&D

spending and future stock returns. Thus, it does not appear that the average historical

stock returns of firms doing R&D outperform the returns of firms without R&D which is

consistent with the hypothesis that the stock price incorporates investors’ unbiased beliefs

about the value of R&D. However, the market is apparently too pessimistic about beaten-

50

down R&D intensive technology stocks’ prospects. The study found that companies with

high R&D to equity market value, which tend to have poor past returns, earn large excess

returns. It is also found that a similar relation exists between advertising and stock returns

and, R&D intensity is positively associated with returns volatility.

The study on investor psychology and asset pricing, Hirshleifer (2001) followed the

familiar quotations “the best plan is …to profit by the folly of others.” Further, the study

is influenced by Bill Blunte’s Deranged Anticipation and Perception Model (DAPM) in

which proxies for market misevaluation are used to predict security returns and concluded

that mispricing is only corrected slowly. The study is based on the survey and assesses the

theory and evidence regarding investor psychology as a determinant of asset pricing. In

the field of asset pricing, Campbell (2000) and Cochrane (2000) emphasize in external

sources of risk. The former argued that asset pricing is concerned with the sources of risk

and the economic forces that determine the rewards for bearing risk. And the later, stated

that the central task of financial economics is to figure out what are the real risks that

drive asset prices and expected returns. In contrast, the study argued that the central task

of asset pricing is to examine how expected returns are related to risk and to investor

misevaluation. The study concluded that the purely rational approach is being subsumed

by a broader approach based upon the psychology of investors. In this approach, security

expected returns are determined by both risk and misevaluation.

An analysis on market timing and capital structure is made by Baker and Wurgler (2002).

The findings of the study supported the generally accepted view that market timing is an

important aspect of real financing decisions. The study traced the implications of equity

market timing through to capital structure. Market-to-book ratio is used to measure the

market timing opportunities perceived by managers. The analysis found that low-leverage

firms tend to be those that raised funds when their valuations is high, and conversely

high-leverage firms tend to be those that raised funds when their valuations is low. The

study summarized that fluctuation in market valuations have large effects on capital

structure that persist for at least a decade. As a consequence, current capital structure is

strongly related to historical market values. It is well known that firms are more likely to

issue equity when their market values are high, relative to book value and past market

values, and to repurchase equity when their market values are low. In sum, the results

suggested the theory that capital structure is the cumulative outcome of past attempts to

time the equity market.

51

f) Review of major studies on market reactions to tangible and intangible

information

The information is broadly classified into the tangible and intangible components. Former

can be calculated from the firm’s financial statements and the later cannot be quantified in

numerical form. The accounting variables like the financial ratios, cash flows, sales, etc

are the examples of tangibles whereas the investor behavior, market behavior, news and

media impact, overconfidence, over and under-reactions, etc are the examples of

intangibles. The reviews of major studies in this section have been organized into five

sub-sections for tangible and intangible information.

i) Review of major studies on intangible information till 2000

The intangible issues like problems on risk measurements, financial ratings, trading halts,

the information diffusion process, etc and its effect on stock returns have been

incorporated into part. The study period covers 1989 to 2000 and the major findings of

the studies are presented in table 2.11 as follows:

Table 2.11: Review of major studies on market reactions to intangible information till 2000

Study Major findingsBernard and Thomas (1989)

Much of the evidences cannot plausibly be reconciled with arguments built on risk mismeasurement but is consistent with a delayed price response.

Goh and Ederington (1993)

Downgrades associated with deteriorating financial prospects convey new negative information to the capital market, but that downgrades due to changes in firms' leverage do not.

Lee, et.al (1994) Trading halts increase, rather than reduce, both volume and volatility. Brennan and Subrahmanyam (1995)

Other things equal, an increase in the number of investment analysts tends to be associated with reduction in the adverse selection cost of transacting.

Hong, et.al (2000) The study support Hong-Stein hypothesis which describes momentum reflects the gradual diffusion of firm-specific information. Thus, it is concluded that especially the negative information, diffuses only gradually across the investing public.

Bernard and Thomas (1989) attempted to discriminate between competing explanations

of post-earnings-announcement drift: a failure to adjust abnormal returns fully for risk

and a delay in the response to earnings reports. The empirical evidences showed that even

after earnings are announced, estimated cumulative abnormal returns continue to drift up

for good news firms and down for bad news firms. The study included the sample of

84,792 firm-quarters of data from NYSE and AMEX firms for the periods 1974-86 and

also conducted some supplementary tests based on 15,457 firm-quarters of data for over

the counter (OTC) stocks on the NASDAQ system from 1974 to 1985. Data were derived

52

from CRSP and COMPUSTAT database. Cumulative abnormal return (CAR) analysis as

a major statistical tool is employed in the study and concluded that much of the evidences

cannot plausibly be reconciled with arguments built on risk mismeasurement but is

consistent with a delayed price response.

In an effort to examine the stock returns and the bond ratings, Goh and Ederington (1993)

analyzed the reaction of common stock returns to bond rating changes. While the

financial literature exhibit a significant negative stock response to downgrades, the study

analyzes whether all downgrades are bad news for equity holders and whether all

downgrades are a surprise. When the rating agencies announce the rating changes, they

also have given the reasons. Based on these announced reasons, the study separate the

rating changes into groups based on whether they have positive or negative implications

for equity holders and whether or not they seem to be in response to recently released

public information. The study hypothesized that the negative reaction should not be

expected for all downgrades because: some rating changes are anticipated by market

participants; and the downgrades because of an anticipated move to transfer wealth from

bondholders to stockholders should be good news for stockholders. The study worked

with a set of 1078 rating changes announced by Moody's during the period 1984 through

1986, and excluded 468 because of insufficient data in CRSP daily returns database. The

study also searched the Wall Street Journal (WSJ) Index for other firm-specific

information releases in the three days surrounding the announcement date of the rating

change. If another announcement occurred during the three-day period, the rating change

announcement is eliminated yielding an uncontaminated sample of 428 ratings changes

(243 downgrades and 185 upgrades). Using Cumulative abnormal returns (CARs), the

study concluded that downgrades associated with deteriorating financial prospects convey

new negative information to the capital market, but that downgrades due to changes in

firms' leverage do not.

In the wake of the 1987 market break, a number of commentators on market mechanisms

recommended the establishment of "circuit breaker" mechanisms. The primary argument

supporting circuit breakers (both price limits and trading halts) is that non-trading periods

provide an opportunity for normal information transmission in times of market duress.

Proponents of circuit breakers claim that, during major price changes there can be a

breakdown in the transmission of information between the trading floor and market

participants. Therefore, "the primary function of a circuit breaker should be to re-inform

participants (Greenwald and Stein (1988)." Lee, et.al (1994) examined the volume,

53

volatility and trading halts in NYSE. The ISSM database is used for the analysis. The

initial sample consists of all trading halts on NYSE common stocks during 1988 as

identified by the ISSM database. Finally, a total of 42 observations were eliminated

yielding a sample of 852 trading halts. The study also look for the Wall Street Journal

Index, the New York Times Index, and the "Wires" and "Papers" services in the

Lexis/Nexis online database to classify the news events underlying the halts and used

only those articles that expressly mentioned the firm and provided a reason for the halt on

the day indicated by the ISSM tape. Then, classified the news into six general categories:

1) Acquisitions and Divestitures, 2) Capital Structure Changes, 3) Takeovers and

Leveraged Buyouts (LBOs), 4) Financial Information, 5) Legal and Miscellaneous News

and 6) No News. With the mean abnormal volume and volatility statistics and the

regression analysis, the major findings of the study are: trading halts increase, rather than

reduce, both volume and volatility. Volume (volatility) in the first full trading day after a

trading halt is 230 percent (50 to 115percent) higher than following "pseudo-halts": non-

halt control periods matched on time of day, duration, and absolute net-of-market returns.

These results are robust over different halt types and news categories. Higher post-halt

volume is observed into the third day while higher post-halt volatility decays within

hours. The extent of media coverage is a partial determinant of volume and volatility

following both halts and pseudo-halts, but a separate halt effect remains after controlling

for the media effects.

Brennan and Subrahmanyam (1995) investigated the relation between the number of

analysis following a security and the estimated adverse selection cost of transacting in the

security, controlling for the effects of previously identified determinants. The study

defined the adverse selection costs of transacting as trading be investors who possess

superior information imposes significant liquidity costs on other market participants due

to adverse selection. Using intraday data of the year 1988, the study found that greater

analyst following tends to reduce adverse selection cost based on the market depth. In

other words, other things equal, an increase in the number of investment analysts tends to

be associated with reduction in the adverse selection cost of transacting.

Several recent studies have documented that, at medium-term horizons ranging from three

to 12 months, stock returns exhibit momentum – that is, past winners continue to perform

well, and past losers continue to perform poorly. Hong, et.al (2000) aimed to test the

Hong-Stein version of the underreaction hypothesis, namely: if momentum comes from

gradual information flow, then there should be more momentum in those stocks for which

54

information gets out more slowly. In other words, the study looks for evidence that

momentum reflects the gradual diffusion of firm-specific information. Using the data

from three primarily sources: the stock returns and turnover data from CRSP, the data on

analyst coverage are from the Institutional Brokers' Estimate System (IBES) and the

options-listing data come from the Options Clearing Corporation from 1976 to 1996 and

establish three key results. First, once one moves past the very smallest stocks, the

profitability of momentum strategies declines sharply with firm size. Second, holding size

fixed, momentum strategies work better among stocks with low analyst coverage. Finally,

the effect of analyst coverage is greater form stocks that are post losers than for past

winners. Thus, the study concluded that the findings are strongly consistent with the

Hong-Stein hypothesis, especially negative information, diffuses only gradually across

the investing public.

ii) Review of major studies on intangible information between 2000 and 2010

The internet message board activities, the effect of state of economy and the news, the

effect of information arrival to the public and its impact on stock returns, etc have been

managed in this sub-section. Table 2.12 presents the review of major studies and its major

findings as below:

Table 2.12: Review of major studies on market reactions to intangible information between 2000 to 2010

Study Major findingsTumarkin and Whitelaw (2001)

The internet message board activity did not predict industry-adjusted returns or abnormal trading volume, which is consistent with market efficiency.

Conrad, et.al (2002)

In explanation of the uncertainty about the state of the economy causes an asymmetry in the response to good news and bad news, the study support the hypothesis that stock prices respond most strongly to bad news in good times.

Vega (2006) There are not all information acquisition variables have the same effect on the market’s efficiency. Whether information is public or private is irrelevant; what matters is whether information is associated with the arrival rate of informed or uninformed traders.

Worthington (2006)

The study shows returns are highest during the ministries of Holt-McEwen and Hawke and lowest during Whitlam and Fraser, while risk is highest during Whitlam and Hawke and lowest during Menzies and Holt-McEwen. Thus, the study concluded that the risk differences potentially arise from the different parties’ economic and social policies, uncertainty among investors about these policies, or doubt among voters concerning future election outcomes.

Epstein and Schneider (2008)

Investors react asymmetrically to market signals as discount good news, but take bad news seriously.

Hertzberg, et.al (2010)

Loan Officers' rotation can be used to limit agency problems in communication and to detect the performance based bad news due to career concerns.

Financial economists believed that the internet is clearly playing an increasing role in

financial market and personal finance. The same issue has been analyzed as News or

55

Noise? internet posting and stock prices. Tumarkin and Whitelaw (2001) depicted that

there is growing effect of posting in internet financial forums affect stock prices, either

because the posting contain new information or because they represent successful

attempts to manipulate stock prices. The study examined the relationship between internet

message board activities and stock returns and trading volume during the study period

beginning from mid-April 1999 to mid-February 2000. The study focused on the

RagingBull.com discussion forum which is an extremely popular site whose format

permits the construction of an objective measure in investor opinions. The study found

that for stocks in the internet service sector, on days with abnormally high message

activities, changes in investor opinion correlated with abnormal industry-adjusted returns.

These event days also coincided with abnormally high trading volume, which persisted

for a second day. However, the study concluded that message board activity did not

predict industry-adjusted returns or abnormal trading volume, which is consistency with

market efficiency.

The growing concern in the capital market is, whether the price response to bad and good

earnings shocks changes as the relative level of the market changes. Conrad, et.al (2002)

examined this relationship. The study is based on the complete sample of annual earnings

announcements during the period 1988 to 1998, consensus earnings forecasts, realized

earnings, and earnings report dates are collected from IBES. The relative level of the

market is based on the difference between the current market price-earnings ratio and the

average market price-earnings ratio over the prior 12 months. The study explained that

the uncertainty about the state of the economy causes an asymmetry in the response to

good news and bad news. That is, when investors believe that the economy is in a bad

state and good news arrives, the inferred probability that the market is in a good state

increases; thus, the positive impact on prices is offset by the rising discount rate generated

by increased investor uncertainty. The aim of the study is to examine whether the strength

of firm-specific responses to new information is affected by the aggregate level of the

market. The study is worked against the a practitioner hypothesis quoted in the Wall

Street Journal, is that the stock prices of individual forms become relatively more

sensitive to bad news than good news as the market rises. This hypothesis is related to

two stands of literature. First, based on research in behavioral psychology, suggest that

investors inappropriately extrapolate past performance. Second, based on extended

regime shifting models, also predicts that the market will respond more strongly to bad

news than good news when stock prices are high. Then, the study suggested that both the

56

behavioral models and the regime shifting model could be extended to explain more fully

how the response of individual forms to earnings announcement may depend on the level

of the market. Primarily, the study found that the stock price response to negative

earnings surprises increases as the relative level of the market rises. Furthermore, the

difference between bad news and good news earnings response coefficients rise with the

market. In sum, the findings generally support the hypothesis that stock prices respond

most strongly to bad news in good times.

Worthington (2006) examined the presence of a political cycle in Australian daily stock

returns over 47 years from 6 January 1958 to 30 December 2005. The study period

includes 19 federal elections, 25 ministries and five terms of Liberal-National or Labor

government. The political cycle is defined in terms of the party in power, the time since

the last election and election information effects. The market variables are defined in

terms of nominal and real returns and nominal and real returns volatility. The results

indicates that highest returns during the ministries of Holt-McEwen and Hawke and

lowest during Whitlam and Fraser, while risk is highest during Whitlam and Hawke and

lowest during Menzies and Holt-McEwen. However, regression analysis shows that

Liberal-National and Labor governments more generally differ in the volatility of returns

where political cycle-sourced return volatility increases at a decreasing rate with the time

in power. Such risk differences potentially arise from the different parties’ economic and

social policies, uncertainty among investors about these policies, or doubt among voters

concerning future election outcomes. The study employs the parametric analysis to test

for a political cycle, a comparison of mean returns provides some empirical evidence to

support the conjecture that returns depend upon the government in power. There is

limited support for an election effect where returns are systematically higher or lower in

the period leading up to immediately following an election. Similar results are obtained

with a regression based analysis. Stock volatility reflects diffuse and easily changed

beliefs about future political behavior, but on balance, these views are never

systematically ‘bad’ or ‘good’ over extended periods of time.

In an efficient market, security prices at any given time fully reflect all available

information. Vega (2006) aimed to deepen the understanding on how private and public

information received by agents prior to earnings announcements affects the post-earnings

announcement drift. Data employed for the study are from six different sources: CRSP,

Compustat, ISSM, Trade and Quote (TAQ), IBES and Dow Jones Interactive. Finally, the

study obtained a final sample of 9,213 firms and 208,540 firm-quarter observations, and

57

used two variables to measure the amount of public information that is available to

investors prior to earnings announcements. Firstly, media coverage is defined as TAQ as

the number of days a particular firm is mentioned in the news prior to its earnings

announcement. Secondly, public news surprises is calculated when agents receive prior to

a firm’s earnings announcement using the stock market’s reaction to headline news. The

study used Easley and O’Hara (1992) information-based trading variables, personal

identification number (PIN), together with a comprehensive public news database to

empirically measure the effect of private and public information on the post-

announcement drift. The findings show that stocks associated with high PIN, consensus

public news surprises, and low media coverage experience low or insignificant drift.

Thus, there are not all information acquisition variables have the same effect on the

market’s efficiency. Whether information is public or private is irrelevant; what matters is

whether information is associated with the arrival rate of informed or uninformed traders.

Financial market participants absorb a large amount of news, or signals, every day.

Processing a signal involves quality judgments: News from a reliable source should lead

to more portfolios rebalancing than news from an obscure source. Unfortunately, judging

quality itself is sometimes difficult. It is true especially for tangible information, such as

earnings reports, that lends itself to quantitative analysis. By looking at past data,

investors may become quite confident about how well earnings forecast returns. Epstein

and Schneider (2008) focused on information processing when there is incomplete

knowledge about signal quality. The main idea is that, when quality is difficult to judge,

investors treat signals as ambiguous. In other words, the study analyzed the role of

uncertain information quality in financial markets. The proposed new model of

information processing by ambiguity-averse investors in the study, it is assumed that

investors perceive a range of signal precisions, and take a worst-case assessment of

precision when evaluating prior utility. The study documented that investors react

asymmetrically to signals: they discount good news, but take bad news seriously.

Moreover, they get disutility from low future information quality. The study also

emphasized three new effects of uncertain information quality on asset prices. First,

investors require compensation for low future information quality. Expected excess

returns are thus higher when information quality is more uncertain, holding fixed the

distribution of fundamentals. Second, investors require more compensation for low

information quality when fundamentals are more volatile. In markets in which

information quality is uncertain, expected excess returns thus scale with volatility, not

58

with covariance with the market or with marginal utility. Third, investors’ asymmetric

response to signals skews the distribution of observed returns: When there are signals of

uncertain quality, which generate negative skewness, signals of known quality generate

positive skewness. Thus, the study concluded that when ambiguity-averse investors

process news of uncertain quality, they act as if they take a worst-case assessment of

quality and react more strongly to bad news than to good news.

A rotation policy that routinely reassigns loan officers to borrowers of a commercial bank

affects the officers’ reporting behavior. When an officer anticipates rotation, reports are

more accurate and contain more bad news about the borrower’s repayment prospects.

Hertzberg, et.al (2010) presented the evidence that reassigning tasks among agents can

alleviate moral hazard in communication. As a result, the rotation policy makes bank

lending decisions more sensitive to officer reports. The threat of rotation improves

communication because self-reporting bad news has a smaller negative effect on an

officer’s career prospects than bad news exposed by a successor. Using data from the

internal records of the small and medium business division of the banks, the study

construct a monthly panel of loan officer-firm relationships. The sample covers the 7-year

period from December 1997, when the small and medium business division was created,

to December 2004. The observation includes 1,248 firms and 100 loan officers in 4,191

non-censored loan officer-firm relationships. In sum, the study concluded that rotation

can be used to limit agency problems in communication and to detect the performance

based bad news due to career concerns.

iii) Review of major studies on tangible information before 1990

Table 2.13 presents the review of major studies on tangible information before 1990s and

its major findings. Some of the key contributions during this period are: the positive

tradeoff between risk and returns, the effect of money supply in the stock returns, the

systematic price reversal, etc which has been organized as follows:

Table 2.13: Review of major studies on market reactions to tangible information before 1990

Study Major findingsFama and MacBeth (1973)

A positive tradeoff between return and risk.

Urich and Wachtel (1981)

The financial markets respond very quickly to the weekly money supply announcement.

De Bound and Thaler (1985)

Systematic price reversals for stocks that experience extreme long-term gains or losses: past losers significantly outperform past winners.

59

Cutler, et.al (1989)

The pieces of information, discount rates or the cash flows have large effects on stock prices.

An empirical work which tests the relationship between average return and risk for New

York Stock Exchange common stocks is performed by Fama and MacBeth (1973). The

theoretical basis of the tests is the "two-parameter" portfolio model and models of market

equilibrium derived from the two-parameter portfolio. The data for the study are monthly

percentage returns - including dividends and capital gains, and with the appropriate

adjustments for capital changes such as splits and stock dividends, for all common stocks

traded on the NYSE during the period January 1926 through June 1968. Related data

were derived from the CRSP. The sample period is divided into nine sub-periods for each

of portfolio formation, initial estimation period and for testing period. Rigorous

regression models are developed and tested in various forms, the behavior of the market

and components of the variance of the returns for the different sub-periods are analyzed in

varying forms of regression models. All above tests fail to reject the hypothesis of these

models that the pricing of common stocks reflects the attempts of risk-averse investors to

hold portfolios that are "efficient" in terms of expected value (return), and dispersion of

return (risk). Moreover, the observed "fair game" properties of the coefficients and

residuals of the risk-return regressions are consistent with an efficient capital market- that

is, a market where prices of securities fully reflect the available information. In

conclusion, the results support the important testable implications of the two parameter

model. Given that the market portfolio is efficient or, more specifically, given that proxy

for the market portfolio is at least approximately efficient. Thus, the tests cannot reject

the hypothesis that average returns on the NYSE common stocks reflect the attempts of

risk-averse investors to hold efficient portfolios. Specifically, on average there seems to

be a positive tradeoff between return and risk. Thus, the evidence support the hypothesis

that in making a portfolio decision, an investor should assume that the relationship

between a security's portfolio risk and its expected return is linear, as implied by the two-

parameter model. Also, the study cannot reject the hypothesis of the two-parameter model

that no measure of risk, in addition to portfolio risk, systematically affects average

returns. Finally, the observed fair game properties of the coefficients and residuals of the

risk-return regressions are consistent with an efficient capital market.

The predicted values from ARIMA models and the median forecasted change from the

survey are used as the expected money supply in a study of macroeconomic effects on

stock returns. Urich and Wachtel (1981) analyzed the market response to the weekly

60

money supply announcements. Interest rates, money supply and the expected money

supply are three variables of the analysis. The period under consideration for the study is

1970 to 1979. The major finding of the study is the financial markets respond very

quickly to the weekly money supply announcement.

Do the Stock Market Overreact? The motivation for De Bound and Thaler (1985) is the

other research in experimental psychology suggests that, in violation of Bayes' rule, most

people tend to overreact to unexpected and dramatic news events. What is an appropriate

reaction? Both classes of behavior that is market behavior and the psychology of

individual decision making behavior can be characterized as displaying overreaction. For

example, individuals tend to overweight recent information and underweight prior and in

spite of the observed trendiness of dividends, investors seem to attach disproportionate

importance to short-run economic developments. Amid these observations, the study is

undertaken to investigate the possibility that the market behavior and the psychology of

individual decision making are related by more than just appearance. In other words, the

goal is to test whether the overreaction hypothesis is predictive i.e. whether such behavior

affects the stock prices. The findings are based on NYSE common stocks monthly return

data retrieved from CRSP for the period 1926 to 1982, is consistent with the overreaction

hypothesis. Specifically, two more hypotheses are tested; first, extreme movements in

stock prices will be followed by subsequent price movements in the opposite direction

and the other, more extreme the initial price movement, the greater will be the subsequent

adjustment. Both hypotheses imply a violation of weak-form of market efficiency.

Substantial weak form of market inefficiencies is discovered. The results also shed new

light on the January returns earned by prior winners and losers. Portfolios of losers

experience exceptionally large January returns as late as five years after portfolio

formation which is concluded with the analysis of cumulative average residuals for

winner and loser portfolios. Portfolios are formed of the 50 most extreme winners and 50

most extreme losers measured by cumulative excess returns over successive five year

formation periods. Thus, the major outcome of the study is systematic price reversals for

stocks that experience extreme long-term gains or losses: Past losers significantly

outperform past winners.

Several studies of asset pricing have challenged the view that stock price movements are

wholly attributable to the arrival of news. Cutler, et.al (1989) estimated the fraction of the

variance in aggregate stock returns that can be attributed to various types of economic

news. To understand whether unexpected macroeconomic developments can explain a

61

significant fraction of share price movements, the study analyzes monthly stock returns

for the 1926-1985 period, as well as annual returns for the longer 1971-1986 period.

Regression models and less structured approaches are used to examine the news and the

study found that the arrival of information about macroeconomic performance news

proxies can explain about one-third of the variance in stock returns; while analyzing the

stock market reactions to identifiable world news – regarding wars, the Presidency, or

significance changes in financial policies affect stock prices, the findings cast doubt on

the view that qualitative news can account for all the return variation that cannot be traced

to macroeconomic innovations. Thus, the study argued that further understanding of asset

price movements requires two types of research. The first should attempt to model price

movements as functions of evolving consensus opinions about the implications of given

pieces of information. The second should develop and test ‘propagation mechanisms’ that

can explain why stocks with small effects on discount rates or cash flows may have large

effects on prices.

iv) Review of major studies on tangible information during 1990s

The tangible information for instance, macroeconomic indicators, the intraday trading

patterns, the effect of dividend omission, etc during the period 1990s have been organized

in this sub-section. The major findings of such studies have been presented in Table 2.14

as follows.

Table 2.14: Review of major studies on market reactions to tangible information during 1990s

Study Major findingsEderington and Lee (1993)

The scheduled macroeconomic news announcements are responsible for most of the observed time-of-day and day-of-week volatility patterns. Thus, there is greatest impact of these announcements on interest rate and foreign exchange futures markets.

Berry and Howe (1994)

The public information arrival is nonconstant, displaying seasonalities and distinct intraday patterns - information arrival exhibits an inverted U-shape pattern across trading days. Next, the study relate the measure of public information to aggregate measures of intraday market activity which suggest a positive, moderate relationship between public information and trading volume, but an insignificant relationship with price volatility.

Michaely, et.al (1995)

The magnitudes of short-run price reactions to dividend omissions are greater than for dividend initiations. In the year following the announcements, prices continue to drift in the same direction, though the drift following omissions is stronger and more robust.

Braun, et.al (1995)

There is strong evidence of conditional heteroskedasticity in both market and non-market components of returns, and weaker evidence of time-varying conditional betas.

La Porta, et.al (1997)

The announcement returns suggest a significant portion of the return difference between value and glamour stocks is attributable to earnings surprises that are systematically more positive for value stocks.

62

Impact of scheduled macroeconomic news announcements on interest rate and foreign

exchange futures markets is examined by Ederington and Lee (1993). The study found

these announcements are responsible for most of the observed time-of-day and day-of-

week volatility patterns in these markets. While the bulk of the price adjustment to a

major announcement occurs within the first minute, volatility remains substantially higher

than normal for roughly fifteen minutes and slightly elevated for several hours.

Nonetheless, these subsequent price adjustments are basically independent of the first

minute’s returns. Thus, the study identified those announcements with the greatest impact

on interest rate and foreign exchange future markets.

The link between information and changes in asset prices is the central issue in financial

economics. A fundamental tenet of market efficiency is that investors react to new

information as it arrives, resulting in price changes that reflect investors' expectations of

risk and return. Berry and Howe (1994) developed a measure of public information flow

to financial markets and used it to document the patterns of information arrival, with an

emphasis on the intraday flows. The measure is the number of news releases by Reuter's

News Service per unit of time. Over 120,000 observations were collected from May 1990

to April 1991. The database contained all information events, not only firm-specific

information, over the full 24-hour day. The study found the public information arrival is

non-constant, displaying seasonality and distinct intraday patterns - information arrival

exhibits an inverted U-shape pattern across trading days. Next, the study relate the

measure of public information to aggregate measures of intraday market activity which

suggest a positive, moderate relationship between public information and trading volume,

but an insignificant relationship with price volatility.

When a firm initiates the payment of a cash dividend, or omits such a payment, the firm is

making an extremely visible and qualitative change in corporate policy. What effect do

such abrupt changes have on returns? Michaely, et.al (1995) investigates both the

immediate (three-day) reaction to initiation or omission announcements and the long-term

post-announcement price performance. The study used CRSP tapes to collect all NYSE

and AMEX companies that initiated dividends during 1964 to 1988 and defined a

dividend initiation as the first cash dividend payment reported on the CRSP master file,

reinstitution of a cash dividend is not considered. During 25 years, total 561 cash

dividend events are considered the resulting sample for the study. Consistent with prior

literature, the study concluded that the magnitudes of short-run price reactions to

omissions are greater than for initiations. In the year following the announcements, prices

63

continue to drift in the same direction, though the drift following omissions is stronger

and more robust.

Many studies have been documented that stock returns volatility tend to rise following

good and bad news, this phenomenon, which Braun, et.al (1995) explained as predictive

asymmetry of second moments. The study investigated the conditional covariance of

stock returns using bivariate exponential ARCH (EGARCH) models. These models allow

market volatility, portfolio specific volatility, and beta to respond asymmetrically to

positive and negative market and portfolio returns, i.e., "leverage" effects. Using monthly

data, the study depicted strong evidence of conditional heteroskedasticity in both market

and non-market components of returns, and weaker evidence of time-varying conditional

betas. Surprisingly while leverage effects appear strong in the market component of

volatility, they are absent in conditional betas and weak and/or inconsistent in nonmarket

sources of risk.

Most of the financial researches agreed that simple value strategies based on such ratios

as book-to-market, earnings-to-price and cash flow-to-price have produced superior

returns over a long period of time. La Porta, et.al (1997) examined the hypothesis that the

superior return to so-called value stocks is the result of expectational errors made by

investors. The study examined the stock price reactions around earnings announcements

for value and glamour stocks over a 5-year period after portfolio formation. The study

used the sample firms of NYSE, AMEX and Nasdaq that are available on Compustat and

CRSP tapes. The sample period runs from 1971:2 through 1993:1. To examine earnings

announcement return differences between value and glamour stocks, the study form

portfolios on the basis of two classifications: the book-to-market ratio and two-way

classification based on cash-flow-to-price and past growth-in-sales. The findings are:

expectational errors about future earnings prospects play an important role in the superior

return to value stocks, post-formation earnings announcement returns are substantially

higher for value stocks than for glamour stocks, event returns for glamour stocks are

significantly lower than glamour returns on an average day, which is inconsistent with the

risk premium explanation for the return differences between value and glamour stocks,

and in full sample, earnings announcement return differences account for approximately

25-30 percent of the annual return differences between value and glamour stocks in the

first two to three years after portfolio formation and approximately 15-20 percent of

return differences over years four and five after formation. Thus, the study concluded that

the announcement returns suggest a significant portion of the return difference between

64

value and glamour stocks is attributable to earnings surprises that are systematically more

positive for value stocks. The evidence is inconsistent with a risk-based explanation for

the return differential.

v) Major studies on tangible information 2000 onwards

Table 2.15 shows the review of major studies and its key findings regarding market

reactions to tangible information for the period 2000 onwards are as follows.

Table 2.15: Review of major studies on market reactions to tangible information 2000 onwards

Study Major findingsVeronesi (2000) The relationship between the precision of public information about economic growth

and performance of the stock market is nontrivial.

Andersen, et.al (2000)

The intra-daily volatility exhibits a doubly U-shaped pattern associated with the opening and closing of the separate morning and afternoon trading sessions on the Tokyo Stock Exchange, which is consistent with market microstructure theories that emphasize the role of private and asymmetric information in the price formation process.

Brav and Lehavy (2003)

There is a significant market reaction to the information contained in analysts’ target prices, both unconditionally and conditional on contemporaneously issued stock recommendation and earnings forecast revisions.

Grinblatt and Moskowitz (2004)

The consistency of positive past returns and tax-loss selling significantly affects the relation between past returns and the cross-section of expected returns.

Domer (2005) Public financial information has an impact on stock market behavior. Thus, the study concluded a positive correlation between the stock prices and the information categories: net asset value, occupancy rates, cash flow and overall capitalization rate.

Kothari, et.al (2006)

Market reaction to aggregate earnings is much different than the reaction to firm earnings and there is little evidence that prices react slowly to aggregate earnings news and the behavioral theories that explain post-earnings announcement drift in firm returns do not seem to describe aggregate price behavior.

Watanabe (2008) Accurate information increases the volatility. Eaves and Williams (2010)

The intraday volume is U-shaped, intraday volatility is closer to L-shaped even if the previous studies reported U-shaped intraday volatility, and the study concluded that the timing of privately informed traders cannot be the source of intraday patterns.

In modern financial markets, investors are flooded with a variety of information: firms’

earnings reports, revisions of macroeconomic indexes, policymakers’ statements, and

political news. These pieces of information are processed by investors to update their

projections of the economy’s future growth rate, inflation rate, and interest rate. Veronesi

(2000) using a simple dynamic asset pricing model, investigated the relationship between

the precision of public information about economic growth and stock market returns.

After fully characterizing expected returns and conditional volatility, the study

documented that higher precision of signals tends to increase the risk premium; when

signals are imprecise or noisy the equity premium is bounded above independently of

investors’ risk aversion; return volatility is U-shaped with respect to investors’ risk

65

aversion; and the relationship between conditional expected returns and conditional

variance is ambiguous. Thus, the study showed that the relationship between the precision

of public information about economic growth and performance of the stock market is

nontrivial.

The volatility in the Japanese stock market based on a 4-year sample of 5-min Nikkei 225

returns from 1994 through 1997, is characterized by Andersen, et.al (2000). The intra-

daily volatility exhibits a doubly U-shaped pattern associated with the opening and

closing of the separate morning and afternoon trading sessions on the Tokyo Stock

Exchange. This feature is consistent with market microstructure theories that emphasize

the role of private and asymmetric information in the price formation process.

Meanwhile, readily identifiable Japanese macroeconomic news announcements explain

little of the day-to-day variation in the volatility, confirming previous findings for US

equity markets. Furthermore, by appropriately filtering out the strong intraday periodic

pattern, the high-frequency returns revealed the existence of important long-memory

intra-daily volatility dependencies.

In recent years, security analysts have been increasingly disclosing target prices, along

with their stock recommendations and earnings forecasts. Using a large database of

analyst price targets, stock recommendations, and earnings forecasts, Brav and Lehavy

(2003) examined the short-term market reactions to target price announcements and long-

term co-movement of target and market prices. Using a large database of analysts’ target

prices issued over the period 1997 to1999, the study found a significant market reaction

to the information contained in analysts’ target prices, both unconditionally and

conditional on contemporaneously issued stock recommendation and earnings forecast

revisions. Using a co-integration approach, the study analyzes the long-term behavior of

market and target prices and found that, on average, the one-year-ahead target price is 28

percent higher than the current market price. Moreover, revisions in target prices contain

information about six-month post-event abnormal returns. Recommendation and earnings

forecast revisions are also found to be informative in the presence of target prices. Since,

the study aimed to explore and document the evidence on the informativeness and time-

series behavior of analysts’ target prices, contributes to the understanding of price

formation in equity markets.

Grinblatt and Moskowitz (2004) analyzed the consistency and sign of the past return as

well as the degree to which tax-motivated trading generates effects on future returns. Both

66

short-term and long-term past returns contain information about expected returns because

they are the proxy for a more fundamental variable that predicts returns. A key finding of

the analysis is that winner consistency is important. The consistency can also be the proxy

for inverse of volatility and this may affect average returns as a proxy for risk. Achieving

a high past return with a series of steady positive months appears to generate a larger

expected return than a high past return achieved with just a few extraordinary months.

The study also highlighted the importance of seasonality associated with past returns and

the degree to which tax-loss trading plays a role in past return predictability. The study

used the approach in analyzing the importance of these complex patterns of returns is

based on a parsimonious stock ranking system derived from simple Fama-MacBeth cross-

sectional regressions. The study analyzes the simultaneous effect of a number of past

return-related variables on the future returns of hedged positions in individual stocks,

which have their size, book-to-market, and industry return components eliminated and the

beta neutral as well. The sample constituted the monthly returns from every records of

security on the CRSP data files from August 1963 to December 1999. From 1963 to

1973, the CRSP sample includes NYSE and AMEX firms only, and post-1973

NASDAQ-NMS firms are added to the sample. Thus, the consistency of positive past

returns and tax-loss selling significantly affects the relation between past returns and the

cross-section of expected returns. Analysis of these additional effects across stock

characteristics, seasons, and tax regimes provides clues about the sources of temporal

relations in stock returns, pointing to potential explanations for this relation. A

parsimonious trading rule generates surprisingly large economic returns despite controls

for confounding sources of return premia, microstructure effects, and data snooping

biases.

The analysis on responses of stock prices to financial announcements is examined by

Domer (2005). The study employed a computer-based content analysis of qualitative data.

The data is from a Swedish real estate firm during the period 1991-1996. The information

collected and analyzed comes from the company's press releases, quarterly statements and

articles in the six largest business magazines in Sweden. Annual statements were

excluded from the study due to the fact that the publication of these statements occurred

at a point of time when the information was already reflected in the share prices. The

major finding of the study is a positive correlation between the stock prices and the

following information categories: net asset value, occupancy rates, cash flow and overall

capitalization rate. These results are compared to other studies investigating the influence

67

of information on stock prices. The results of the study confirmed previous results. Thus,

the main contribution of the study is that it supports the assumption that public financial

information has an impact on stock market behavior.

A study on stock market's reaction to aggregate earnings news, Kothari, et.al (2006)

found a substantially different pattern in aggregate earning and returns. In prior studies,

for individual firms, stock prices react positively to earnings news but require several

quarters to fully reflect the information in earnings. The study concluded two major

findings: First, returns are unrelated to past earnings, suggesting that prices neither

underreact nor overreact to aggregate earnings news. Second, aggregate returns correlate

negatively with concurrent earnings during the study period 1970 to 2000. The earnings

series include all NYSE, AMEX, and NASDAQ stocks with data for earnings, price, and

book equity on the Compustat Quarterly file from 1970 – 2000. The market return is the

CRSP value-weighted index and compound monthly index returns to obtain quarterly

returns. Fama-MacBeth regression, time series analysis, correlation, auto-regressive

models, autocorrelations and behavioral models were used for the analysis. The study

also suggests that earnings and discount rates move together over time which is

inconsistent with asset-pricing models that imply discount rates and cash flows move in

opposite directions, and provides new evidence that discount-rate shocks explain a

significant fraction of aggregate stock returns. In conclusion, the market’s reaction to

aggregate earnings is much different than the reaction to firm earnings and there is little

evidence that prices react slowly to aggregate earnings news and the behavioral theories

that explain post-earnings announcement drift in firm returns do not seem to describe

aggregate price behavior.

A study on price volatility and investor behavior in an Overlapping Generations Model

with information asymmetry is conducted by Watanabe (2008). The study begins with the

issue: the mounting evidence of both trend-following and contrarian behavior among

various investor groups in recent empirical studies. Trend-followers buy assets upon price

appreciation and sell them upon depreciation, while contrarians trade in the opposite way.

Such trading behavior is found in both domestic and international markets. Moreover,

prices in these markets are found to vary much more than the stocks’ fundamental values.

The study follows an Overlapping Generations Model (OGM) with multiple securities

and heterogeneously informed agents. The study first found that asset prices can be highly

volatile relative to dividend variability and less informed agents rationally behave like

trend-followers, while better informed agents follow contrarian strategies. Trading

68

volume has a hump-shaped relation with information precision and is positively

correlated with absolute price changes. Calibrating the Full-Information Model and

explaining the phenomena from a fully rational perspective, the empirical studies

documented that various investor classes follow trend-chasing and contrarian strategies in

both domestic and international markets. Many of these markets are found to exhibit

excess volatility and, in some cases, strong co-movements in asset returns. Finally,

accurate information increases the volatility and correlation of stock returns in highly

volatile and strongly correlated equilibrium.

In a study of intraday volume and volatility, Eaves and Williams (2010) documented that

no matter how pronounced intraday patterns may appears, it is difficult to account for

cross-correlations among related assets when those assets trade continuously and

simultaneously. Based on the practice, futures contracts are auctioned periodically and

sequentially on the Tokyo Grain Exchange (TGE) the study analyses the intraday volume

and volatility. The dataset covers the 1,407 business days from May 1994 through

January 2000, encompassing 5,540 trading sessions for corn, 8,394 sessions for red beans,

5,596 for soybeans, and 7,004 for sugar. Even though intraday TGE volume is U-shaped,

intraday volatility is closer to L-shaped even if the previous studies reported U-shaped

intraday volatility for example the market microstructure theory which sought to explain

why intraday volatility is U-shaped. After accounting for the public information in

immediately preceding auctions for the same commodity, for earlier trading in other

commodities, and for trading on overseas markets open overnight in Tokyo, the intraday

patterns are effectively flat. Thus, the timing of privately informed traders cannot be the

source of intraday patterns.

g) Review of major studies related to media effects

The media coverage, public relations, other investor behavior and stock returns, media

optimism and pessimism and its relation to stock returns, high and low media coverage

and stock returns, etc are reviewed in this sub-section. The study period covered 1987 to

2011. Table 2.16 shows the studies and its major findings as follow.

The Efficient Market Hypothesis (EMH) assumes that the real-world investors at the time

of their portfolio decisions have access to the complete prior history of all stock returns.

When, however, investors’ decisions are made, the price data may not have been in

reasonably-accessible form and the computational technology necessary to analyze all

these data may not even have been invented. In such cases, the classification of all prior

69

price data as part of the publicly available information set may introduce an important

bias. The study developed a two-period model of capital market equilibrium in an

environment where each investor knows only about a subset of the available information.

Thus, the study analyzes the impact on the structure of equilibrium asset prices caused by

the particular type of incomplete information. The comparative statistics are used to

Table 2.16: Review of major studies on media effect on stock returnsStudy Major findingsMerton (1987) Financial markets dominated by rational agents may nevertheless produce anomalous

behavior relative to the perfect-market model. Thus, media coverage, public relations and other investor marketing activities could play an important causal role in creating and sustaining speculative bubbles and fads among investors.

Tetlock (2007) The study primarily contributed the three things as: First and foremost, the study found that high levels of media pessimism robustly predict downward pressure on market prices, followed by a reversion to fundamentals. Second, unusually high or low values of media pessimism forecast high market trading volume. Third, low market returns lead to high media pessimism.

Fang and Peress (2009)

High-media coverage stocks earn lower returns.

Engelberg and Parsons (2011)

The presence or absence of local media coverage is strongly related to the probability and magnitude of local trading.

analyze the cross-sectional differences among expected returns. Total of 1387 sample

firms were taken from the COMPUSTAT tapes as on December 31, 1985. Merton (1987)

found that media coverage, public relations and other investor marketing activities could

play an important causal role in creating and sustaining speculative bubbles and fads

among investors, the expanded media coverage of a firm, industry or other sector of the

economy is stimulated by changes in the same economic fundamentals that cause firms to

change their plans and investors to reassess their portfolio, advertising that initially

attracts investor attention to a firm is assumed to leave that firm’s investor base

unchanged if the underlying fundamentals do not justify a change. Thus, the study

concluded that financial markets dominated by rational agents may nevertheless produce

anomalous behavior relative to the perfect-market model. Institutional complexities and

information costs may cause considerable variations in the time scales over which

different types of anomalies are expected to be eliminated in the market place. Whether or

not the specific information inefficiency posited can be sustained in the long run, the

model may nevertheless provide some intermediate insights into the behavior of security

prices.

Causal observation suggests that the content of news about the stock market could be

linked to investor psychology and sociology. However, it is unclear whether the financial

news media induces, amplifies, or simply reflects investors’ interpretations of stock

70

market performance. Tetlock (2007) attempted to characterize the relationship between

the content of media reports and daily stock market activity, focusing on the immediate

influence of the Wall Street Journal’s “Abreast of the Market” column on U.S. stock

market returns over the 16 year period from 1984 to 1999 and 7895 words and verbs were

analyzed and classified into 7 broad groups and 77 categories based on Harvard

dictionary. Using principal components analysis (PCA), the study construct a simple

measure of media pessimism from the content of the WSJ column, then estimate the inter-

temporal links between this measure of media pessimism and the stock market using

basic vector autoregressions. First and foremost, the study found that high levels of media

pessimism robustly predict downward pressure on market prices, followed by a reversion

to fundamentals. Second, unusually high or low values of media pessimism forecast high

market trading volume. Third, low market returns lead to high media pessimism. These

findings suggested that measures of media content serve as a proxy for investor sentiment

or non-informational trading.

On a study of media coverage and the cross-section of stock returns, Fang and Peress

(2009) tested the hypothesis, by reaching a broad population of investors, mass media can

alleviate informational frictions and affect security pricing even if it does not supply

genuine news. The study investigates this hypothesis by studying the cross-sectional

relation between media coverage and expected stock returns and found that stocks with

no media coverage earn higher returns than stocks with high media coverage even after

controlling for well-known risk factors. These results are more pronounced among small

stocks and stocks with high individual ownership, low analyst following, and high

idiosyncratic volatility. The findings suggest that the breadth of information

dissemination affects stock returns. The sample considered all the listed companied on the

NYSE, contains mainly large stocks and 500 randomly selected companies listed on the

NASDAQ between 1993 and 2002. Univariate analysis, comparison of average returns of

stocks with firm characteristics and media coverage and multivariate analysis, four

different factor models: the market model, the Fama-French (1993) three-factor model,

the Carhart (1997) four-factor model, and a five-factor model that includes the Pastor-

Stambaugh (2003) liquidity factor has used for the analysis. The major finding of the

study is that high-media coverage stocks earn lower returns.

On a study of media and financial markets, Engelberg and Parsons (2011) analyzed the

causal impact of media in financial markets. The objective is to disentangling the causal

impact of media reporting from the impact of the events being reported. The study is

71

conducted by comparing the behaviors of investors with access to different media

coverage of the same information event. The study followed two approaches: the first is

to select the events which is the determinants of media coverage and market responses

can be decoupled, Brute-force approach. The second is cross-sectional approach, the basic

idea of this is to take two groups of agents and for the same information event, vary only

media exposure. This study is primarily focus on second approach. Using the multivariate

regression model for 19 mutually exclusive trading regions corresponding with large U.S.

cities, the study found that local media coverage strongly predicts local trading, after

controlling for earnings, investor, and newspaper characteristics for all earnings

announcements of S&P 500 Index firms. Moreover, local trading is strongly related to the

timing of local reporting. Thus, analyzing the simultaneous reactions of investors in 19

local markets to the same set of information events like earnings releases of S&P 500

Index firms, the study concluded that the presence or absence of local media coverage is

strongly related to the probability and magnitude of local trading.

h) Review of major studies on news effects

The number of news stories and market activities might not be associated, the news

events like: dividends disclosure, bonus and right announcements, financial disclosure,

etc might have effect on stock returns. But, what are the evidences available for these

variables are presented in this sub-section which includes the major studies during 1992

to 2009. The review of major studies during the period and its key contributions are

presented in Table 2.17 as follows:

One striking characteristic of the stock market is that the volatility of returns can be very

different at different times; daily volatility also fluctuates, and can change very rapidly. It

seems plausible that changes in volatility may have important effects on required stock

returns, and thus on the level of stock prices. Campbell and Hentschel (1992) analyzed

the volatility feedback mechanism or ‘no news is good news’ by modifying the GARCH

model of returns to allow for volatility feedback effect. The study emphasized all large

pieces of news have a negative volatility effect; conversely, all small pieces of news have

a positive volatility effect. The arrival of a small piece of news lowers future expected

Table 2.17: Review of major studies on news effect on stock returnsStudy Major findingsCampbell and Hentschel (1992)

The study concluded that volatility feedback contributes little to the unconditional variance of returns thus much of the variance of stock is in fact due to other changes in expected excess returns, and not to news about future dividends.

Mitchell and The study concluded the number of news stories and market activity are directly related.

72

Mulherin (1994) Maheu and McCurdy (2004)

The study interprets the innovation to returns, which is directly measurable from price data, as the news impact from latent news innovations. The latent news process is postulated to have two separate components, normal news and unusual news events, which have different impacts on returns and expected volatility for individual stocks.

Boyd, et.al (2005)

On average, the stock market responds positively to news of rising unemployment in expansions and negatively in contractions. Since, the economy is usually in an expansion phase, it follows that the stock market usually rises on the announcement of bad news from the labor market.

Zhang (2006) Greater information uncertainty produce relatively higher expected returns following good news and relatively lower expected returns following bad news.

Hirshleifer, et.al (2009)

The univariate and multivariate tests provide statistically significant evidence that high-news days are associated with a lower sensitivity of announcement abnormal returns to earnings news, a higher sensitivity of post-announcement abnormal returns to earnings news, and a lower trading volume response to earnings news.

volatility and increases the stock price. In the extreme case where no news arrives, the

market raises because ‘no news is good news’. Volatility feedback therefore implies that

stock price movements will be correlated with future volatility. The resulting model is

asymmetric because volatility feedback amplifies large negative stock returns and

dampens large positive returns. The model also implies that volatility feedback is more

important when volatility is high. In US monthly and daily data in the period 1926-1988,

the asymmetric model fits the data better than the standard GARCH model, accounting

for almost half the skewness and excess kurtosis of standard monthly GARCH residuals.

Estimated volatility discounts on the stock market range from 1 percent in normal times

to 13 percent after the stock market crash of October 1987 and 25 percent in early 1930s.

However volatility feedback has little effect on the unconditional variance of stock

returns. The study also explained that the basic problem is that stock returns are

determined endogenously in general equilibrium, one cannot explain the behavior of

stock returns in economic terms by applying a statistical model directly to returns; and it

is further feature that volatility feedback is more important when volatility is high than

when volatility is low. In sum, the study concluded that volatility feedback contributes

little to the unconditional variance of returns thus much of the variance of stock is in fact

due to other changes in expected excess returns, and not to news about future dividends.

Whether the amount of information that is publicly reported affects the trading activity

and the price movements in securities markets is the capital market issue. Mitchell and

Mulherin (1994) focused on the relationship between the numbers of news

announcements reported daily by Dow Jones & Company and aggregate measures of

securities market activity including trading volume and market returns. The study found

73

that the number of Dow Jones announcements and market activity are directly related.

The results are robust to the addition of factors previously found to influence financial

markets such as day-of-the-week dummy variables, news importance as proxy by large

New York Times headlines and major macroeconomic announcements, and non-

information sources of market activity as measured by dividend capture and triple

witching trading. However, the observed relationship between news and market activity is

not particularly strong and the patterns in news announcements do not explain the day-of-

the-week seasonality in market activity. The analysis of the Dow Jones database

confirmed the difficulty of linking volume and volatility to observed measures of

information. The data cover 2,011 business days during 1983 to 1990. Using the time

series pattern analysis, correlation and the regression, the study concluded that the

number of news stories and market activity are directly related.

There is a wide-spread perception in the financial press that volatility of asset returns has

been changing. The new economy is introducing more uncertainty. Indeed, it can be

argued that volatility is being transferred from the economy at large into the financial

markets, which bear the necessary adjustment shocks. In order to assess the empirical

validity of the perception and to investigate the sources and characteristics of changing

volatility dynamics on many important financial and economic decisions, Maheu and

McCurdy (2004) modeled the components of the return distribution, which are assumed

to be directed by a latent news process. The study interprets the innovation to returns,

which is directly measurable from price data, as the news impact from latent news

innovations. The latent news process is postulated to have two separate components,

normal news and unusual news events, which have different impacts on returns and

expected volatility for individual stocks. Normal news innovations are assumed to cause

smoothly evolving changes in the conditional variance of returns where as the unusual

news process causes infrequent large moves in returns. The study selected the random

sample of 11 US firms, daily price data for the randomly chosen firms that fit the sample

criteria were obtained from the CRSP database at the end of December, 2000 and used

three indices – DJIA, Nasdaq 100 and CBOE Technology Index (TXX). GARCH-jump

model with autoregressive jump intensity (GARJI) is applied for the analysis. The

conditional variance of stock returns is a combination of jumps and smoothly changing

components. A heterogeneous Poisson process with a time-varying conditional intensity

parameter governs the likelihood of jumps. Unlike typical jump models with stochastic

volatility, previous realizations of both jump and normal innovations can feedback

74

asymmetrically into expected volatility. The study developed the model which improve

the forecasts of volatility, particularly after large changes in stock returns and provide the

empirical evidence of the impact and feedback effects of jumps versus normal return

innovations, leverage effects, and the time-series dynamics of jump clustering.

In short-run response of stock prices to the arrival of macroeconomic news – the

announcement of the unemployment rate, Boyd, et.al (2005) presented the stock market’s

response to unemployment news arrival depends on whether the economy is expanding or

contracting. On average, the stock market responds positively to news of rising

unemployment in expansions and negatively in contractions. Since, the economy is

usually in an expansion phase, it follows that the stock market usually rises on the

announcement of bad news from the labor market. The monthly unemployment

announcements database are used for the study and it cover the period from February

1957 to December, 2000. The regression models are used to analyze the stock and bond

price responses to unemployment news. The explanation of the findings - the seemingly

odd pattern of stock price responses are: Conceptually, three primitive factors determine

stock prices – the risk-free rate of interest, the expected rate of growth of earnings and

dividends or the growth expectations, and the equity risk premium. Thus, if

unemployment news has an effect on stock prices, that must be because it conveys

information about one or more of these primitives.

The study documented the relationship between information uncertainty and stock

returns. Based on the substantial evidence of short-term stock price continuation, the prior

literature often attributes to investor behavioral biases such as underreaction to new

information. Zhang (2006) investigated the role of information uncertainty in price

continuation anomalies and cross-sectional variations in stock returns using sample data

from three sources. Returns from the CRSP monthly stocks which include NYSE,

AMEX, and NASDAQ stocks, book value and other financial data are from Compustat,

analyst forecast revisions are from IBES. The sample period spans from January 1983 to

December 2001. Since, Fama and French three-factor model does not capture the

momentum effect, the study used a four-factor model to test portfolio returns. The general

market reaction principle is good news predicts relatively higher future returns and bad

news predicts relatively lower future returns. If short-term price continuation is due to

investor behavioral biases, there should be greater price drift when there is greater

information uncertainty. Specifically, the study focused on two price continuation

anomalies: post-analyst forecast revision price drift and price momentum, using ex-post

75

returns as a proxy for expected returns, the analysis found consistent results across six

proxies for information uncertainty: firm size, firm age, analyst coverage, dispersion in

analyst forecasts, return volatility, and cash flow volatility. For each of the six proxies,

greater information uncertainty leads to relatively lower future stock returns following

bad news and relatively higher future returns following good news, suggested that

uncertainty delays the flow of information into stock prices. In sum, the study concluded

that greater information uncertainty should produce relatively higher expected returns

following good news and relatively lower expected returns following bad news.

Recent financial literature proposes that limited investor attention causes market

underreactions, the explanation of underreaction is that investors with limited attention

neglect newly arriving information signals. Hirshleifer, et.al (2009) examined this

explanation by measuring the information load faced by investors. The study provides

new insight into the validity of the attention hypothesis by testing directly whether

extraneous news distracts investors, causing market prices to underreact to relevant news.

The investor distraction hypothesis, which holds that the arrival of extraneous earnings

news causes trading volume and market prices to react sluggishly to relevant news about

a firm. Specifically, the study examined how the number of earnings announcements by

other firms affects a firm’s volume, announcement period return, and post-event return

reactions to an earnings surprise. Using the quarterly earnings announcement data from

the CRSP-Compustat merged database and IBES from 1995 to 2004, the study found that

the immediate price and volume reaction to a firm’s earnings surprise is much weaker,

and post-announcement drift much stronger, when a greater number of same-day earnings

announcements are made by other firms. The Industry-unrelated news and large earnings

surprises have a stronger distracting effect. These findings are consistent with the investor

distraction hypothesis. In sum, univariate and multivariate tests provide statistically

significant evidence that high-news days are associated with a lower sensitivity of

announcement abnormal returns to earnings news, a higher sensitivity of post-

announcement abnormal returns to earnings news, and a lower trading volume response

to earnings news.

i) Major studies related to investors overconfidence

During 1987 to 2004, the significant numbers of studies are available related to investor

overconfidence. Some of the major studies have been presented in this part which

includes the overreaction hypothesis, earnings hypothesis, overreaction and decision

76

making, overreaction and trading volume, etc. These studies are classified into three sub-

section based the suitable time frame as below.

i) Major studies related to investors overconfidence before 2000

Table 2.18 organized the major studies and its key findings in 2000 which is presented as

follows:

Table 2.18: Review of major studies on investor overconfidence on stock returns in 2000

Study Major findingsDeBondt and Thaler (1987)

Reconfirmed the overreaction hypothesis i.e. systematic price reversals for stocks that experience extreme long-term gains or losses and, excess returns in January are related to past performance.

Zarowin (1989) The study fails to support the overreaction to earnings hypothesis and concluded the winner-loser effect is primarily a size effect.

Russo and Schoemaker (1992)

The study examines the costs, causes, and remedies for overconfidence and acknowledged that although overconfidence distorts decision making, it can serve a purpose during decision implementation. The overconfidence has remained a hidden flaw in managerial decision making.

Daniel, et.al (1998)

Overconfidence implies negative long-lag autocorrelations, excess volatility, and, when managerial actions are correlated with stock mispricing, public-event-based return predictability.

Odean (1998) Overconfidence increases trading volume and market depth, but decreases the expected utility of overconfident traders.

Camerer and Lovallo (1999)

While analyzing whether optimistic biases could plausibly and predictably influence economic behavior in one particular setting, on undergraduates and MBA graduates. The study concluded that the subjects are simply overconfident; and the inside view which creates that confidence leads them to neglect the quality of their competition.

Hong and Stein (1999)

Each news watcher observes some private information, but failed to extract other news watchers’ information from prices. If information diffuses gradually across the population, prices underreact in the short run but they can only implement simple strategies, their attempts at arbitrage must inevitably lead to overreaction at long horizons.

The study titled Further Evidence On Investor Overreaction and Stock Market

Seasonality, a study made by DeBondt and Thaler (1987) which support the findings of

DeBondt and Thaler (1985) which are systematic price reversals for stocks that

experience extreme long-term gains or losses: Past losers significantly outperform past

winners. The study is based on the major issues regarding the "winner-loser" effect

pertained in the previous work are: first, there is a pronounced seasonality in the "price

correction." Almost all of it occurs in the successive months of January, especially for

the losers. Second, the correction appears to be asymmetric: after the date of portfolio

formation, losers win approximately three times the amount that winners lose. Third, the

characteristics of the firms in the extreme portfolios are not fully described. Finally, the

interpretation of the results as evidence of investor overreaction has been questioned.

Thus, the objectives of the study are: to re-evaluate the overreaction hypothesis and using

the same data set; and, to investigate the hypothesis that the winner-loser effect can be

77

explained by changes in CAPM-betas. The analysis is framed to support the most curious

results - the strong seasonality in the test period returns of winners and losers and a large

portion of the excess returns occurs in January. The study further explores some issues:

first, are there any seasonal patterns in returns during the formation period? Next, within

the extreme portfolios, do systematic price reversals occur throughout the year, or do they

occur only in January? Finally, are the January corrections driven by recent share price

movements or by more long-term factors? Are losing firms particularly small? Are small

firms for the most part losers? Are there any additional excess returns genuinely

attributable to company size when size is measured in a way that is independent of short-

term price movements? Can the study use accounting data to distinguish the overreaction

hypothesis from other explanations of the winner-loser effect? To answer these and other

questions, the study employed the NYSE listed CRSP monthly return data set from 1926

to1982. Calculations of cumulative excess return are made, then ranked and formed 48

portfolios each for winner and loser. The 50 stocks with the highest are assigned to a

winner portfolio while the 50 stocks with the lowest cumulative excess return assigned to

a loser portfolio. Descriptive Statistics, OLS Regression Analysis, Correlation Analysis,

CAPM, Friedman Two-Way Analysis of Variance (chi-square) are used for the analysis.

The principal findings of the study are: excess returns for losers in the test period (and

particularly in January) are negatively related to both long-term and short-term formation

period performance. The winner-loser effect cannot be attributed to changes in risk as

measured by CAPM-betas. The winner-loser effect is not primarily a size effect. The

small firm effect is partly a losing firm effect, but even if the losing firm effect is

removed, there are still excess returns to small firms and the earnings of winning and

losing firms show reversal patterns that are consistent with overreaction. Thus, the

additional evidence that supports the overreaction hypothesis and the seasonal pattern of

returns is also examined where excess returns in January are related to both short-term

and long-term past performance, as well as to the previous year market returns.

Zarowin (1989) on market overreact to corporate earnings information evaluated whether

the stock market overreacts to extreme – good and bad, earnings, by examining firm’s

stock returns over the 36 months subsequent to extreme earnings years. Consistent with

the overreaction hypothesis, stock returns of the poorest earners outperform those of the

best earners. While the poorest earners do outperform the best earners, the poorest earners

are also significantly smaller than the best earners. When poor earners are matched with

good earners of equal size, there is little evidence of differential performance. The basic

78

data analysis strategy of the study is to form portfolios of firms that are characterized by

extreme good vs. bad, current period earnings performance and to compare the

subsequent stock returns of the poorest earners versus the best earners. The CRSP

monthly return file and the Compustat annual industrial file are the sources of the data,

from 1971 to 1981. Stock returns and firm size are computed for the analysis. Firm’s

excess return with market, average excess returns, cumulative average excess return

(CAR), regression analysis and pair analysis are employed. The findings of the study fail

to support the overreaction to earnings hypothesis. The statistically significant differences

between the returns of extreme prior period performers appear to be the result not of

investor overreaction to earnings but of the size effect. The conclusions contrast with

those of DeBondt and Thaler (1987), who maintain, the winner-loser effect is not

primarily a size effect. Thus, the study suggests that size, and not tendency for prior

period losers to outperform prior period winners in the subsequent period.

“To know that we know what we know and that we do not know what we do not know,

this is true knowledge” (Confusius). The good decision marking requires more than

knowledge of facts, concepts, and relationships. It also requires metaknowledge – an

understanding of the limits of our knowledge. Unfortunately, people tend to have a deeply

rooted overconfidence in their beliefs and judgments. Because metaknowledge is not

recognized or rewarded in practice, nor instilled during formal education, overconfidence

has remained a hidden flaw in managerial decision making. Thus, Russo and Schoemaker

(1992) examined the costs, causes, and remedies for overconfidence and acknowledge

that although overconfidence distorts decision making, it can serve a purpose during

decision implementation.

Based on two well-known psychological bases, firstly, investor overconfidence about the

precision of private information and, secondly, bias of self-attribution which causes

asymmetric shifts in investors’ confidence as a function of their investment outcomes.

Daniel, et.al (1998) proposed a theory of securities market under- and overreactions. The

study is guided by the recent years’ of empirical evidences on security returns has

presented a sharp challenge to the traditional view that securities are rationally priced to

reflect all publicly available information. Some of the more pervasive anomalies can be

classified as; even-based return predictability, public-event-data average stock returns of

the same sign as average subsequent long-run abnormal performance; short-term

momentum, positive short-term autocorrelation of stock returns for individual stocks and

the market as a whole, long-run reversal; negative autocorrelation of short-term returns

79

separated by long lags or overreaction; high-volatility of asset prices relative to

fundamentals; short-run post-earnings announcement stock price “drift” in the direction

indicated by the earnings surprise, but abnormal stock price performance in the opposite

direction of long-term earnings changes. The theory is based on investor overconfidence

and variations in confidence arising from biased self-attribution. The study developed the

basic constant confidence model and analyzed the six propositions and showed that the

overconfidence implies negative long-lag autocorrelations, excess volatility, and, when

managerial actions are correlated with stock mispricing, public-event-based return

predictability. Biased self-attribution adds positive short-lag autocorrelation (momentum),

short-run earnings “drift,” but negative correlation between future returns and long-term

past stock market and accounting performance. The theory is also offered several untested

implications and implications for corporate finance theory.

What happens in financial markets when people are overconfident? Overconfident is a

characteristic of people, not of markets which is costly to society. Overconfident traders

do not share risk optimally, they expend too many resources on information acquisition,

and they trade too much. Odean (1998) also analyzed market models in which investors

are rational in all respects expect how they value information. The major findings of the

study are: trading volume increases when price takers, insiders, or market makers are

overconfident; overconfident traders can cause markets to underreact to the information

of rational traders; leading to positive serially correlated results; overconfidence reduces

traders’ expected utility and overconfident traders hold under diversified portfolios;

overconfidence increases market depth; overconfident insiders improve price quality, but

overconfident price takers worsen it; and overconfident traders increase volatility through

overconfident market markers makers may dampen this effect. When information is

costly, overconfident traders who actively pursue information fare less well than passive

traders. Price-taking traders, who are confident about their ability to interpret publicly

disclosed information, reduce market efficiency; overconfident insiders temporarily

increase it. When there are many overconfident traders, markets tend to underreact to the

information of rational traders. Markets also underreact to abstract, statistical, and highly

relevant information and overreact to salient, but less relevant information. Like those

who populate them, markets are predictable in their biases. Thus, the study concluded that

overconfidence increases trading volume and market depth, but decreases the expected

utility of overconfident traders.

80

Psychological studies show that most people are overconfident about their own relative

abilities, and unreasonably optimistic about their futures (Weinstein, 1980). Camerer and

Lovallo (1999) analyzed whether optimistic biases could plausibly and predictably

influence economic behavior in one particular setting – entry into competitive games or

markets. The study used the plant-level data from the U.S. Census of Manufacturers

spanning 1963-1982, employed the random and the self-selection sample selection

procedures for 8 different experiments on undergraduates and MBA graduates. The

sample constitutes 118 graduates in total. The study reached to different conclusions

about equilibrium predictions when using the skill-based payoffs instead of random

payoffs – they enter more when betting on their skill, which is not to say that the subjects

behave irrationally – indeed, they forecast the number of competitors quite well, and most

pass tests of expectational rationality. The subjects are simply overconfident; and the

inside view which creates that confidence leads them to neglect the quality of their

competition.

Hong and Stein (1999) developed a model which features the two classes of traders, news

watchers and momentum traders. The study shares the same goal which Barberis et.al

(1998) and Daniel et.al (1998) focused i.e. to construct a plausible model that delivers a

unified account of asset-price continuations and reversals. However, taken different

approach, both earlier studies used representative agent models: Barbaris et.al develop a

regime-switching learning model, where investors wind up oscillating between two states

– one where they think that earnings shocks are excessively transitory and one where they

think that earnings shocks are excessively persistent. Daniel et.al (1998) emphasized the

idea that investors are likely to be overconfident in the precision of their private

information, and that this overconfidence will vary over time as they learn about the

accuracy of their past predictions. While the findings of the study is driven by the

externalities that arisen when heterogeneous traders interact with one another. The

proposed model of the study is judged in terms of three criteria: first, it should rest on

assumptions about investor behavior that are either a priori plausible or consistent with

casual observation; second, it should explain the existing evidence in a parsimonious and

unified way, and finally, it should make a number of further predictions which can be

tested and ultimately validated. With these criteria, the study reached to the conclusion

that each news watcher observes some private information, but failed to extract other

news watchers’ information from prices. If information diffuses gradually across the

population, prices underreact in the short run. The underreaction means that the

81

momentum traders can profit by trend chasing. However, if they can only implement

simple strategies, their attempts at arbitrage must inevitably lead to overreaction at long

horizons.

iii) Review of major studies on investor overconfidence 2000 onwards

The studies on investor overreaction 2000 onwards covered the issues: overconfidence

and assets pricing, overconfidence and trading volume, the survival of pessimism and

optimism in the financial market, gender based overconfidence, etc. Table 2.19 presents

some of the prominent studies and their major findings as follows:

Daniel and Titman (2000) examined why investors are likely to be overconfident and how

this behavioral bias affects investment decisions. The analysis suggested that investor

overconfidence can potentially generate stock return momentum and that this momentum

effect is likely to be the strongest in those stocks whose valuation requires the

interpretation of ambiguous information. Consistent with this, study found that

momentum effects are stronger for growth stocks than value stocks. A portfolio strategy

based on this hypothesis generates strong abnormal returns that do not appear to be

attributable to risk. Although these results violate the traditional efficient markets

hypothesis, they do not necessarily imply that rational but uniformed investors, without

the benefit of hindsight, could have actually achieved the returns. Authors argued that to

examine whether unexploited profit opportunities exist, one must test for what is called

adaptive-efficiency, which is a somewhat weaker form of market efficiency that allows

for the appearance of profit opportunities in historical data, but requires these profit

opportunities to dissipate when they become apparent. In conclusion, the study rejected

the notion of adaptive-efficiency in favor of an alternative theory which suggests that

asset prices are influenced by investor overconfidence.

Table 2.19: Review of major studies on investors overconfidence 2000 onwardsStudy Major findingsDaniel and Titman (2000)

Asset prices are influenced by investor overconfidence.

Barber and Odean (2000)

Overconfidence leads to excessive trading, individual investors who hold common stocks directly pay a tremendous performance penalty for active trading, the overconfidence can explain high trading levels and resulting poor performance of individual investors. Thus, trading is hazardous to your wealth.

Wang (2001) Under-confidence or pessimism cannot survive in financial market, but moderate overconfidence or optimism can survive and even dominate, particularly when the fundamental risk is large.

Barber and Odean (2001)

The study as per the prediction of theoretical models - men are more prone to overconfidence than women, particularly so in male-dominated realms such as finance.

82

Gervais and Odean (2001)

The study developed a multi-period market model, its contribution is explained as: a trader in this model initially does not know this own ability. He infers this ability from his successes and failures. In assessing his ability, the trader takes too much credit for his success. This leads him to become overconfident. A trader’s expected level of overconfidence increases in the early stages of this career. Then, with more experience, he comes to better recognize his own ability.

Jiang et.al. (2004)

High information uncertainty (IU) exacerbates investor overconfidence and limits rational arbitrage.

To shed light on the investment performance of common stocks held directly by

households, Barber and Odean (2000) analyzed a unique data set that consists of position

statements and trading activity for 78000 households at a large discount brokerage firm

over a six year period ending in January 1997. The empirical findings of the study are:

overconfidence leads to excessive trading, individual investors who hold common stocks

directly pay a tremendous performance penalty for active trading, the overconfidence can

explain high trading levels and resulting poor performance of individual investors. Thus,

with the supporting Benjamin Graham’s statement – “the investor’s chief problem and

even his worst enemy is likely to be himself,” the study concluded that trading is

hazardous to your wealth.

The survival of non-rational investors in an evolutionary game model with a population

dynamic for a large economy is analyzed by the study. The dynamic indicated that the

growth rate of wealth accumulation drives the evolutionary process. The study focuses

the analysis on the survival of overconfidence and investor sentiment and found that

under-confidence or pessimism cannot survive, but moderate overconfidence or

optimism can survive and even dominate, particularly when the fundamental risk is large.

Thus, the findings of Wang (2001) provided that new empirical implications for the

survivability of active fund management. The study results lend support to the relevance

of the psychology of investors in studying financial markets.

The study on gender, overconfidence and common stock investment, Barber and Odean

(2001) analyzed the trading behavior of boys and girls. Theoretical models predict that

overconfident investors trade excessively. Psychological research demonstrates that, in

areas such as finance, men are more overconfident than women, this difference in

overconfidence yields two predictions: men will trade more than women, and the

performance of men will be hurt more by excessive trading than the performance of

women. The study tests this prediction by partitioning investors on gender. Thus, theory

predicts that men will trade more excessively than women. Using account data for over

35,000 households from a large discount brokerage, the study analyze the common stock

83

investments of men and women from February 1991 through January 1997. Consistent

with the predictions of the overconfidence models, the study document that men trade 45

percent more than women; the excess trading reduces men’s net returns by 2.65

percentage points a year as opposed to 1.72 percentage points for women. While both

men and women reduce their net returns through trading, men do so by 0.94 percentage

points more a year than do women. The differences in turnover and return performance

are even more pronounced between single men and single women. Single men trade 67

percent more than single women thereby reducing their returns by 1.44 percentage points

per year more than do single women. Thus, the study as per the prediction of theoretical

models - men are more prone to overconfidence than women, particularly so in male-

dominated realms such as finance. Overconfident investors overestimate the precision of

their information and thereby the expected gains of trading. They may even trade when

the true expected net gains are negative, provided the strong support for the behavioral

finance model. Men trade more than women and thereby reduce their returns more so

than do women. Furthermore, these differences are most pronounced between single men

and single women.

It is a common feature of human existence that constantly learns about our own abilities

by observing the consequences of our actions. For most people there is an attribution bias

to the learning: the overestimation of own success. Gervais and Odean (2001) developed

a multi-period market model describing both the process by which traders learn about

their ability and how a bias in this learning can create overconfident traders. A trader in

this model initially does not know this own ability. A trader infers this ability from his

successes and failures. In assessing his ability, the trader takes too much credit for his

success. This leads him to become overconfident. A trader’s expected level of

overconfidence increases in the early stages of this career. Then, with more experience, a

trader comes to better recognize his own ability. Thus, the patterns in trading volume,

expected profits, price volatility, and expected prices resulting from this endogenous

overconfidence are analyzed.

The role of information uncertainty (IU) in predicting cross-sectional stock returns is

examined by Jiang, et.al (2004). IU is defined in terms of value ambiguity or the

precision with which firm value can be estimated by knowledgeable investors at

reasonable cost. The study used different proxies for IU and found that on average, high-

IU firms earn lower future returns i.e. the mean effect and, price and earnings momentum

84

effects are much stronger among high-IU firms i.e. the interaction effect. Thus, the study

concluded that high-IU exacerbates investor overconfidence and limits rational arbitrage.

2.3 Review of major studies in Nepalese context

The history of stock exchange in developed economies has about half a century but it is a

novel practice in most of the developing and transitional economics including Nepal. It is

the general understanding that the maturity of financial market and its participants help to

grow the market substantially. Being the new practice of stock trading in Nepalese stock

market, the quantity of systematic studies seems quite low as well. The major studies on

market information and stock return with its major contributions for the Nepalese Finance

is presented in Table 2.20 as below.

The study examined the relationship of market equity, market-to-book value, price

earnings and dividends with liquidity, profitability, leverage, assets turnover and interest

coverage ratio carried out by Pradhan (1993). The study is based on 55 observations for

the period 1986 to 1990. The sample includes 17 listed companies of NEPSE. Using

linear regression and portfolio analysis to examine the relationship among the variables,

the study revealed that there is positive relationship between market equity and price

earnings ratio, and the negative relationship of market-to-book value with liquidity,

profitability and dividends. Thus, the major finding of the study is the positive

relationship between stock returns and size whereas inverse relation between stock

returns and market-to-book value.

Table 2.20: Review of major studies in Nepalese context

Study Major findings

Pradhan (1993) The positive relation between stock returns and size where as inverse relation between returns and market-to-book value.

Pradhan and Balampaki (2004)

Stock returns is positively related with earning yield and size, where as negatively related to book-to-market ratio and cash flow yield and among the others, book-to-market value was found to be more informative.

Baskota (2007) There is no persistence of volatility in Nepalese stock market and the stock price movements are not explained by the macro-economic variables.

Prasai (2010) The study documented a significant positive relationship between size and stock returns and a significant negative relationship between book to market equity and stock returns.

The study examined the effect of fundamental variables on stock returns and employed

the pooled cross-sectional data of 40 enterprises listed in NEPSE, the only stock exchange

in Nepal. Total 139 observations were collected for the period 1995/96 to 1999/00. The

regression models which explain the stock returns on fundamental variables such as

85

earnings yield, size, book-to-market equity and cash flow yield is employed for the

analysis. The result shows that earnings yield and cash flow yield have significant

positive impact on stock returns and insignificant impact of book-to-market value, size

has a negative impact on stock returns. Among the other findings, the major contribution

of Pradhan and Balampaki (2004) is documented as: the positive relationship of stock

returns with earning yield and size, whereas negative relationship to book-to-market ratio

and cash flow yield and, book-to-market value is found to be more informative.

The impact of trading days, trading volumes, money supply, interest rates, inflation, and

industrial production on the stock returns is analyzed by Baskota (2007). The study is

based on the data collected for the period 1994 to 2006 of NEPSE. There is no persistence

of volatility in Nepalese stock market and the stock prices movements are not explained

by the macroeconomic variables are the findings of the study. Using the event analysis

approach, the study conducted that the political events are not only the factors that

explain the movements in NEPSE.

Prasai (2010) analyzed the fundamental measures and macroeconomic variables and its

influences on stock returns. The sample size for the study is of 48 enterprises listed in

NEPSE whereas total 276 observations were collected. The analysis covered six years

starting from 2000/01 and end with 2006/07. The findings of the study are: a significant

but unexpected positive relationship between size and stock returns; On the other hand,

the study revealed a significant negative relationship between book-to-market equity and

stock returns; while earnings yield and cash flow yield are found to have no predictive

power. The study also examined the individual effect of macroeconomic variables -

interest rate, exchange rate, inflation and money supply and concluded that interest rate

and inflation have the significant explanatory power for stock market movements.

2.4 Concluding Remarks

The stock market movement is one of the most popular areas in finance. The French

mathematician, Louis Jean-Baptiste Alphonse Bachelier is credited with being the first

person to formulate the stochastic model or the random process. Bachelier (1900) the

seminal work is now called Brownian motion. Brownian motion is the presumably

random drifting of particles suspended in a fluid or the mathematical model used to

describe such random movements, which is often called a particle theory. The

mathematical model of Brownian has several real-world applications. An often quoted

example is stock market fluctuations. However, the movements in share prices may arise

86

due to unforeseen events which do not repeat themselves. The Bachelier’s study primarily

focused on; what is the probability that a certain market price be attained before a certain

date? And, the application of probability theory to the stock exchange. The influences for

price movements are innumerable – the past, current and even anticipated events that

often have no obvious connection with its changes might influence the prices. Apart from

the natural variations, some artificial causes might also intervene for price movements.

The stock price movements depend upon infinite number of factors. Thus, it is almost

impossible to predict the market prices accurately using the econometric and the

mathematical modeling. For instance, at the same time, some buyers believe an increase

in the prices whereas sellers trust a decrease. Therefore, it is just an imagination that one

can win with certainty in the stock market. Even if such happened, the combination will

not be persistent because the buyer believes in a probable rise, otherwise he would not

buy, but if he buys, it is because someone sells to him, whereas and the seller obviously

believes in a probable decline. With these explanation, it is logical to state that the

dynamics of the stock price movements is never be an exact science, but it is possible to

study mathematically and with the application of econometric model given that the static

state of the market at a given point of time.

Bachelier (1900) which laid the foundation of stock price predictability and the pioneer

work by Markowitz (1952), the portfolio theory, provided the basis for individual

investors to allocate their resources with due consideration of risk and return tradeoff.

Further, the portfolio theory extended to CAMP which explains the individual stock co-

movements with the overall markets that determine the performance of the stock or the

expected returns which helps to forecast the stock prices. Specially, after the evolution of

CAPM in 1960s, many studies have been carried out to determine the factors affecting the

stock returns. But, the review of major studies suggests that there is lack of consensus on

a single model, methodology and the process of determining the stock returns. For

instance, some evidences shows that stock returns is divided into selectivity and risk

factors whereas others proved that the changes in expected future dividends or expected

future returns leads the stock prices; the firm specific fundamental measures are the

sources of stock returns; the intangible components are the prime causes of stock returns;

the behavioral issues dictates the stock prices; the stock market itself determine its future;

among others, are the major areas of market information and stock returns which have

been continuously contributing for the stock price movements. These empirical evidences

clearly postulated that there are multiple factors that have been supplying variations in

87

stock returns. So that, there is absence of consensus among the existing evidences

regarding the single area of the study, methodology, tools and techniques which clearly

indicates the importance of further studies in this area of Finance.

Stock returns forecasting being the central issues in Finance, numerous studies have tried

to find the most reliable model, tool and technique that explain the majority of variability

of stock returns. To identify such variations, multiple qualitative and quantitative

techniques occupy the major pie of the previous studies. Some studies prioritized the firm

level accounting variables whereas many others documented other variables such as

investor behavior, market behavior, media and political effects, etc. The effects of

individual investor behavior in stock returns have been documented by Lakonishok, et.al

(1994), Ikenberry et al. (1995), Barberis, et.al (1998), Klibanoff, et.al (1998), Odean

(1999), Chan (2003), Biais et.al. (2005), Barber and Odean (2008), Kaniel, et.al (2008),

Foucault, et.al (2011), Sun and Wei (2011) and Doskeland and Hvide (2011), among

others. The perception of individuals and the enterprises have been documented by

Loughran and Ritter (1995), Armen et.al. (2001), and, Brau and Fawcett (2006) among

others. Many academicians and the practitioners still believe on trends and the time bound

cycles that stock market assumed to be followed. Contrary, some others trust that stock

market exhibit the random walk behavior though both ideas have been supported by the

empirical evidences. Regarding the market behavior, some of the major studies are Fama

(1965), French (1980), Brown and Warner (1985), Ritter (1988), Schwert (1989), De

Long, et al (1990), Hasbrouck (1991), Jegadeesh and Titman (1993), Chan, et.al (2001),

Hirshleifer (2001), and, Baker and Wurgler (2002), among others.

On top of the stated intangible variables, the risk mismeasurement, credit rating, trading

halts, analyst’s coverage, and alike also have been fueling for the stock market

movements. Some major studies on intangible effects are Bernard and Thomas (1989),

Goh and Ederington (1993), Lee, et.al (1994), Brennan and Subrahmanyam (1995),

Hong, et.al (2000), Tumarkin and Whitelaw (2001), Conrad, et.al (2002), Vega (2006),

Worthington (2006), Epstein and Schneider (2008), and Hertzberg, et.al (2010), among

others. These studies also incorporated the internet message board effects, news effects,

corporate policy, and political effects on stock returns. These studies provided some

insight that there are some other dimensions in the stock market that have been

consistently providing information about the future movements so that these variables can

also be incorporated as an important factor for stock returns. For instance, On media

coverage, Merton (1987) documented the media coverage, public relations and other

88

investor marketing activities could play an important causal role in creating and

sustaining speculative bubbles and fads among investors; Tetlock (2007) revealed the

three primary contributions, firstly, high levels of media pessimism robustly predict

downward pressure on market prices, followed by high or low values of media pessimism

forecast high market trading volume and finally, low market returns lead to high media

pessimism. Similarly, high-media coverage stocks earn lower returns (Fang and Peress,

2009) and Engelberg and Parsons (2011) showed that the presence or absence of local

media coverage is strongly related to the probability and magnitude of local trading. On

news effect - Campbell and Hentschel (1992) depicted that much of the variance of stock

returns is in fact due to other changes in expected excess returns, and not to news about

future dividends; the number of news stories and market activity are directly related

(Mitchell and Mulherin, 1994), among others. On investor overreaction and underreaction

- the systematic price reversals for stocks that experience extreme long-term gains or

losses and, excess returns in January are related to past performance (DeBondt and

Thaler, 1987); contrarily to the above findings, Zarowin (1989) failed to support the

overreaction to earnings hypothesis and concluded the winner-loser effect is primarily a

size effect. Russo and Schoemaker (1992) showed that the overconfidence has remained a

hidden flaw in managerial decision making; Daniel, et.al (1998) described overconfidence

implies negative long-lag autocorrelations, excess volatility, and, when managerial

actions are correlated with stock mispricing, public-event-based return predictability;

overconfidence increases trading volume and market depth, but decreases the expected

utility of overconfident traders documented by Odean (1998); and, Hong and Stein (1999)

showed each news watcher observes some private information, but failed to extract other

news watchers’ information from prices; among others before 2000. Daniel and Titman

(2000) documented that asset prices are influenced by investor overconfidence; trading is

hazardous to your wealth (Barber and Odean, 2000); Under-confidence or pessimism

cannot survive in financial market, but moderate overconfidence or optimism can survive

and even dominate, particularly when the fundamental risk is large (Wang, 2001); Barber

and Odean (2001) documented that men are more prone to overconfidence than women,

particularly so in male-dominated realms such as Finance; A trader’s expected level of

overconfidence increases in the early stages of this career (Gervais and Odean, 2001);

and, high information uncertainty exacerbates investor overconfidence and limits rational

arbitrage (Jiang et.al, 2004), among others.

89

Apart from the voluminous studies in the developed and western economy, limited

studies were conducted in local context. Some of the major studies include the positive

relation between stock returns and size where as inverse relation between returns and

market-to-book value by Pradhan (1993). Stock returns is positively related with earning

yield and size, where as negatively related to book-to-market ratio and cash flow yield

and among the others, book-to-market value is found to be more informative by Pradhan

and Balampaki (2004), Baskota (2007) revealed that there is no persistence of volatility in

Nepalese stock market and the stock price movements are not explained by the macro-

economic variables, Prasai (2010) documented a significant positive relationship between

size and stock returns and a significant negative relationship between book to market

equity and stock returns, among others.

Thus, the review of previous studies provides sufficient evidences on the controversy

among the existing studies for explaining stock returns. Such contradictions proved that

there is lack of consensus on single model, tool and technique or justify the need for

further studies in this area. Similarly, the impact of market information on stock returns is

inconclusive. More specially, in local context, the study on the market information and

stock returns is most probably a novel phenomenon. In addition, the concurrent

development of Nepalese financial sector and the gradual expansion of the economy

along with its sophistications deserve the study.

Chapter 3

RESEARCH METHODOLOGY

90

This chapter is organized into five sections. First section deals with the brief description

of research design, second section describes the nature and sources of the data and

selection of sample. The detail about the selection of sample enterprises has been

described in section three and section four explains the method of analysis employed for

the interpretation of data. Finally, the limitations of the study have been presented in

section five.

3.1 Research design

The research designs adopted in this study consists of descriptive and causal-comparative

research design.

The descriptive research design is a fact-finding operation searching for adequate

information. It is undertaken in order to ascertain and be able to describe the

characteristics of the variables of interest. It is a type of study, which is generally

conducted to assess the opinions, behaviors, or the characteristics of a given population. It

does not necessarily seek to explain relationships, test hypothesis, make predictions or get

the meanings and implications of a study rather it is a process of accumulating facts. The

descriptive research design is selected for the study to learn the profile of the respondents,

presentation and description of the data collection, and to describe the characteristics of

the investors in the Nepalese stock market.

The causal-comparative research investigates the possible causes affecting a particular

situation by observing existing consequences and searching for the possible factors

leading to the results. This research is also known as ‘ex post facto’ or ‘after the fact’

research (i.e. data are collected after all the events of interest occurred). This is because

both the effect and alleged causes have already occurred. In other words, causal-

comparative research is that research in which the independent variable or variables have

already occurred and in which researcher starts with the observation of the dependent

variable or variables. Then, analyze the independent variables in retrospect for their

possible relations to, and effect on the dependent variable or variables. This research

design is selected for the study to delineate the cause of one or more variables in stock

returns.

3.2 Nature and sources of data

91

The study employed both the secondary and the primary data. The secondary data has

been used to validate the issues of the study. The tests have been carried out by using

annual dataset of listed enterprises of NEPSE until mid-July 2010. The description of the

annual database employed for the study is the period from July 16 th to July 15th because of

the availability of annual reports as per the Nepali calendar. The primary data has been

collected through the structured questionnaire from the Nepalese stock investors.

Secondary data

The secondary data are collected from NEPSE and SEBON database. Further, some of

them are collected directly from the listed enterprises. The major sources of the secondary

data are: the annual reports of security market authorities and the listed enterprises.

Despite the total listed enterprises (176) in NEPSE and 1443 firm year at mid-July 2010,

only the database of 826 firm years are collected (Details are presented in Appendix A)

from 146 enterprises due to its availability. The firm year is defined as the difference

between the mid-July 2010 and listing date of the enterprise. The variables selected for

the analysis are: earnings per share (EPS), market value per share (MPS), cash dividend

in percentage, total common stock outstanding (size), book value per share (BPS), sales

volume or annual deposit (in case of financial institutions), and the cash flow (Details are

presented in Appendix B). Further, to determine the news effects on stock returns, total

1683 news headings related to Nepalese stock exchange are collected for the period

covering January 13, 1994 to July 15, 2010 equivalent to 6029 days from the national

daily “Kantipur” (Details are presented in Appendix C). The selection of the “Kantipur”

daily newspaper is because of its publication was started (Thursday, February 18, 1993)

prior to the establishment of NEPSE (Thursday, January 13, 1994). The similar

approaches used by Lee, et.al (1994) and Tetlock (2007) among others, are employed to

analyze the total news headings and it is categorized into (i) bad news, (ii) good news and

(iii) information (only). The categorization of news heading is based on the content

analysis approach as suggested by Domer (2005) (Details are presented in Appendix D).

Moreover, to find out the leadership (political) effect on stock returns, the details of the

Nepalese prime ministers and their political parties, and the head of state (King’s regime)

during same period have been collected from various records and news sources (Details

are presented in Appendix E). To measure the market returns on the basis of market

information, the annual, monthly and daily NEPSE indices are used for the period

covering July 15, 1994 to July 15, 2010. The annual average index starting from July 16th

and ending at July 15th each year from 1994 to 2010 is used to measure the market rate of

92

returns (Details are presented in Appendix F). Thus, the secondary database for the study

comprises four types of information as: published financial statements of listed

enterprises, daily news related to NEPSE and listed enterprises, the list prime ministers

and their tenures, and finally the daily, monthly and yearly NEPSE indices.

Primary data

A survey was carried out during the month of December 2011 to collect the opinions of

investors in various issues in Nepalese stock market. The stock investors are selected

from different brokers’ floors in Kathmandu. The selection of the brokers’ floor is based

on the random sampling procedure. Out of 39 brokerage firms in Kathmandu valley, 10

were selected as sample brokerage firms (Details are presented in Appendix G). With due

consideration of the behavioral nature of the study, the time to approach to the stock

investors is strictly managed at 12:00 o’clock at the mid-day when stock market open for

trading. The sample size is considered as 364 stock investors, it is because of the

undefined population of stock investors in Nepalese context as suggested by (Cochran,

1977). The structured questionnaires (both in Nepali and English medium) with 36

questions were distributed among the investors. Each questionnaire consist of 7

demographic information, and remaining 29 were related to the stock market and

behavioral issues. The mix of questions comprises - multiple choices, fill in the blanks,

ranking variables (identifying 5 out of 10 variables, and ranking 5 variables), likert items

(with 3 point scale, 4, and 5 point scale), open questions and attitude scale

(agree/disagree) which is designed to grasp the opinion of stock investors in different

issues in Nepalese stock market. The printed questionnaires were provided to the

respondents at each sample brokers’ trading floor in different locations within Kathmandu

valley.

3.3 Selection of enterprises and respondents

The selection of enterprises for the study contains 146 listed enterprises out of total 176

till July 15th, 2010. Though, the focus of the secondary data analysis is towards the study

of the large sample or the total listed enterprises in NEPSE from mid-January 1994 to

mid-July 2010 are treated as large sample suggested by Chan, et.al (1991), Devas, et.al,

(2000), Asness, et.al (2000), Grinblatt and Moskowitz (2004), Kaniel, et.al (2008),

Hertzberg, et.al (2010), and Foucault, et.al (2011) among others. The sample periods vary

from stock to stock but usually run from January 1994 to July 2010. The final date is the

same for all stocks but the initial date varies. The selected enterprises include both

93

manufacturing and non-manufacturing enterprises: insurance companies, financial

institutions, hydropower and trading firms of Nepal Stock Exchange and also the delisted

securities are included. But, some enterprises are not included in this large sample

because of its unavailability of required data.

Table 3.1 presents the sector-wise distribution of listed (NEPSE) enterprises and selected

enterprises for secondary data sources. The figures corresponding to the listed enterprises

show the number of enterprises in each sector of the economy. The listed enterprises are

considered as the population of the study for the secondary data analysis. The figures

corresponding to the population indicate the percentile values. Whereas, in subsequent

row, figures indicates the number of selected enterprises based on the data availability.

The row total constitutes 146 enterprises. The majority enterprises were selected from the

merchant banking and finance sector and about 83 percent of population included for the

study.

Table 3.1: Sector-wise Distribution of Population and Selected Enterprises Sector CB DB MF IC MC OS Total

Listed Enterprises 23 40 61 19 18 15 176

PopulationFigures in percentage

100%13.07 22.73 34.66 10.8 10.23 8.52

SelectionFigures in number

14623 37 59 18 1 8

CB=Commercial banks, DB=Development banks, MF=Merchant Bank & Finance companies, IC=Insurance companies, MC=Manufacturing companies, and OS=Others (Hotels, Hydro, Trading, Telecom & Film)

Source: www.nepalstock.com

The comprehensive details of the selected enterprises with trading symbol, date of listing,

study period and numbers of observations are presented in Appendix H. The overview of

the observation table is shown in Table 3.2.

The majority of the observations are collected from finance companies comprises 45.76

percent followed by commercial banks (21.67 percent). Because of the lack of proper data

availability of Nepalese manufacturing sector, its presence for the study is low which has

only 1.09 percent of total observations. The observable section shows the potential firm

year for secondary data collection which indicates 1443 firm year from 176 enterprises.

The next section explains the observed firm year which is 826 from 146 enterprises. The

proportion indicates the selection of enterprises out of the population, for instance, total

commercial banks are included for the study whereas only 5.56 percent of manufacturing

firms are considered. Similarly, the next column describes the ratio of observed firm

years out of its potentials, for example, 89.93 percent of the potential firm years under

development bank are considered for analysis followed by commercial banks whereas

94

manufacturing firms constitute only about six percent. Finally, the percentage column

exhibits the allocation of total observations into different sector of economy. Thus, the

study is based on 826 observations.

Table 3.2: Overview of sector-wise observations

SN SectorObservable Observed Proportion Percentag

e EnterprisesFirm Yrs

EnterprisesFirm Yrs

Selection Obs.

A Commercial Banks 23 201 23 179 100.00 89.05 21.67B Development Bank 40 139 37 125 92.50 89.93 15.13C Finance Companies 61 486 59 378 96.72 77.78 45.76D Insurance Companies 19 179 18 87 94.74 48.60 10.53E Manufacturing firms 18 265 1 9 5.56 3.40 1.09

FOthers (hydro, hotels, trading, telecom & film)

15 173 8 48 53.33 27.75 5.81

Total 176 1443 146 826 82.95 57.24 100.00Source: www.nepalstock.com

The respondents for the primary data analysis are stock investors in Nepalese stock

market. The investors who have been regularly trade the securities are the target groups of

respondents for the study. The investors were selected based on the selection of the

sample brokerage firms (10 out of 39). The brokers’ floors are considered as the platform

of the investors and those who were presented in the floor during the survey period are

considered as the respondents. Total 364 printed questionnaires were distributed to the

investors during the trading hours (12:00 to 3:00 pm) and collected written the responses.

The Nepali or English medium questionnaires, as per investors’ preference, were

distributed and requested to provide the responses based on their trading experience and

knowledge. The survey was started on 1st December and concluded on 31st December

2011. Total 164 fill up questionnaires were collected thus the response rate is about 45

percent.

3.4 Method of analysis

The study intends to analyze the market information and stock returns, and to determine

the factors affecting investment decision making among others. The market information is

divided into tangible and the intangible components. The tangible information is assumed

as: earnings per share, market value per share, cash dividend in percentage, total common

stock outstanding, book value per share, sales volume or annual deposit, and cash flows.

Similarly, the intangibles are: the news effects – bad news, good news and informational;

the political leadership effects on stock returns. The specification of the econometric

models is the essential steps of data analysis. This section describes the employed

econometric models, the statistical tools and techniques for the study.

95

The methods of data analysis are broadly divided into two subsections – secondary data

analysis and primary data analysis.

Besides the descriptive statistics, correlation matrix analysis, portfolio formation,

regression analysis, Kolmogorov-Smirnov test, stock returns decomposition, the test of

significance of econometric models using t-tests, f-tests, and the detection and correction

of autocorrelation, multicolinearity and heterocedasticity are the major analysis under the

section of the secondary data analysis.

Further, the primary data analysis includes the descriptive statistics of demographic

variables along with percentage, frequency distribution, simple tabular presentation, cross

table analysis, mean score analysis in Likert items, the test of association – chi-square

test, and the factor analysis which includes: Cronbach’s Alfa test, the correlation matrix

analysis, anti-image correlation matrix – the measure of sampling adequacy (MSA),

Kaiser-Meyer-Olkin and Bartlett's Test, the initial and rotated solution for factor analysis,

and the scree plot are used.

Methods of secondary data analysis

The regression equations are formulated to exact the relationship of stock returns on the

tangible and intangible information. The firm specific variables and the market index

variables are treated as dependent and independent in order. The review of literature

suggests that book-to-market equity is a significant independent variable that explains

major variations in stock returns. Thus, book-to-market equity is assumed to be an

essential explanatory variable. Different regression models proposed for the study is

describes as below.

With the description of dependent and independent variables, the basic regression models

for the study are presented as follows:

Model 1: Log (Bit/Mit) = α + b1 BMi0 + b2Δ Bi + b3Δ Mi + ut ………………………………… (3.1)

Log (Bit/Mit) = α + b1 LogBMi0 + b2LogΔ Bi + b3LogΔ Mi + ut …………….. (3.1a)

Priori sign (+) (+) (-)

Where,

Log (Bit/Mit) = log book to market equity of the ith firm at t periods

BMi0 = book to market equity of ith firm at 0 period

Δ Bi = change in book value of ith firm

96

Δ Mi = change in market price of ith firm

ut = random terms

The equation 3.1 decomposes the book-to-market equity which helps to illustrate the

good news effects and bad news effects on stock returns. The error term is added in the

above equation because the given variables do not always hold exactly. The priori sign

for book-to-market equity is positive as suggested by Chan, et.al (1991), The log book-to-

market ratio of the firm at time t can be expressed as its book-to-market ratio at time 0,

plus its change in book value, minus its change in the market value, that is,

Log (Bit/Mit) = BMi0 + Δ Bi - Δ Mi ……………………………………………………………….. (3.1b)

Now it is assume that, a time 0, all firms have the same log book-to-market ratio (bm0),

and that between time 0 and time t, information about the news arrives. Suppose that

some firms receive good news and some firms get the bad news about the ongoing

projects. Consistent with Bernard and Thomas (1989) and Zhang (2006) among others,

the study assume that poor earnings convey sufficiently bad information about the firm’s

future earnings, the market response to the bad earnings news inversely causes the stock

price to fall proportionately more. In other words, ︱ Δ mi ︱ > ︱ Δ bi ︱ resulting an

overall increase in bmi. On the other hand, good news has the opposite effect: change in

book value is positive, but change in market value is more positive, resulting a decrease

in bm. For example, if the book value is Rs 100, market value is Rs 200, then in case of

bad news about the earnings cause the decline in book value by Rs 10 and the market

value by more than decrease in book value, for instance Rs 30 so that the book-to-market

equity increases to 0.53 from 0.50. The same case can be explained in the opposite way

by increase in book value of the stock Rs 10 due to the good news information about the

earnings. The market perceived the good news proportionately more, for instance Rs 30

so that the new book-to-market ratio reaches to 0.48 from the original 0.50. In sum, the

bad news causes book-to-market equity to increase and the good news causes the book-

to-market equity to decrease. Under this interpretation, low bm firms are those that realize

higher earnings than high bm firms.

Model 2: r(t-i,t) = E(t-i)[r(t-i,t)] + rT(t-i,t) + rI

(t-i, t) + ut ……………………………………………… (3.2)

Where,

97

r(t-i,t) = firm level stock returns from t-i to t where i is the lag period

E(t-i)[r(t-i,t)] = the expected returns at t-i, to t, and

rT = returns resulting from tangible information

rI = returns resulting from intangible information

Model 2 in equation 3.2 indicates the decomposition of stock returns into tangible and

intangible components that can be attributable to tangible information and intangible

information from the period t-i to t. More specifically, the realized returns from t-i to t can

be explained by this model. Where E(t-i)[r(t-i,t)] is the expected returns at t-i, to t, and rT and

rI are the unanticipated returns resulting from tangible and intangible information

respectively during the same period.

In terms of BM decomposition, the equation 3.2 can also be expressed by replacing each

component with their proxy variables. If the accounting growth measures are taken as the

tangible information, then the distinction between tangible and intangible returns can be

viewed as a distinction between that portion of a stock’s returns that can be explained by

the accounting growth measures and that portion that is unrelated to those of fundamental

performance measures. With assuming the proxies of tangible and intangible information

– book value to lag book value and market price to lag-market price, respectively. Thus,

the extension of the equation 3.2 into the log-linear model is as below.

Model 3: log [Bt/Pt] = α + b0 log [Bt-i/Pt-i] + b1 [Bt/Bt-i] + b2 [Pt/Pt-i] + ut …………… (3.3)

r(t-i,t) = α + b0 log [Bt-i/Pt-i] + b1 [Bt/Bt-i] + b2 [Pt/Pt-i] + ut ……………..… (3.3a)

r(t-i,t) = α + b0 log [Bt-i/Pt-i] + b1 log [Bt/Bt-i] + b2 log [Pt/Pt-i] + ut ……….. (3.3b)

Priori sign (+) (+) (-)

Where,

log [Bt/Pt] = log book to market equity at time t

log [Bt-i/Pt-i] = lagged log book to market equity at i lag periods

[Bt/Bt-i] = book value per share to lagged book value per share at i lag periods

[Pt/Pt-i] = market price per share to lagged market price per share at i lag

periods

r(t-i,t) = firm level stock returns from t-i to t where i is the lag period. For

instance, 5th lagged period returns can be calculated as: ((MPPS - Lag5_MPPS) /

Lag5_MPPS) + Lag5_CD

The elements of this BM decomposition in equation 3.3 are directly related to those of the

tangible and intangible return decomposition given in equation 3.1. First, log [B t-i/Pt-i]

98

serves as a proxy for the firm’s expected return between t-i to t. The log [Bt/Bt-i] captures

both the anticipated and unanticipated growth in book value from t-i to t. The

unanticipated component of this can be thought of as a proxy for the new tangible

information that arrives between t-i to t, while log [Pt/Pt-i] is equal to log return which

reflect all information, tangible as well as intangible. Similar to equation 3.1, the priori

expected sign for b1 is positive and b2 is negative as suggested by Daniel and Titman

(2006). The study use the log-linear model (log-log or double-log) because, first of all, it

measure the elasticity between the dependent and independent variables; secondly, the log

model transform the observed data into smaller scale because the log which is also known

as logarithm or common log has base 10; whereas by convention, ln means natural

logarithm which has base e (i.e. 2.1718).

Model 4: log [St/Pt] = α+ log [St-i/Pt-i] + rS(t-i,t) + r(t-i,t) + ut ……..………………....... (3.4)

log [St/Pt] = α + b0 log [St-i/Pt-i] + b1 [St/St-i] + b2 [Pt/Pt-i] + ut ……………… (3.4a)

r(t-i,t) = α + b0 log [St-i/Pt-i] + b1 [St/St-i] + b2 [Pt/Pt-i] + ut …………….….… (3.4b)

r(t-i,t) = α + b0 log [St-i/Pt-i] + b1 log [St/St-i] + b2 log [Pt/Pt-i] + ut …..………. (3.4c)

Priori sign (+) (+) (-)

Where,

log [St/Pt] = log sales to price ratio at time t

log [St-i/Pt-i] = log sales to price ratio for the period i to t-i where i is the lag

periods

rS(t-i,t) = sales returns for the period i to t-i where i is the lag periods

r(t-i,t) = firm level stock returns from t-i to t where i is the lag periods

[St/St-i] = sales to lagged sales ratio for the period t-i to t where i is the lag

periods

[Pt/Pt-i] = market price to lagged market price ratio for the period t-i to t

where i is the lag periods

ut = random terms

Similar to the book-to-market decomposition approach, it is also possible to decompose

the other accounting ratios that have been shown to predict stock returns. Equation 3.4

shows the decomposition of sales to price ratio, the log sales to price ratio into log sales to

price at t-i, to t period, log change in sales per unit of share which is viewed as the

another proxy for the tangible return and the next component is firm’s stock return at t-i,

to t period assuming firm returns only through market price of firm stock. The expected

sign of the coefficients are positive, positive and negative respectively for model 4.

99

Model 5: log [Ct/Pt] = α+ log [Ct-i/Pt-i] + rC(t-i,t) + r(t-i,t) + ut …….………………....... (3.5)

log [Ct/Pt] = α + b0 log [Ct-i/Pt-i] + b1 [Ct/Ct-i] + b2 [Pt/Pt-i] + ut …..………… (3.5a)

r(t-i,t) = α + b0 log [Ct-i/Pt-i] + b1 [Ct/Ct-i] + b2 [Pt/Pt-i] + ut …...……….….… (3.5b)

r(t-i,t) = α + b0 log [Ct-i/Pt-i] + b1 log [Ct/Ct-i] + b2 log [Pt/Pt-i] + ut ….………. (3.5c)

Priori sign (+) (+) (-)

Where,

log [Ct/Pt] = log cash flow to price ratio at time t

log [Ct-i/Pt-i] = log cash flow to price ratio for the period i to t-i where i is the lag

periods

rC(t-i,t) = cash flow returns for the period i to t-i where i is the lag periods

r(t-i,t) = firm level stock returns from t-i to t where i is the lag periods

[Ct/Ct-i] = cash flow to lagged cash flow ratio for the period t-i to t where i is

the lag periods

[Pt/Pt-i] = market price to lagged market price ratio for the period t-i to t

where i is the lag periods

ut = random terms

Model 6: log [Et/Pt] = α+ log [Et-i/Pt-i] + rE(t-i,t) + r(t-i,t) + ut ……..………………....... (3.6)

log [Et/Pt] = α + b0 log [Et-i/Pt-i] + b1 [Et/Et-i] + b2 [Pt/Pt-i] + ut …..……….… (3.6a)

r(t-i,t) = α + b0 log [Et-i/Pt-i] + b1 [Et/Et-i] + b2 [Pt/Pt-i] + ut …...……….…..… (3.6b)

r(t-i,t) = α + b0 log [Et-i/Pt-i] + b1 log [Et/Et-i] + b2 log [Pt/Pt-i] + ut ….………. (3.6c)

Priori sign (+) (+) (-)

Where,

log [Et/Pt] = log earnings to price ratio at time t

log [Et-i/Pt-i] = log earnings to price ratio for the period i to t-i where i is the lag

periods

rE(t-i,t) = earnings returns for the period i to t-i where i is the lag periods

r(t-i,t) = firm level stock returns from t-i to t where i is the lag periods

[Et/Et-i] = earnings to lagged earnings ratio for the period t-i to t where i is the lag

periods

[Pt/Pt-i] = market price to lagged market price ratio for the period t-i to t where i is

the lag periods

ut = random terms

Model 7: r(t-i,t) = α + b0 [Bt-i/Pt-i] + b1 [St-i/Pt-i] + b2 [Ct-i/Pt-i] + b3 [Et-i/Pt-i] + ut …… (3.7)

100

Priori sign (+) (+) (+) (+)

Where,

[Bt-i/Pt-i] = book to price ratio for the period i to t-i where i is the lag periods

[St-i/Pt-i] = sales to price ratio for the period i to t-i where i is the lag periods

[Ct-i/Pt-i] = cash flow to price ratio for the period i to t-i where i is the lag

periods

[Et-i/Pt-i] = earnings to price ratio for the period i to t-i where i is the lag

periods

ut = random terms

The regression model 3.7 indicates that the price scaled variables as the independent

variables for the firm level stock return as dependent variable. The model shows the

independent effect of price scaled variables’ effect for the stock returns at ith lag periods.

Model 8: rt = α + b1 B/P(t-i,t) + b2 E/P(t-i,t) + b3 rB(t-i,t) + b4 r(t-i,t) + b5 ι(t-i) + ut ………..(3.8)

Expected sign (+) (+) (-) (-) (-)

Where,

B/P (t-i, t) = book to price ratio for the period i to t-i where i is the lag periods

E/P (t-i, t) = earnings to price ratio for the period i to t-i where i is the lag

periods

rB(t-i,t) = book returns for the period i to t-i where i is the lag periods

r(t-i,t) = firm level stock returns from t-i to t where i is the lag periods

ι(t-i) = composite share issuance variable the period t-i where i is the lag

periods

ut = random terms

The regression model 3.8 is expected to yield the results from a set of Fama-MacBeth

regressions of stock returns on various components of the book-to-market decomposition

with addition of lagged stock return and composite share issuance measures as

explanatory variables. The dependent variable in model 5 is annual firm level log stock

returns and the independent variables are: log book to market ratio at time t, and t-i; log

book return from t-i to t; firm level past log return; and the composite share issuance

variable, respectively. The expected sign as suggested by Daniel and Titman (2006) are:

positive, positive, negative, negative, and negative, respectively for the above mentioned

variables.

101

The calculation of tangible and intangible information can also be possible with the

following regression model. With this model, the calculation of the portion of the stock

returns that cannot be explained by fundamental accounting variables is possible. The

tangible return is the portion of stock return that can be explained by fundamental

variables whereas the unexplained portion as the intangible return. The stock returns

decompose into tangible and intangible information as below.

Model 9: ri(t-i, t) = b0 + b1 B/P (t-i, t) + b2 riB

(t-i,t) + ui,t …………………………….. (3.9)

B/P (t-i, t) = book to price ratio for the period i to t-i where i is the lag periods

rB(t-i,t) = book returns for the period i to t-i where i is the lag periods

r(t-i,t) = firm level stock returns from t-i to t where i is the lag periods

ut = random terms

Where, (b0 + b1 BM (t-i,t)) is the proxy of expected return, (b2 riB

(t-i,t)) is tangible return and

ui,t is the proxy of intangible information. Model 5 and extension of the similar models by

accounting growth measures estimate the coefficients of the parameters so that estimated

tangible and intangible returns can be determined using the following equations.

The firm’s tangible return over certain the time period can also be calculated as;

riT(B)

(t-i,t) = bˆ0 + bˆ1. BP (t-i,t) + bˆ2.riB

(t-i,t) ……….…………………… (3.9a)

and the intangible return is;

r iI(B)

(t-i,t) = uit ………………………………………………………... (3.9b)

The sum of the tangible and intangible returns is equal to the total stock returns.

With the same procedures, using the following regression equations, the total tangible and

intangible returns can be calculated.

Model 10: ri(t-i, t) = y0 + y1B/P (t-i,t) + y2S/P (t-i.t) + y3C/P (t-i,t) + y4E/P(t-i,t) + y5.riB

(t-i,t) +

y6.riS

(t-i, t) + y7.riC

(t-i, t) + y8. riE

(t-i, t) + ui,t …………………………..… (3.10)

Where,

B/P (t-i, t) = book to price ratio for the period i to t-i where i is the lag periods

S/P (t-i, t) = sales to price ratio for the period i to t-i where i is the lag periods

C/P (t-i, t) = cash flow to price ratio for the period i to t-i where i is the lag

periods

102

E/P (t-i, t) = earnings to price ratio for the period i to t-i where i is the lag

periods

rB(t-i,t) = book returns for the period i to t-i where i is the lag periods

rS(t-i,t) = sales returns for the period i to t-i where i is the lag periods

rC(t-i,t) = cash flow returns for the period i to t-i where i is the lag periods

rE(t-i,t) = earnings returns for the period i to t-i where i is the lag periods

ut = random terms

Specifically, in each year, the past return for each enterprise is broken up into three parts,

namely, an expected return (growth) component captured by the lagged accounting

measures, and unanticipated tangible and intangible return components.

The expected returns can be calculated as:

E(ri) = yˆ1B/P (t-i,t) + yˆ2S/P (t-i.t) + yˆ3C/P (t-i,t) + yˆ4E/P(t-i,t) ………………… (3.10a)

Total tangible return is defined as:

riT(Tot)

(t-i,t) = yˆ5. r iB

(t-i,t) + yˆ6. r iS

(t-i,t) + yˆ7. r iC

(t-i,t) + yˆ8. r iE

(t-i,t) ………. (3.10b)

and, the intangible return is defined as:

riI(Tot)

(t-i,t) = ui,t ……………………………………………………………. (3.10c)

Model 11: ri(t) = α + b0 B/P (t-i) + b1 rB(t-i,t) + b2 rI(B) + b3 ι (t-i,t) + ut …………… (3.11)

Where,

ri(t) = firm returns for the period t.

B/P (t-i) = lag period book to market ratio where i is the lag period

rB(t-i,t) = book returns for the period i to t-i where i is the lag periods

rI(B) = Intangible book returns

ι(t-i,t) = composite share issuance variable for the period i to t-i where i is

the lag periods

ut = random terms

The regression model 3.11 is specified to find the independent effect of intangible

variables for the prediction of future returns. The first intangible variable is the variable

which is related to price scaled accounting measures assumed as the proxy of intangible

information, and the next proxy is composite share issuance measure. This model can

103

further be converted in terms of separate accounting growth variables with the similar

procedures.

ri(t) = α + b0 S/P(t-i) + b1 rS(t-i,t) + b2 rI(S) + b3 ι (t-i,t) + ut …………............ (3.11a)

ri(t) = α + b0 C/P(t-i) + b1 rC(t-i,t) + b2 rI(C) + b3 ι (t-i,t) + ut……………….... (3.11b)

ri(t) = α + b0 E/P(t-i) + b1 rE(t-i,t) + b2 rI(E) + b3 ι (t-i,t) + ut……..................... (3.11c)

Where,

r S(t-i,t) = sales returns for the period i to t-i where i is the lag periods

rI(S) = Intangible sales returns

r C(t-i,t) = cash flow returns for the period i to t-i where i is the lag periods

rI(C) = Intangible cash flow returns

r E(t-i,t) = earnings returns for the period i to t-i where i is the lag periods

rI(E) = Intangible earnings returns

ut = random terms

The regression models (3.11, 3.11a, 3.11b, and 3.11c) are designed to find the

independent effect of intangible returns for the prediction of future returns. The price

scaled variables: book-to-market equity, sales to price, cash flows to price and earnings to

price is employed in the above models as the proxy of intangible information

respectively. The next proxy of intangible is composite share issuance measure.

Model 12: rm_ave = α + b0 bXt + b1 gXt + b2 iXt + ui…………...…………………….. (3.12)

rm_midJul = α + b0 bXt + b1 gXt + b2 iXt + ui……….….………………….. (3.12a)

Expected sign (-) (+) (+)Where,

rm_ave = average market returns

bXt = bad news counts

gXt = good news counts

iXt = informational news counts

rm_midJul = end period market returns

ut = random terms

The dependent variable in equation 3.12 is the average market rate of return which is

calculated based on the annual, monthly and daily NEPSE indices whereas Mid-July

market rate of return is the dependent variable in equation 3.11a. The news effects are

104

also analyzed by annually, monthly and daily basis on market returns. The priori sign for

both equations are: negative, positive, and positive for bad news, good news, and

informational news respectively.

Model 13: rm_ave = α + b1D1 + b2D2 + b3D3 + ui …….………………………….. (3.13)

rm_midJul = α + b1D1 + b2D2 + b3D3 + ui …..…….………………….. (3.13a)

The ANOVA model 3.13 and 3.13a shows the relationship between average market

returns and mid-July market returns with dummy variables for political leadership.

Where,

D1 = CPN-UML led government 1, otherwise 0

D2 = Others’ parties led government 1, otherwise 0

D3 = UCPN (M) led government 1, otherwise 0

rm_ave = average market returns

rm_midJul = end period market returns

ut = random terms

Model 14: rm_ave = α + b0 bXt + b1 gXt + b2 iXt + b4D1 + b5D2 + b6D3 + ui ……... (3.14)

rm_midJul = α + b0 bXt + b1 gXt + b2 iXt + b4D1 + b5D2 + b6D3 + ui ……… (3.14a)

Where,

rm_ave = average market returns

bXt = bad news counts

gXt = good news counts

iXt = informational news counts

D1 = CPN-UML led government 1, otherwise 0

D2 = Others’ parties led government 1, otherwise 0

D3 = UCPN (M) led government 1, otherwise 0

rm_midJul = end period market returns

ut = random terms

The ANCOVA model 3.14 and 3.14a indicates the relationship between dependent

variable and the independent variables. Former is the market returns and the latter are:

bad news, good news, informational news, and the dummy variables of political

leadership.

Definition of key terms

105

The study broadly employed the tangible and intangible variables to determine the

relationship between the dependent and independent variables. The basic fundamental

variables are: earnings per share, market value per share, cash dividend, total common

stock outstanding, book value per share, sales volume or annual deposit, and cash flows.

The descriptions of major variables that are employed for the study are as follows:

a. Earnings per share:

It is the indicator of firms’ profitability, which is the ratio of earnings available to equity

holders to the number of common stock outstanding. It can be calculated using the

equation below:

EPS = Earnings

No of common stock outstanding

b. Market value per share:

It is the market price per share which is determined by the free flow of demand and

supply of the equities in the secondary market. The study uses the market value per share

as at end of each year (i.e. July 15th).

c. Cash dividend:

It is the annual rate of returns to shareholders in terms of cash out of annual earnings of

the enterprises and approved by the annual general meeting (AGM) of the board of

directors (BOD).

d. Size:

The market value of the outstanding number of common stock of the enterprises at the

end of July 15th each year is considered as the size of the firm. Market value of equity is

the total market value of common stock outstanding at the end of period t. It has been

calculated based on the market price per share as follows:

MEit = Pit x Nit .............................................................................……… (3.14)

Where, MEit is the market value of equity outstanding of the firm i at the end of year t, Pit

refers the market price per share and Nit refers to the number of common stock

outstanding.

e. Book value per share:

Book value is the outcome of value of common equities divided by the number of

common stock outstanding. The sum of reserve and capital shown in audited balance

sheet is the measure of value of common equities. The book value per share can be

calculated as follows:

106

BVPSValue of common equities

No of common stock outstanding

f. Sales volume:

It is the annual sales revenue of enterprises which representation of sales volume. In case

of financial institutions the annual total deposit is considered as the proxy of sales.

Similarly, for insurance companies the number of issued insurance policies which is

shown in annual reports is considered as sales.

g. Cash flows:

It is the audited annual cash flow of the enterprises during a year which was collected

from the annual report of the enterprises.

h. Book-to-market equity:

The book to market equity is the ratio of book value per share and the market value per

share at the end of fiscal year. It can be calculated as book value of equity divided by

market value of equity as follows:

BE/MEi,t = ………………………………………………….. (3.15)

Where, BE/MEit refers to the ratio of book value of equity to market value of equity of the

ith firm at the end of year t, BEit is the book value of equity of the ith firm at the end of

period t and MEit is the market value of equity of the ith firm at the end of year t.

i. Sales-to-price:

It is the ratio of sales volume to market value per share at the end of the year t. It is also a

price scaled fundamental variable used for stock returns analysis.

j. Cash-to-price:

Cash flows can also indicate the annual operation of the form. The study uses the cash to

price ratio as an independent variable for regression analysis. It is derived as the annual

cash flow of the firm to the market price per share at the end of each period t.

k. Earning-to-price:

Earnings to price ratio is the next price scaled variable which is used as an independent

variables for study. It is defined as the ratio of earnings per share at the end of period t to

the market price per share which is calculated as follows:

E/Pit = …………………………………………………………… (3.16)

107

Where, E/Pit refers to the earnings to price ratio of the ith firm at the end of period t, EPSit

is the earnings per share, and MPSit is the market price per share at the end of year t.

l. Stock returns:

It is assumed that stock return is the sum of year ended capital gain and cash dividend of

the enterprises suggested by Fama and French (2002). The firms’ stock return is used as

dependent variable in cross-sectional regression analysis. Hence, the stock returns have

been defined as the rate of change in market price of common stock of a firm during

period t over the period t-1. The firm’s stock returns can be calculated as follows:

rit =

( Pt−Pt−1 )+CashDividendPt−1 . ……..……………………….. (3.17)

Equation 3.17 has rit which is the annual firm (ith) return on equity for the year t, Pit is the

market price per share of equity at current year t and Pi(t-1) is the market price per share of

equity for the previous year end t-1.

m. Market returns:

It is assumed as the market rate of return is the overall stock market performance. It has

also been defined as the rate of change in NEPSE index during year t over the year t-1

and it can be calculated using the equation below:

rm = …………………………………………. (3.18)

In equation 3.15, rm is the annual return on overall common stock listed in NEPSE. The

stock returns is treated into two ways: first, the average stock returns which is based on

the average NEPSE index whereas the alternative measure is the stock returns as at mid-

July during the study periods for yearly, monthly and daily database.

n. Book/sales/cash flow/earnings returns:

It is calculated with the similar procedures as the capital gain (i.e. difference between

current year values and last year’s values divided by last year’s value). With the similar

fashion, sales return, cash flow return and earning return can be calculated.

o. Tangible and intangible components:

It is the parts of total stock returns. Total tangible and total intangible return is calculated

as per the relationship formed in model 10.

p. Composite share issuance measure:

It is defined with the following relationship,

108

ι(t-i,t) = log (MEt/MEt-i)………………………………………………… (3.19)

The composite share issuance measure can also be defined as the part of a firm’s growth

in market value that is not attributable to stock returns.

q. News coverage:

It is assumed that the representative news publication is the Kantipur daily. The Nepalese

stock market related headlines in this daily newspaper are collected from the inception of

the publication. Since, the publication of the daily news paper was started in February 18,

1993 which is earlier than the stock market operation in Nepal on January 13, 1994. Thus,

the required annual news coverage is collected through the content analysis of the said

daily newspaper.

r. Dummy variables:

The major political parties led government after the stock operation in Nepal is observed

from various news sources for the period covering the January 13, 1994 to July 15, 2010.

The descriptions of dummy variables are: NC led government 1, otherwise 0 (D1), UML

led government 1, otherwise 0 (D2), King (KG) led government 1, otherwise 0 (D3), and

UCPN (Maoist) led government 1, otherwise 0 (D4).

Thus, with the description of the above variables, the study classified them broadly into

tangible and intangible components, then, analyzed its effects on stock returns and market

returns.

Method of primary data analysis

The primary data analysis has been carried out on the basis of responses collected from

the survey administered based on the structured questionnaire. The focus of the primary

data analysis is to identify the responses of stock investors in aggregate level on various

investment issues in Nepal as well as some of the important behavioral aspects. Though,

the various statistical tools are employed for the study as per the nature of the questions,

the objective remains the same. The demographic variable-wise information in

association with various investment and behavioral issues might be the interesting section

of analysis for the study. On top of the said analysis on primary database, the factor

analysis is also conducted to identify the crucial factors for stock investment in Nepalese

context. The sixteen Likert items with 5-point scale and its responses on the basis of

maintaining “1:10” variable to responses ratio provided the database for the factor

analysis.

109

3.5 Limitations of the study

The limitations of the study are as follows:

o The study is based on the selected variables such as book value, market value,

earnings, cash flows, common stock outstanding, and sales volume which were

suggested by Chan, et.al, (1991); Davis (1994); Daniel and Titman (1997, 2006);

Berk, et.al (1999); Vuolteenaho (2002); Jafee, et.al (1989); Fama and French (1992,

1993, 1995); La Porta (1996); Banz (1981); Daniel, et.al (2001) among others. But,

failed to incorporate the variables like: net stock issues, accruals and momentum

(Fama and French, 2008), and beta (Fama and French (1992) among others) for the

study. The excluded variables might also have the significant explanatory power with

respect to the cross-section of realized stock returns.

o Further, the study excluded the fundamental macro economic variables that also have

the significant explanatory power to predict the stock returns. For instance, Chen, et.al

(1986) documented the macro economic variables like: industrial production,

inflation, interest rate affect the stock returns. Similarly, T-bills, growth rate and the

spread rate (Chen, et.al 1991); the macro-economic volatility (Schwert, 1989), GDP

deflator, foreign direct investment, etc. Thus, the study would have been more

meaningful if such variables are included to analyze the stock returns.

o During the study period (1994 to 2010), there is the potential of 1443 firm year data

but, due to the lack of proper management of required data and the limitations of the

data providers, only 826 firm year’s data are able to organized. The infrastructure for

the database management seems measurable so that the study is suffered. Thus, in

case of the proper availability of firm specific database, the results of the study might

be more reliable and the study could be extended for the population study. Though,

the study of sample in many cases generates the more accurate, reliable and cost

effective outcomes, the study prefer to analyze the population as a whole to increase

the number of observations for the study.

o Generally, stock return is the sum of cash dividend and the capital gain. Cash

dividend is paid out from the annual earnings of the enterprises whereas capital gain

can be attained from the stock trading in the secondary market. The seasonal offerings

- bonus and rights share issues in most cases yield the capital gain but issuing

seasonal offerings is not only the basic condition to achieve the capital gain. The

110

study derived the annual stock returns of the enterprises using cash dividend and the

capital gain. But, it does not consider the bonus and right share adjustment while

determining the capital gain. Thus, it is expected that the results of the study would

more appropriate if the adjustment of seasonal offerings would also be made.

o The scope of the study helps to select the variables for the study. The most reliable

proxy of the firm size is market equity (Daniel and Titman, 1997). But, it can also be

measured by the sales volume of the enterprises (Davis, 1994) which is considered as

the firm specific fundamental variable. It is more appropriate to measure sales

volumes in monetary terms but the study assumed differently due to the lack of

required published database. In case of insurance companies, there is severe

unavailability of historical records regarding the collected of annual insurance

premium. Thus, the study assumed that the number of insurance policies issued during

a fiscal year is the proxy of sales volume of such enterprises rather than the insurance

premium in monetary terms. It would be a logical state if the study is able to replace

the numbers by the rupees.

o Further, the majority of the selected enterprises in the population are financial

institutions and they work as the financial intermediaries, mobilize the deposit

(current as well as the fixed account) for the various purposes. The study assumed that

the sales revenue of such financial institution is equal to the total annual deposit.

Thus, it would be more appropriate if the study could adjust the contribution of

previous long-term deposit into the current year deposit at least for the study periods.

o Due to the lack of published daily and monthly database of stock prices of listed

companies, the study employed the closing price of the enterprises as at the end of

each fiscal year (July 15th). The analysis would have been more pervasive if the daily

and the monthly closing prices would have been included to determine the short run

stock returns. Rendleman, et.al (1982); Barber and Odean (2008); Foucault, et.al

(2011); Loughran and Ritter (1995); French (1980); Brown and Warner (1985); Ritter

(1988), among others use the daily returns whereas Grinblatt and Moskowitz (2004);

Banz (1981); Fama and French (1993, 1996); Chan (2003); La Porta (1996), among

others, employed the monthly returns files for the analysis. Thus, the analysis of the

study would have been extended if the daily and monthly database of the selected

enterprises would be included.

o The study period incorporates the inception of the organized stock exchange operation

in Nepal. But, the study failed to collect the sufficient observations basically before

111

2000 and for the year after mid-July 2010. Former is because of the lack of organized

sources and the later is because of Nepali fiscal year (July 16 th to July 15th). As per the

regulatory provision and the administrative procedures, the listed enterprises are able

to publish its annual financials only after six months or more after completing the

fiscal year. Thus, the study failed to incorporate the latest (2010/011) financials of the

enterprises.

o The number of firm years of the selected enterprises is not similar. Though the study

period is considered as January 13, 1994 to July 15, 2010; the date of listing of the

enterprises, database management, the varying mandatory frameworks, imposed

formatting for regulatory submission for different sector of enterprises is different, etc

cause the variation on observed firm year of the listed enterprises. The variation

ranges from 1 year to 17 years basically as per the age of the firm. Thus, the

survivorship bias or the look-ahead bias as suggested by Fama and French (1996a),

Banz and Breen (1986), and Kothari, et.al (1992) among others is also exists for the

study.

o The primary data is collected from the Kathmandu valley which excluded the opinion

of stock investors residing outside the valley. The investors present during the trading

period at the brokerage floor is considered as the respondents of the study which

bypass the ideas of other investors who perform the stock trading directly from house,

offices and elsewhere. Since, the opinions of the next substantial pie of the

respondents are leaved out for the study so that the results of the primary data analysis

might not the pervasive.

o Similarly, the study used the sample size of 384 stock investors because of the lack of

precise number and list of subjects in the population. The sample size is considered as

364 as suggested by Cochran (1977) at 95 percent level of confidence. Thus, the results

of the study are not free from the limitations of the sample and the sampling procedures.

o The survey was conducted to get the responses of stock investors. Individuals have

different kinds of biases like: Kaniel, et.al (2008) stated that individuals are believed

to have psychological biases; Sum and Wei (2011) suggested the overconfidence

biases; behavioral bias in individuals’ investment choices (Doskeland and Hvide,

2011 and Zhang, 2006 among others), optimistic biases (Camerer and Lovallo, 1999),

Self-attribution biases (Daniel, et.al, 1998), etc. Thus, the study might also be suffered

by similar types of biases which are not tested and considered while the interpretation

of findings. Therefore, the findings of the study would have been better if such types

of biases would have been avoided.

112

o The study analyzed the news effects on stock returns. The published news headings

on a daily newspaper (Kantipur) is assumed a representation sources similar to the

idea suggested by Klibanoff, et.al (1998) and Chan (2003) among others, and does not

consider the other news if any in other news papers. Total news headings for the study

periods are classified into three categories i.e. bad news, good news, and

informational news based on the news heading’s content analysis approach but

excludes the reading of whole article. Thus, the dual interpretable news headings

might mislead the categorization so that such kinds of limitations are essential to

consider while interpreting the results.

o Further, the study ignored the local companies and media effects. Gurun and Butter

(2012) documented that, on average and holding other factors constant, when the

media report news about companies headquartered nearby - that is, local companies -

they use fewer negative words compared to their reports about non-local companies.

Similarly, the study ignored the private information effects on stock returns. Thus, if

the private information effect and the local companies’ effects on media reports would

have been included in the study, the findings might be more glamorous.

o The study incorporates only the political and news effects on stock returns as

intangibles. The other intangibles like: weekend effects (French, 1980); turn-of-the-

year effect (Ritter, 1988); corporate policy changes (Michaely, et.al, 1995);

overconfidence (Odean, 1998; Daniel et.al, 1998; among others); pessimism (Wang,

2001); R&D expenditure and advertising (Chan, et.al, 2001); internet posting and

stock prices (Tumarkin and Whitelaw, 2001); rotation policy of loan officers

(Hertzberg, et.al, 2010); analyst’s recommendations employed by Sun and Wei

(2011); etc are not included. Thus, the results of the study would explain more about

the stock returns if more proxies of intangibles are included in the analysis.

113

Chapter 4

PRESENTATION AND ANALYSIS OF

DATA

This chapter presents the secondary and primary data analysis which deals with the issues

on stock returns and the market information in Nepalese stock market. The chapter is

divided into three sections. The first section is the analysis of secondary database using

the econometric models on the firm specific and market returns series which incorporates

the descriptive statistics and the analysis of stock market returns. The financial news

effect on stock market along with the political leadership effect for the market growth and

development is also shown in this section. The next part shows the presentation of

primary database and the findings of opinion survey, and the third section includes the

concluding remarks on overall data analysis.

4.1 Secondary Data Analysis

In a system approach, the capital market is a component of the whole economic system.

The capital market might be influenced by its own behavior and the other available

information from various sources. In open economic system, the financial market is a

mechanism that fuel for all the economic activities and gradually been influencing by

different kinds of information. The information actually carries some monetary values so

that the valuation of the financial instruments does not remain static for the long period of

time. Being the highly volatile characteristics of the capital market, many opportunities as

well as challenges emerges and disappears if it is not captured at the right time and the

right way. The performance in the capital market in the form of stock price tends to be

useful information for the investment decision makers. But, the magnitude of the

usefulness depends upon the form of the financial market and its growth level. The

financial investors generally use the concept of the investment theories those are

supported by the extensive evidences and, the overall market information can be broadly

classified into fundamental and behavioral information. The accounting growth measures

which is treated as the fundamental information might be the useful market information in

case of relatively static and growing economy. On the other hands, the behavioral issues

114

and the personal characteristics of the stock market participants are also very much

essential to increase the level of confidence and belief towards the sustainable

development of financial market.

The study employed some secondary database to examine the signals of the capital

market movements, different price scaled variables such as: book to market price ratio,

earnings to price ratio, cash flow to price ratio, sales to price ratio, along with the

individual variables and the stock returns series are used as the proxies of market

information because of their relatively static nature. The other information such as:

financial news coverage and political leadership effects are also considered for the

secondary analysis because these variables are not incorporated in the above accounting

growth measures. The news and politics has its own effects for stock returns thus are

treated as separate independent variables and placed them as the other market

information.

Then, the secondary data analysis is presented in a specific order as: the profile analysis,

descriptive statistics, Daniel-Titman regression analysis, news and political effect analysis

for market returns, and, an extended analysis of news and stock returns: the graphical

presentation.

A. Profile Analysis

Table 4.1 incorporates the analysis of 176 enterprises across 14 years starting from 1997

to 2010. Panel A shows the movements of book to market ratio for 825 firm years where

the maximum mean ratio is 1.23 in 1998 and the minimum mean ratio is 0.29 in 2000

followed by 0.35 in 2009, similarly, 1.33 is the highest standard deviation for the year

1998 and the minimum is 0.16 for 2000. Panel B shows the firm year in first row, mean in

second and standard deviation in third row, the figures indicates that the mean book value

of the enterprises gradually decreases from 2000 to 2010 but the movement is volatile

before 2000. The maximum mean book value per share is Rs 293.41 and Rs 145.59 for

the year 1998 and 2007 respectively. The standard deviation on the other hands, indicates

that the highest Rs 215.02 in 1998 and lowest Rs 79.49 in 2009. The Panel C indicates the

average cash dividend of the enterprises and its instablility, as the analysis of 822 firm

years from 1997 to 2010, it ranges between 26.74 percent to 7.16 percent in 2000 and in

2008 respectively where as the standard deviation ranges 49.73 percent to 18.74 for the

year 2010 and 1999 in order. Panel D shows the features of cash flow in million which

115

shows highest in 2009 and lowest (negative) in 1998 for Rs 128.76 million and Rs – 5.10

million

Table 4.1 Profile AnalysisThis table presents the profile analysis of variables: book to market ratio, book value per share, cash dividend, cash flow, cash flow to market price ratio, earnings to price ratio, earnings per share, market equity, market price per share, sales revenue, sales to market price ratio, and stock returns. The measurements employed for the profile analysis are: ratios for Panel A, E (in thousands), F and K (in million); Rs for Panel B, G, and I; millions for Panel D, H and J; and, percentage for Panel C and L. The study period covers from 1997:07 to 2010:07 indicate the firm year, mean and standard deviation for all variables.

Year 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Panel A: Book to Market Ratio (For 825 firm year)F.Year 3 8 10 13 18 36 44 59 74 86 101 112 128 133

Mean 1.00 1.23 0.54 0.29 0.35 0.56 0.72 0.81 0.80 0.76 0.53 0.36 0.35 0.55

SD 0.38 1.33 0.42 0.16 0.22 0.24 0.27 0.40 0.61 0.71 0.62 0.49 0.29 0.49

Panel B: Book Value Per Share (For 825 firm year)F.Year 3 8 10 13 18 36 44 59 74 86 101 112 128 133

Mean 221.88 293.41 189.01 228.28 188.64 173.04 164.49 165.40 164.75 152.52 145.59 149.10 156.47 157.15

SD 123.99 215.02 87.94 83.26 82.10 84.92 87.86 89.68 84.18 101.58 110.32 84.55 79.49 113.63

Panel C: Cash Dividend (For 822 firm year)F.Year 3 8 10 13 18 36 44 59 74 86 101 111 128 131

Mean 20.00 25.00 23.00 26.74 18.35 12.43 15.76 10.61 11.95 13.74 9.86 7.16 9.42 12.69

SD 26.46 20.53 18.74 29.63 25.09 20.61 24.28 23.13 25.88 35.78 31.97 32.72 40.59 49.73

Panel D: Cash Flow (in Millions) (For 825 firm year)F.Year 3 8 10 13 18 36 44 59 74 86 101 112 128 133

Mean 102.36 -5.10 29.03 4.87 25.73 18.47 8.52 11.75 3.24 21.55 19.10 33.17 128.76 -8.34

SD 176.13 35.43 98.93 67.84 85.32 39.56 64.25 60.39 69.32 55.80 63.10 104.15 848.26 888.76

Panel E: Cash Flow to Market Price Ratio (in Thousands) (For 825 firm year)F.Year 3 8 10 13 18 36 44 59 74 86 101 112 128 133

Mean 164.98 -9.47 27.59 8.54 31.70 33.64 12.32 50.19 7.43 67.55 46.48 25.28 195.65 72.81

SD 275.45 54.14 114.42 47.31 68.95 68.58 128.09 334.04 253.88 226.11 342.58 109.85 1185.77 1758.85

Panel F: Earnings to Price Ratio (For 825 firm year)F.Year 3 8 10 13 18 36 44 59 74 86 101 112 128 133

Mean 0.31 0.19 0.15 0.08 0.06 0.08 0.08 0.09 0.14 0.05 0.01 0.05 0.06 0.08

SD 0.28 0.25 0.16 0.06 0.04 0.06 0.07 0.22 0.14 0.31 0.40 0.20 0.08 0.10

Panel G: Earnings Per Share (For 825 firm year)F.Year 3 8 10 13 18 36 44 59 74 86 101 112 128 133

Mean 72.38 53.07 49.73 62.85 42.32 30.86 26.27 25.77 32.81 21.24 19.94 28.64 31.33 29.95

SD 52.33 39.03 28.48 36.81 33.73 29.17 31.82 42.99 33.88 50.59 63.91 48.64 46.66 56.89

Panel H: Market Equity (in Millions) (For 825 firm year)F.Year 3 8 10 13 18 36 44 59 74 86 101 112 128 133

Mean 52.64 115.29 113.08 146.72 163.42 134.20 139.58 130.37 161.04 176.26 188.26 246.36 468.31 588.24

SD 59.36 122.08 115.51 131.20 160.54 164.52 168.56 166.71 209.72 230.74 239.73 329.59 1379.38 1381.76

Panel I: Market Price Per Share (For 825 firm year)F.Year 3 8 10 13 18 36 44 59 74 86 101 112 128 133

Mean 294.33 527.75 609.60 1055.38 828.28 407.81 317.89 297.31 325.33 380.04 559.02 892.40 733.09 425.45

SD 293.68 585.28 516.51 617.65 623.67 384.50 345.09 349.21 409.02 592.42 883.02 970.09 833.75 550.09

Panel J: Sales Revenue (in Millions) (For 824 firm year)F.Year 3 8 10 13 18 36 44 59 74 86 101 112 127 133

Mean 2070.22 2807.633120.3

6 4752.89 4708.802712.0

9 2536.60 2283.132185.8

3 2420.26 2520.402897.6

6 3941.62 4782.62

SD 3265.38 3464.423675.3

2 5192.15 5961.144783.7

5 4866.56 4824.934644.7

2 5273.94 5690.876807.8

7 8375.14 9379.37

Panel K: Sales to Market Price Ratio (in Millions) (For 824 firm year)F.Year 3 8 10 13 18 36 44 59 74 86 101 112 127 133

Mean 4.14 7.08 5.25 3.75 4.34 5.39 6.60 5.94 5.38 5.99 3.31 2.30 4.70 10.64

SD 4.51 6.72 4.38 3.26 3.85 6.80 8.40 7.96 7.73 11.23 4.14 3.44 7.75 16.22

Panel L: Stock Returns (For 683 firm year)

F.Yea 1 3 8 10 13 18 36 44 59 74 86 100 111 120

116

r

Mean 0.14 0.14 0.64 1.30 0.15 -0.26 -0.07 0.05 0.23 0.15 0.68 1.03 -0.01 -0.32SD 0.00 0.19 0.42 0.78 0.51 0.30 0.18 0.21 0.34 0.29 0.67 1.36 0.44 0.29

respectively and the standard deviation ranges between 35.43 and 888.76. The cash flow

to market price ratio is shown in Panel E which is in general upward sloping, negative in

1998 and highest in 2009, the figures are in thousand. Panel F shows the down ward trend

of earning to price till 2001 and took a upward movement upto 2005 and again decreased

sharply till 2007 and then started to move upwards. In figures, the highest mean ratio is

0.31 early in 1997 and lowest 0.01 in 2007 whereas the standard deviation of earning to

price ratio shows the highest 0.40 in 2007 followed by 0.31 in 2006 and lowest 0.04 in

2001. Similarly, Panel G shows the annual movements of earning per share of 825 firm

year, the general trend indicates the downward movements with some spikes in 2000,

2005 and then showed the upward slope 2007 onwards. The maximum mean earnings per

share is Rs 72.38 in 1997 and minimum Rs 19.94 in 2007 followed by Rs 21.24 in 2006.

The standard deviation on the other hands indicates the high point Rs 63.91 in 2007 and

the low point Rs 28.48 in 1999. Market equity is shown in Panel H which indicates the

upward movement early from the beginning which was started from mean value of

market equity Rs 52.64 million and reached to Rs 588.24 million in 2010 with the same

fashion the standard deviation also started from Rs 59.36 million to Rs 1381.76 million

during the study period. Similarly, the average market price per share is shown in Panel I

which exhibit the U-shape with the highest point of Rs 1055.38 followed by Rs 892.40 in

2000 and 2008 respectively whereas the highest value of standard deviation is Rs 970.09

and lowest is Rs 293.68 for the year 2008 and 1997 respectively. On the other hands,

Panel J shows the yearly features of the sales revenue of 824 firm years which indicates

another U-share during 2000 to 2010 and prior to this period the movement is increasing

till 2000. Panel K similarly indicates the sales to market price ratio, the figures are in

million, the trend line does not shows the smooth movement but potray the ups and

downs where the highest point of mean sales to price is 10.64 million in 2010 and lowest

is 2.30 million in 2008. Finally, the stock returns movements for the period covering 1997

to 2010 is shown in Panel L which constitute 683 firm years with four negative average

return figures. Among the various mean points, the highest is 130 percent in 2000

followed by 103 percent in 2008, after these peak points the stock returns experienced the

sharpe decline. Similarly, the lowest point is negative 32 percent for the year 2010

followed by negative 26 percent in 2002. Standard deviation on the other hands shows the

highest 136 percent in 2008 followed by 78 percent in 2000.

Source: Appendix B

117

The graphical presentation of the figures presented in Table 4.1 are shown in the Figure

4.1 which shows the graph of four price scaled variables and eight other accounting

variables for the period mid-July 1997 to mid-July 2010. The database consist of 176

enterprises and the maximum 825 firm year and the minimum 683 firm year. In aggregate

the movement of majority of the selected variable exhibit the downward movement and

Figure 4.1 Graphical presentation showing the trends of the variables: book to market ratio, book value per share, cash dividend, cash flow, earnings per share, cash flow to market price ratio, earnings to price ratio, market equity, market price per share, sales to market price ratio, sales revenue, and stock returns for the period covering 1997:07 to 2010:07. The figures show the trends of respective variables employed for the study.

Source: Appendix B

118

three out of twelve variables indicate the upward movements namely, market equity

(size), sales to price ratio and the sales revenue.

B. Summary Statistics

Table 4.2 presents the summary statistics of the secondary database which is used to

describe the characteristics of the variables selected for the study. The number of

observation contained maximum of 826 firm years and minimum of 822 firm years

during the study period. Average earnings per share for the whole period is Rs 29.15 with

median value Rs 21.18, the minimum and maximum values are Rs 444.08 (negative) and

Rs 626.00 respectively, the standard deviation is Rs 48.87 and the first and third quartiles

are Rs 11.02 and Rs 36.69 in order. The market price per share in second row shows the

mean value Rs 545.07, the share price ranges between Rs 44 to 6830 during the study

period and the standard deviation is Rs 716.68. Taking the figures of book value per

share, mean is Rs 160.29, median is Rs 138.21, and the maximum and minimum values

are Rs 1005.86 and Rs 364 (negative) in order. The standard deviation shows the

magnitude of variation of the variable which is Rs 97.77 represents the stability of book

value as compare to market price per share. On the other hands, cash dividend represents

the mean value is 11.78 percent with median 1.05 percent and the maximum is 560

percent whereas the standard deviation is 35.23 percent. The figures of sales and cash

flow are shown in sixth and seventh row whereas market equity is in fifth indicates that

the mean value of Rs 287.78 million, minimum value is Rs 8 million and maximum value

is Rs 15000 million, the standard deviation shows Rs 815.04 million. The stock returns is

shown in twelfth row which indicates average returns 5.59 percent with median 2.08

percent, the maximum return is 80.21 percent with 9.54 percent as standard deviation of

the whole study period. The price scaled variables: book to market ratio, earnings to price

ratio, cash flow to price ratio and sales to price

Table 4.2Summary Statistics

This table presents the summary statistics of the variables used for the study. The five point scale with median, standard deviation, number of observations per variables, unit of measurement and the name of the variables are presented in columns and individual variables are shown in rows. The first three variables: earnings per share, market price per share and book value per share are measured in Rs, the cash dividend and stock returns are in percentage terms, the market equity, sales revenue and cash flow are measured in millions in Rs, book-to-market ratio, earnings to price ratio, cash flow to price ratio, and sales to price ratios are measured in times where cash flow to price ratio is in times in thousand and sales to price ratio is in times in millions. All the variables are measured for the period 1997:07 to 2010:07.

Variables Unit N Mean Median MinimumMaximu

m

Quartile Std. Dev.Q1 Q3

Earnings per share Rs 826 29.15 21.18 -444.08 626.00 11.02 36.69 48.87

Market price per share Rs 826 545.07 295.00 44.00 6830.00 174.75 626.75 716.68

Book value per share Rs 826 160.29 138.21 -364.00 1005.86 114.36 183.35 97.77

119

Cash dividend Percent 823 11.78 1.05 0.00 560.00 0.00 10.53 35.23

Market equity Million (Rs) 826 287.78 92.07 8.00 15000.00 48.00 320.00 815.04

Sales revenue Million (Rs) 825 3200.67 598.07 0.01 50094.73 269.82 1776.33 6776.75

Cash flow Million (Rs) 826 31.38 1.53 -9523.19 9327.70 -0.36 18.59 492.67

Book to market ratio Times 826 0.56 0.47 -1.44 4.91 0.23 0.76 0.53

Earnings to price ratio Times 826 0.07 0.06 -3.52 1.60 0.03 0.11 0.21

Cash flow to price ratio in '000' 826 66.21 5.35 -17968.28 12777.67 -1.68 48.46 866.73

Sales to price ratio in Million 825 5.59 2.08 0.00 80.21 0.91 5.65 9.54

Stock returns Percent 822 5.59 2.08 0.00 80.21 0.91 5.65 9.54Source: Appendix B

Table 4.3Correlation Matrix

This table shows the correlation coefficients of the variables employed for the study which are: earnings per share (EPS), market price per share (MPPS), book value per share (BVPS), sales, cash flow, market equity (ME), stock returns (Rt) and composite share issuance (CSI) variable. The strength of the correlation coefficient is measured at 5 percent level of significance. The Pearson correlation is used for the analysis. The study period ranges from 1997:07 to 2010:07. The figures in parenthesis are p-values.

LogMPPS LogBVPS LogSales LogCashFlow LogME LogRt LogCSILogEPS 0.41 0.57 0.22 0.10 -0.04 0.09 -0.08  (0.00) (0.00) (0.00) (0.02) (0.28) (0.09) (0.10)LogMPPS   0.40 0.31 0.42 0.40 0.30 0.35    (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)LogBVPS     0.07 0.14 -0.05 0.02 -0.07      (0.05) (0.00) (0.16) (0.64) (0.18)LogSales       0.21 0.39 0.15 0.26        (0.00) (0.00) (0.00) (0.00)LogCashFlow         0.61 0.08 0.30          (0.00) (0.18) (0.00)LogME           0.18 0.58            (0.00) (0.00)LogRt             0.06              (0.35)

Source: Appendix B

ratio is placed in eighth to eleventh rows respectively. The mean values are: 0.56, 0.07,

66.21 and 5.59 for the stated price scaled variables respectively where are median values

are: 0.47, 0.06, 5.35 and 2.08, these median values divide the whole series into two equal

parts. Similarly, the standard deviations in number are: 0.53, 0.21, 866.73 and 9.54. The

unit of measurement for cash to price ratio is in thousand and for sales to price ratio is in

million.

Table 4.3 shows the correlation coefficients of the variables are considered for the study.

Among the total correlation coefficients, nine sets of variables which have no significant

correlation and the remaining nineteen pairs have significant positive correlation at 95

percentage confidence interval. Among the significant correlations, the log cash flow and

log market equity has the correlation coefficient 0.61 is the highest value followed by

0.58 for log market equity and log composite share issuance variable whereas the lowest

correlation coefficient is 0.10 which describes the movements of both variables in the

120

same direction. There are total four negative correlations among the variables, out of

them 0.08 (negative) is the highest correlation between log composite share issuance and

the log earnings per share followed by 0.07 (negative) between log book value per share

and log composite share issuance variable. Further, there is negative correlation between

log market equity and log earnings per share and log book value per share.

C. Daniel-Titman Regression Results

I. Book to Market Decomposition

Table 4.4 presents the book-to-market equity decomposition using Fama-MacBeth

regressions. The relationship between the dependent variable and the change in book

value is assumed to have positive whereas market value is negative. The existence of the

stated relationship between the variables proves the information effect on stock price. In

the first regression estimates, the evidence proves that the priori sign is as expected and

significant at 5 percent level. While taking the independent effect of lagged book to

market effect and the changes in book value and changes in market value, the priori sign

is disappeared in model 3 in case of changes in market price. The figures in Panel A

shows that a unit change in BMio leads to 60.80 percent change in Bit/Mit, 0.20 percent

change in △Bi and 0.10 percent (negative) change in △Mi while taking independent

effect the magnitude decreases. The final column indicates the observations retained in

the analysis to generate the Kolmogovor-Smirnov test (p-values) in accepted level. Panel

B shows that the elasticity between the variables i.e. 1 percent change in Log BMi0, Log △Bi and Log △Mi leads to 88.30, 12.00 and 18.60 (negative) percentage changes in

dependent Log Bit/Mit respectively and the explanatory power of the independent

variables is 98 percent as shown in model 4. Thus, the coefficient indicates that the

relationship between the variables is persistence taking the mutual effects of the selected

variables.

Further, the book to market decomposition can be made by replacing the independent

variables and replacing the dependent by firm level stock returns taking 2 to 5 lagged

periods. It is expected to have the same priori sign as presented in Table 4.4. The basic

regression model 3.2 is transformed into three different versions in Panel A, Panel B and

in Panel C which is shown in the Table 4.5 respectively. The first column indicates the

regression models with lagged periods in parenthesis (i.e. 2 to 5) and for each models the

first row shows the coefficients and the subsequent row indicates p-values. Similarly,

seventh column shows the p-values of ANOVA tests, the next column indicates the

121

coefficient of determination, then K-S rest column shows the normality test values (p-

values) and finally, N indicates the number of observations in each model which is ranges

between 403 to 89 observations.

Table 4.4Regression Analysis for Book to Market Decomposition

This table shows the book-to-market decomposition. The dependent variable is log book-to-market equity at time t. The BMio is the lagged book-to-market equity of the firm i at the period 0 to t. △bi is change in book equity at 0 period to t and △mi is change in market price at 0 period to t. R-square is the coefficient of determination, N is the number of observations and the p-values in the Model Sig. column. The study period covers 1997:07 to 2010:07. The p-values are presented in the parenthesis.

α b1 b2 b3 Model Sig R-squareK-S Test of

residual N

Panel A: Log (Bit/Mit) = α + b1 BMi0 + b2△Bi + b3△Mi + ut Model 1 bi -0.640 0.608 0.002 -0.001 0.000 0.95 0.05 437

p (0.000) (0.000) (0.000) (0.000)        Model 2 bi -0.484 0.461     0.000 0.84 0.20 279

p (0.000) (0.000)            Model 3 bi -0.149   0.000 0.001 0.000 0.17 0.20 279

p (0.000)   (0.333) (0.000)        Panel B: Log (Bit/Mit) = α + b1 LogBMi0 + b2Log△Bi + b3Log△Mi + ut

Model 4 bi 0.081 0.883 0.120 -0.186 0.000 0.98 0.20 50p (0.000) (0.000) (0.000) (0.000)        

Model 5 bi -0.027 0.701     0.000 0.866 0.05 280p (0.002) (0.000)            

Source: Appendix B

Table 4.5 presents the regression estimates of the extension of book to market

decomposition. In Panel A, the interpretation is very much similar to the estimates

available in Table 4.4 Panel A but the coefficients are much stronger. For instance, taking

4 lag periods in model 3, the elasticity is 0.914 followed by 0.873 and 0.857 for taking 5

lag and 2 lag periods respectively between log book-to-market and lagged log book-to-

market ratio. Further, 1 unit change in Bt/Bt-i leads to 39.80 percent change, 37.70 percent,

and 33.70 percent changes in log book to market prices by taking 4 lag, 2 lag and 3 lag

periods, in order. Similarly, 1 percent changes in Pt/Pt-i leads to the highest 28.7 (negative)

percent changes in dependent variable in model 1, 19.10 (negative) percent changes in

model 2 and the least effect is 14.50 (negative) percent in model 3.

Table 4.5Regression Analysis for Book to Market Decomposition: An Extension

This table shows the book-to-market decomposition with an extension of firm level stock returns. The dependent variable is log book-to-market equity at time t for Panel A and firm level stock returns from t to t-1 period for Panel B and Panel C. The B t-i/Pt-i

is the lagged book-to-market equity of the firm for the period t-i to t. The Bt/Bt-i is book to lagged book value ratio and Pt/Pt-i is the ratio of price to lagged price at t-i to t period. R-square is the coefficient of determination; N is the number of observations and the p-values in the Model Sig. column, similarly the p-values of K-S test of residuals and dependent variables are presented in the second last column. The study period covers 1997:07 to 2010:07. The p-values are presented in the parenthesis.

  α b1 b2 b3 Model Sig R-squareK-S Test of Res/DV (p) N

Panel A: log [Bt/Pt] = α + b1 log [Bt-i/Pt-i] + b2 [Bt/Bt-i] + b3 [Pt/Pt-i] + ut

Model 1(i=2)

bi -0.112 0.857 0.377 -0.287 0.000 0.963 0.200 322p (0.000) (0.000) (0.000) (0.000)        

122

Model 2(i=3)

bi -0.239 0.727 0.337 -0.191 0.000 0.829 0.200 185p (0.000) (0.000) (0.000) (0.000)        

Model 3(i=4)

bi -0.420 0.914 0.398 -0.145 0.000 0.934 0.058 146p (0.000) (0.000) (0.000) (0.000)        

Model 4(i=5)

bi -0.268 0.873 0.288 -0.148 0.000 0.934 0.054 200p (0.000) (0.000) (0.000) (0.000)        

Panel B: r(t-i,t) = α + b1 log [Bt-i/Pt-i] + b2 [Bt/Bt-i] + b3 [Pt/Pt-i] + ut

Model 5(i=2)

bi -1.044 -0.175 0.003 1.068 0.000 0.882 0.146 401p (0.000) (0.000) (0.739) (0.000)  

Model 6(i=3)

bi -0.971 -0.230 -0.007 1.012 0.000 0.870 0.200 287p (0.000) (0.000) (0.605) (0.000)  

Model 7(i=4)

bi -0.959 -0.030 0.004 0.995 0.000 0.988 0.056 169p (0.000) (0.157) (0.445) (0.000)  

Model 8(i=5)

bi -0.955 -0.019 -0.001 0.998 0.000 0.971 0.087 89p (0.000) (0.534) (0.829) (0.000)        

Panel C: r(t-i,t) = α + b1 log [Bt-i/Pt-i] + b2 log [Bt/Bt-i] + b3 log [Pt/Pt-i] + ut Model 9(i=2)

bi 0.109 -0.266 -0.094 2.006 0.000 0.822 0.161 403p (0.000) (0.000) (0.145) (0.000)  

Model 10(i=3)

bi 0.141 -0.281 -0.136 2.299 0.000 0.827 0.064 297p (0.000) (0.000) (0.056) (0.000)  

Model 11(i=4)

bi 0.033 -0.166 -0.076 3.106 0.000 0.964 0.074 124p (0.003) (0.000) (0.024) (0.000)  

Model 12(i=5)

bi 0.036 -0.038 -0.060 3.166 0.000 0.965 0.070 95p (0.014) (0.298) (0.093) (0.000)        

Source: Appendix B

The R2 values ranges between 96.30 percentages to 82.90 percentages. While replacing

the dependent variable in Panel B by rt-i,t and maintaining no changes in independent

variables, the coefficient indicates the highest 0.23 unit (negative) changes to firm level

stock returns while changing 100 percent in lagged log book-to-market ratio for 3 lagged

periods followed by 0.175 units (negative) in 2 lagged periods. The estimates do not

retain the expected priori signs where it is negative for b1, unstable for b2 and positive for

b3. While explaining the coefficient of b2, it is shown that the highest 0.007 (negative)

unit effects on dependent variable whereas the lowest is 0.001 (negative) unit effects for

firm level stock returns for 3 and 5 lagged periods, in order. Under Panel C, all the

regression coefficients carry the similar interpretation as: a 100 percent change in

independent variables leads to -0.266, -0.094 and 2.006 unit change firm level stock

returns respectively in case of model 9 (taking 2 lag periods). Similarly, in 3 lag periods,

0.281 unit significant changes for lagged log book-to-price, the coefficient is insignificant

for log Bt/Bt-i and 2.299 unit significant changes for log Pt/Pt-i variable. The coefficient of

determination is highest for 5 lagged periods in model 12 followed by model 11. The

analysis is based on 403 observations for model 9 and 95 observations for model 12 and

the K-S test values in Panel B and C are of the dependent variables rather than the

residuals. From the Table 4.5, it is concluded that the firm level stock returns is

negatively affected by the lagged book-to-market ratio and positively by market price to

123

lagged market price ratio but the relationship between returns and the book to lagged

book values is inconclusive.

II. Sales to Price Decomposition

The next regression estimates is the decomposition of sales to price ratio as similar to the

book-to-market decomposition process. The ratio is decomposed into log sales to price

ratio at t-i to t period as base, and the ratio of sales to lag sales and the ratio of price to

lag price for the lag period 2 to 5 years. Table 4.6 shows the estimated parameters of

twelve the regression models employed for different lag periods with the changes and

modification of concerned variables. In each Panel, the estimate starts from the lag period

2 year to 5 years. The dependent variable for Panel B and Panel C is the firm level stock

returns whereas the basic independent variables are the same for all estimates. The

hypothesized priori sign for the parameters b1 and b2 are positive and negative for b3. In

Panel A, all the parameters are statistically significant at 95 percent confidence level. The

coefficient of b1 measure the elasticity between the dependent and log lagged sales to

price ratio where the elasticity is 0.979 for 2 lag, 0.915 for 5 lag, 0.812 for 3 lag and

0.729 for 4 lag periods. But, the coefficients are significantly nil for the variable sales to

lagged sales ratio and the negative relation as per priori for the price to lagged price ratio.

Taking 2 lag period, the coefficient 0.214 (negative) indicates that the movement of price

to lagged price ratio from 0 to 1 leads a 21.40 (negative) percentage change in sales to

price ratio, followed by -0.154 for 3 lag periods and least effect is -0.092 for taking 4 lag

periods. The R-square values ranges from 97 percent to 67.40 percent and the number of

observations in Panel A ranges and 317 to 199 observations. The p-values of K-S test are

the result of the residual analysis. The Panel B shows the relationship between firm level

stock returns and sales to price and its components. From model 5 to model 8, it is shown

that there is minimal and uncertain effect of lagged sales to price effect on stock returns,

no effect of sales to lagged sales ratio and significant

Table 4.6Regression Analysis for Sales to Price Decomposition

This table shows the sales to price decomposition with an extension of firm level stock returns. The dependent variable is log sales to price ratio at time t for Panel A and firm level stock returns from t to t-1 period for Panel B and Panel C. The B t-i/Pt-i is the lagged book-to-market equity of the firm for the period t-i to t. The Bt/Bt-i is book to lagged book value ratio and Pt/Pt-i is the ratio of price to lagged price at t-i to t period. R-square is the coefficient of determination; N is the number of observations and the p-values in the Model Sig. column. The study period covers 1997:07 to 2010:07. The p-values are presented in the parenthesis.

Α b1 b2 b3Model

SigR-

squareK-S Test of Res/DV (p) N

Panel A: log [St/Pt] = α + b1 log [St-i/Pt-i] + b2 [St/St-i] + b3 [Pt/Pt-i] + utModel 1(i=2)

bi 0.482 0.979 0.000 -0.214 0.000 0.970 0.065 317p (0.000) (0.000) (0.000) (0.000)        

124

Model 2(i=3)

bi 1.511 0.812 0.000 -0.154 0.000 0.842 0.075 199p (0.000) (0.000) (0.000) (0.000)        

Model 3(i=4)

bi 1.970 0.729 0.000 -0.092 0.000 0.674 0.200 235p (0.000) (0.000) (0.000) (0.000)  

Model 4(i=5)

bi 0.924 0.915 0.000 -0.107 0.000 0.801 0.050 227p (0.000) (0.000) (0.000) (0.000)        

Panel B: r(t-i,t) = α + b1 log [St-i/Pt-i] + b2 [St/St-i] + b3 [Pt/Pt-i] + ut

Model 5(i=2)

bi -0.894 -0.002 0.000 0.985 0.000 0.867 0.200 380p (0.000) (0.718) (0.428) (0.000)  

Model 6(i=3)

bi -0.801 -0.006 0.000 0.942 0.000 0.876 0.064 296p (0.000) (0.411) (0.209) (0.000)        

Model 7(i=4)

bi -0.803 0.005 0.000 0.923 0.000 0.848 0.061 210p (0.000) (0.615) (0.283) (0.000)  

Model 8(i=5)

bi -0.784 -0.006 0.000 0.964 0.000 0.885 0.053 155p (0.000) (0.572) (0.171) (0.000)        

Panel C: r(t-i,t) = α + b1 log [St-i/Pt-i] + b2 log [St/St-i] + b3 log [Pt/Pt-i] + ut Model 9(i=2)

bi 0.001 0.000 0.020 2.533 0.000 0.998 0.200 57p (0.642) (0.634) (0.000) (0.000)  

Model 10(i=3)

bi 0.009 -0.001 -0.003 2.663 0.000 0.997 0.200 65p (0.058) (0.356) (0.080) (0.000)        

Model 11(i=4)

bi 0.022 0.000 -0.005 2.584 0.000 0.992 0.200 47p (0.031) (0.810) (0.172) (0.000)  

Model 12(i=5)

bi 0.183 -0.026 -0.050 3.620 0.000 0.977 0.200 113p (0.000) (0.000) (0.000) (0.000)        

Source: Appendix B

positive effect of price to lagged price ratio for firm level stock returns. More specifically,

0.985 unit changes on firm returns when 1 unit changes in price to lagged price ratio

taking 2 lag periods, 0.964 unit changes, 0.942 unit changes and 0.923 unit changes

taking 5 lag, 3 lag and 4 lag periods respectively and all of them are significant at 5

percent. Further, the Panel C indicates that a 100 percent changes in P t/Pt-i generates 2.533

unit changes in rt-i, t taking 2 lag periods, 2.584 units when taking 4 lag periods, 2.663 unit

changes taking 3 lag periods and 3.62 unit changes in dependent variable while taking 5

lag periods. The coefficient of determination values ranges between 0.998 and 0.977, the

numbers of observations in Panel C are relatively low because of the normality test. The

p-values of K-S test indicate the analysis of residuals in case of Panel A and C and the

analysis of dependent variable in case of Panel B.

In sum, there is consistent negative relationship between sales to price and price to lagged

price ratio and consistent positive relation between firm level stock returns and price to

lagged price ratio whereas inconclusive relation and least effects of lagged sales to price

and sales to lagged sales ratio for stock returns.

III. Cash Flow to Price Decomposition

The decomposition of cash flow to price ratio is shown in Table 4.7 which is divided into

three panels. The normality test is of the dependent variable is shown Panel B and the

residual analysis is shown in Panel A and Panel C. The expected priori sign is proved in

125

Panel A and when the dependent variable changes to firm returns there is consistently

positive sign for price to lagged price ratio. The average coefficient for b1 is 0.585 that

measure the elasticity between cash flow to price and lagged cash flow to price ratio, b 2

constitute 0.024 which describes 1 unit change in cash flow to lagged cash flow ratio

leads to 2.40 percent change in cash flow to price ratio, similarly, the average coefficient

if b3 is -0.163 with the similar interpretation as b2 and the average coefficient of

determination is 51.10 percent. In Panel B, on average 0.993 unit changes in firm returns

because of 1 unit changes in price to lagged price ratio and a100 percent change in lagged

cash flow to price ratio leads to 0.010 unit change in firm returns on an average. An

average R-square is 93 percent and average p-value for K-S test is 0.065 that describe all

the regression models in Panel B are normally distributed. Likewise, the regression

coefficient in Panel C shows the similar meaning as a 100 percent change in lagged cash

flow to price and price to lagged price ratio leads to 0.002 and 1.976 unit changes in firm

level stock return taking lag period 1. Similarly, 0.05, 0.03 and 2.885 unit changes in firm

returns in case of lag period of 2, the figures in regression model 11 are significant at 5

percent. In sum, while taking the independent variable effect, the price to lagged price

ratio has the substantial explanatory power for firm level stock returns during the study

periods.

Table 4.7Regression Analysis for Cash flow to Price Decomposition

This table shows the cash flow to price decomposition in Panel A and the extension of firm level stock returns in Panel B and C. The dependent variable is log cash flow-to-price ratio at time t for Panel A and firm level stock returns from t to t-i period for Panel B and Panel C. The Ct-i/Pt-i is the lagged cash flow-to-price ratio of the firm for the period t-i to t. The Ct/Ct-i is cash flow to lagged cash flow value ratio and Pt/Pt-i is the ratio of price to lagged price at t-i to t period. R-square column indicates the coefficient of determination; N is the number of observations and the p-values in the Model Sig. column. The study period covers 1997:07 to 2010:07. The p-values are presented in the parenthesis.

α b1 b2 b3Model

Sig R-squareK-S Test of Res/DV (p) N

Panel A: log [Ct/Pt] = α + b1 log [Ct-i/Pt-i] + b2 [Ct/Ct-i] + b3 [Pt/Pt-i] + ut Model 0(i=1)

bi 0.434 0.959 0.121 -0.266 0.000 0.963 0.200 221p (0.000) (0.000) (0.000) (0.000)        

Model 1(i=2)

bi 2.486 0.558 0.000 -0.211 0.000 0.489 0.200 247p (0.000) (0.000) (0.000) (0.000)        

Model 2(i=3)

bi 2.849 0.468 0.000 -0.155 0.000 0.414 0.200 182p (0.000) (0.000) (0.000) (0.000)        

Model 3(i=4)

bi 2.737 0.489 0.000 -0.110 0.000 0.406 0.200 134p (0.000) (0.000) (0.000) (0.000)        

Model 4(i=5)

bi 2.755 0.452 0.001 -0.075 0.000 0.283 0.200 102p (0.000) (0.000) (0.000) (0.042)        

Panel B: r(t-i,t) = α + b1 log [Ct-i/Pt-i] + b2 [Ct/Ct-i] + b3 [Pt/Pt-i] + utModel 5(i=2)

bi -0.944 0.012 0.000 0.953 0.000 0.850 0.059 282p (0.000) (0.055) (0.736) (0.000)  

Model 6(i=2)

bi -0.993 0.005 0.000 1.014 0.000 0.985 0.061 247p (0.000) (0.288) (0.074) (0.000)        

Model 7(i=3)

bi -0.968 0.020 0.000 0.978 0.000 0.898 0.059 195p (0.000) (0.146) (0.577) (0.000)        

126

Model 8(i=4)

bi -0.998 0.000 0.000 1.001 0.000 0.999 0.089 84p (0.000) (0.489) (0.000) (0.000)        

Model 9(i=5)

bi -0.978 0.013 0.000 1.022 0.000 0.918 0.059 90p (0.000) (0.464) (0.845) (0.000)        

Panel C: r(t-i,t) = α + b1 log [Ct-i/Pt-i] + b2 log [Ct/Ct-i] + b3 log [Pt/Pt-i] + ut Model 10(i=1)

bi -0.008 0.002 0.000 1.976 0.000 0.996 0.092 69p (0.163) (0.077) (0.785) (0.000)        

Model 11(i=2)

bi -0.123 0.050 0.031 2.885 0.000 0.967 0.085 132p (0.003) (0.000) (0.001) (0.000)        

Model 12(i=3)

bi -0.042 0.012 0.001 3.389 0.000 0.976 0.066 71p (0.398) (0.284) (0.965) (0.000)  

Model 13(i=4)

bi 0.017 0.009 -0.007 3.505 0.000 0.959 0.199 83p (0.851) (0.660) (0.677) (0.000)  

Model 14(i=5)

bi 0.072 -0.025 -0.030 4.349 0.000 0.963 0.093 67p (0.557) (0.368) (0.246) (0.000)        

Source: Appendix B

IV. Earnings to Price Decomposition

The separation of earnings and price from the variable earnings price ratio is shown in

Table 4.8 which is divided into three Panels. The first section describes the

decomposition of earnings to price ratio into lagged earnings to price ratio and the

independent effect of earnings and price variables. The various studies in the financial

market literatures proved that earnings have

Table 4.8Regression Analysis for Earnings to Price Decomposition

This table shows the earnings to price decomposition with an extension of firm level stock returns. The dependent variable is log earnings to price ratio for the period t to t-i in Panel A and firm level stock returns from t to t-i period for Panel B and Panel C. The Et-i/Pt-i is the lagged earnings-to-price ratio of the firm for the period t-i to t. The Et/Et-i is earnings to lagged earnings ratio and Pt/Pt-i is the ratio of price to lagged price at t-i to t period. R-square is the coefficient of determination; N is the number of observations and the p-values in the Model Sig. column. The K-S test column indicates the test of normality of the series. The study period covers from 1997:07 to 2010:07. The p-values are presented in the parenthesis.

Α b1 b2 b3 Model Sig

R-square K-S Test of Res/DV (p)

N

Panel A: log [Et/Pt] = α + b1 log [Et-i/Pt-i] + b2 [Et/Et-i] + b3 [Pt/Pt-i] + ut

Model 1(i=1)

bi -0.022 0.988 0.371 -0.364 0.000 0.999 0.200 278p (0.000) (0.000) (0.000) (0.000)  

Model 2(i=2)

bi -0.140 0.940 0.309 -0.255 0.000 0.977 0.200 324p (0.000) (0.000) (0.000) (0.000)  

Model 3(i=3)

bi -0.561 0.434 0.001 -0.094 0.000 0.432 0.200 372p (0.000) (0.000) (0.000) (0.000)  

Model 4(i=4)

bi -0.876 0.194 0.000 -0.073 0.000 0.299 0.200 298p (0.000) (0.000) (0.003) (0.000)  

Model 5(i=5)

bi -0.768 0.337 0.010 -0.064 0.000 0.398 0.200 226p (0.000) (0.000) (0.000) (0.000)        

Panel B: r(t-i,t) = α + b1 log [Et-i/Pt-i] + b2 [Et/Et-i] + b3 [Pt/Pt-i] + ut

Model 6(i=2)

bi -0.955 0.014 0.001 1.003 0.000 0.986 0.200 255p (0.000) (0.030) (0.262) (0.000)  

Model 7(i=2)

bi -0.894 0.032 0.001 0.993 0.000 0.961 0.200 134p (0.000) (0.033) (0.462) (0.000)  

Model 8(i=3)

bi -0.887 0.029 0.000 0.985 0.000 0.961 0.099 228p (0.000) (0.027) (0.357) (0.000)  

Model 9(i=4)

bi -0.825 -0.014 0.000 0.950 0.000 0.863 0.200 205p (0.000) (0.701) (0.652) (0.000)  

Model 10(i=5)

bi -0.829 -0.014 -0.001 0.962 0.000 0.887 0.050 156p (0.000) (0.754) (0.350) (0.000)        

127

Panel C: r(t-i,t) = α + b1 log [Et-i/Pt-i] + b2 log [Et/Et-i] + b3 log [Pt/Pt-i] + ut

Model 11(i=1)

bi 0.006 0.001 -0.004 1.918 0.000 0.994 0.200 180p (0.025) (0.617) (0.114) (0.000)  

Model 12(i=2)

bi -0.001 -0.003 -0.008 2.627 0.000 0.997 0.053 65p (0.765) (0.369) (0.012) (0.000)  

Model 13(i=3)

bi 0.002 -0.010 0.008 2.693 0.000 0.992 0.092 70p (0.814) (0.148) (0.256) (0.000)  

Model 14(i=4)

bi -0.018 -0.027 -0.026 3.655 0.000 0.967 0.200 149p (0.567) (0.320) (0.234) (0.000)  

Model 15(i=5)

bi 0.036 0.060 0.098 3.763 0.000 0.974 0.171 107p (0.360) (0.075) (0.001) (0.000)        

Source: Appendix B

significant effects for price movement in different market context. This table replicates

the similar findings in Nepalese scenario as well. When taking 1, 2 and 3 lag periods,

there is significant effect of lagged earnings to price ratio for firm level stock returns as

shown in Panel B but there is insignificant and negative effect for 4 and 5 lag periods.

Likewise, the earnings to lagged earnings have minimal and insignificant effect for stock

returns but the coefficients of b3 indicates the positive and significant effect on stock

returns. The price to lagged price effect is also strong, consistent and significant in Panel

C but there are unreliable effect of lagged earnings to price ration and earnings to lagged

earnings variables for firm level stock returns as shown in model 11 to model 15. Taking

a look in Panel A, all the regression coefficients are significant at 5 percent level, the

relationship is strong while taking 1 lag periods and as on the increment of the lag periods

to 2, 3, 4 and 5, the magnitude is started to decrease gradually for price to lagged price

ratio, the similar manner for lagged earnings to price ratio except model 5 and the similar

pattern for earnings to lagged earnings ratio. K-S test column indicates the p-values for

normality where Panel A and C is the test of regression residuals and for Panel B, the

values shows the normality test of dependent variable. With these evidences, it is

concluded that for the firm level stock returns there is significant effect of lagged earnings

to price ratio up to three years and the maximum a 100 percent change in lagged earnings

to price ratio generates a 0.032 unit changes in firm level stock returns.

From the Table 4.5 to 4.8, the independent effect of price scaled variables on firm level

stock returns is analyzed. The evidence shows that it is not necessary to increase the stock

returns when sales volume increases because in most of the cases there is negative

relationship between sales to price and stock returns but the relationship is inconsistent

considering the lag periods 2 to 5 years. Another finding is: there is inconsistent

relationship between the firm returns and earnings to price ratio but its strength is more

than the cash flow and sales effect. In numerical form, the maximum effect a 3.2 percent

point change in firm returns because of a 100 percent change in earnings to price variable

128

when lag periods is 2 years and the relationship gradually decreases when lag period is 3

years and fourth and fifth lag periods respectively. When talking about the consistent

independent effects of price scaled variables, it is shown that the positive effect between

firm returns and cash flow to price ratio and the negative relationship of book to market

ratio and the returns. Among the variables, the highest regression coefficients of the

relationship are: 23 percent point changes in firm returns when 2 lag periods and 17.5

percent point changes in firm returns when 3 lag periods because of a 100 point changes

in book to market ratio.

Further, the mutual effect of price scaled variables for the movement of stock returns is

analyzed in Table 4.9. From the table, it shows the disappearance of the negative sign in

most of the cases which is shown in Table 4.5 Panel B and C. Since, the analysis at 4 lag

periods still retains the negative sign but it is only significant at 27.3 percent level. There

is least effect of sales to price and cash flow to price ratio for firm returns but the strong

and significant effect of earnings to price ratio for stock returns is appeared. Taking only

1 to 3 years of lag periods, the numbers of observations are relatively higher as the

maximum is 576 observations and the least is 319 observations. The series is normally

distributed and the coefficients of determination are 28.9 percent, 24.7 percent and 11.4

percent respectively for the lag period 1, 2 and 3 years. The strength of relationship is

significant and consistent for book to market ratio than the earnings to price. When

considering the coefficients of book to price and earning to price ratio for the models 2, 3

and 4, five out of six regression coefficients are significant at two standard deviations. In

contrast, only one regression coefficients in model 5 and model 6 is significant out of ten

coefficients.

Table 4.9Regression Analysis of Firm Returns on Price Scaled Variables

This table shows the analysis of firm returns on price scaled variables. The dependent variable is firm returns from 1 lag to 5 lag years. The independent variables are: book to price ratio, sales to price, cash flow to price and earnings to market price ratio for the period t to t-i. The Bt-i/Pt-i is the lagged book to price ratio for the period t-i to t, S t-i/Pt-i is the lagged sales to price ratio for the period t-i to t, C t-i/Pt-i is the lagged cash flow to price ratio for the period t-i to t, and E t-i/Pt-i is the lagged earnings to price ratio for the period t-i to t. α is constant and the p-values of ANOVA test is Model Sig, R-square is the coefficient of determination; N is the number of observations and K-S test column indicates the test of normality of the series. The study period covers from 1997:07 to 2010:07. The p-values are presented in the parenthesis.

  α b1 b2 b3 b4 Model Sig R-squareK-S Test of Res/DV (p)

N

r(t-i,t) = α + b0 [Bt-i/Pt-i] + b1 [St-i/Pt-i] + b2 [Ct-i/Pt-i] + b3 [Et-i/Pt-i] + ut Model 1(i=1)

bi -0.242 0.340 0.000 0.000 0.234 0.000 0.247 0.200 576p (0.000) (0.000) (0.000) (0.531) (0.000)  

Model 2(i=2)

bi -0.269 0.354 0.000 0.000 0.262 0.000 0.289 0.067 502p (0.000) (0.000) (0.000) (0.067) (0.000)  

Model 3(i=3)

bi -0.173 0.255 0.000 0.000 -0.019 0.000 0.114 0.200 319p (0.000) (0.000) (0.000) (0.156) (0.887)  

Model 4 bi 0.033 -0.049 0.000 0.000 0.217 0.019 0.041 0.053 289

129

(i=4) p (0.426) (0.273) (0.050) (0.015) (0.148)  Model 5(i=5)

bi 0.082 0.000 0.000 0.000 -0.338 0.539 0.013 0.200 236p (0.159) (0.996) (0.580) (0.356) (0.180)        

Source: Appendix B

Thus, it is concluded that for the analysis of firm level stock returns movements only the

three years of historical accounting data are useful to find the signals and to establish the

relationship between the dependent and independent variables.

Table 4.9a presents the regression results of firm level stock returns on book to market,

earnings to price ratio, lagged firm returns and the share issuance variables. The random

terms represent the contribution of undefined variables for the movement of firm returns.

There are altogether eleven models are presented, out of them, the contribution of the

changes in firm’s common stock volume has negligible relation with firm returns because

4 out of 6 coefficients have significantly zero relationship. When looking at the

coefficient of determination, interestingly, when adding one more independent variable in

model 4 (i=3), the r-square value decreased from 25.50 percent in model 4a to 23.50

percent in model 4. The evidence in model 2a and model 5 indicates the extreme

coefficients as 0.30 and -0.30 respectively; coincidently the coefficients are same though

the lag period is different. Against the earlier consistent relationship between the firm

returns and the book to market price ratio in Table 4.5 panel B and C, it is shown the

fluctuating relationship between book to price and returns which is supported by the

irregularly positive and negative signs that appear in Table 4.9i. Best on the proof of

relationship in decomposition section, the book to market ratio is retained and because of

the second strong relationship with firm returns, the earnings to price ratio is also retained

even though its relationship is not consistent. The firm level stock returns and firm returns

are used as interchangeably. The extreme relationship between the firm returns and

earnings to price ratio is 0.499 (positive) and 0.901 (negative) as shown in model 5 and

model 6a, similarly, the book returns’ contribution ranges between 0.703 followed by

0.622 and 0.103 (negative) but only 6 coefficients out of 11 are significant at 5 percent for

book returns.

In majority of the cases, the relationship between past returns and firm returns is negative

which suggest the early winner fail to achieve in later periods and vice-versa. The seven

coefficients are significant at 5 percent among ten for past returns variable. The

interpretation of all the coefficients that appeared in Table 4.9a is: a one unit change in

independent variables (book to price, earnings to price, book returns, past returns and

130

share issuance measure) generates bi unit change in dependent variable. The major

conclusions are: there is fluctuating relation of book to market ratio with firm returns and

there is chances of early losers to achieve in the later periods when taking the analysis of

5 lag periods.

Table 4.9a

Regression Results of Firm Level Stock Returns on Book-to-Market, Earnings to Price, Past Returns and Share Issuance Measures

This table reports the results of a set of regressions of firm level stock returns on lagged book and earnings to price ratios (BP(t-i,t) + b2 EP(t-i,t)), past accounting growth measures – book returns (rB(t-i,t)), past returns (r(t-i,t)) and share issuance measure ι(t-i,t) for the period t-i, to t where ut is the random terms. The study period covers from 1997:07 to 2010:07. The p-values are presented in the parenthesis. R-square is the coefficient of determination; N is the number of observations and the p-values in the Model Sig. column. The K-S test column indicates the test of normality of the series.

rt = α + b1 BP(t-i,t) + b2 EP(t-i,t) + b3 rB(t-i,t) + b4 r(t-i,t) + b5 ι(t-i) + ut

  α b1 b2 b3 b4 b5Model Sig

R-square

K-S Test of Res(p)

N

Model 1(i=0)

bi 0.053 -0.017 0.022 0.050   0.000 0.000 0.204 0.064 549p (0.005) (0.492) (0.665) (0.011)   (0.000)        

Model 2(i=1)

bi -0.152 0.259 0.130 -0.065 0.019 0.000 0.000 0.304 0.200 398p (0.000) (0.000) (0.023) (0.021) (0.197) (0.000)  

Model 2a(i=1)

bi -0.210 0.302 0.169 -0.049 0.033   0.000 0.237 0.200 398p (0.000) (0.000) (0.005) (0.096) (0.027)          

Model 3(i=2)

bi -0.031 0.162 -0.015 -0.103 -0.127 0.000 0.000 0.471 0.074 312p (0.159) (0.000) (0.743) (0.000) (0.000) (0.000)  

Model 3a(i=2)

bi -0.071 0.201 -0.005 -0.103 -0.134   0.000 0.454 0.080 297p (0.001) (0.000) (0.916) (0.000) (0.000)          

Model 4(i=3)

bi 0.057 0.043 0.230 0.065 -0.241 0.000 0.000 0.235 0.057 276p (1.631) (1.077) (2.053) (2.010) (-7.184) (-0.776)  

Model 4a(i=3)

bi 0.022 0.032 0.147 0.078 -0.229   0.000 0.255 0.200 254p (0.471) (0.382) (0.142) (0.007) (0.000)          

Model 5(i=4)

bi 0.171 -0.300 0.499 0.072 -0.221 0.000 0.000 0.160 0.200 202p (0.001) (0.000) (0.004) (0.053) (0.000) (0.000)  

Model 5a(i=4)

bi 0.094 -0.232 0.443 0.065 -0.192   0.001 0.086 0.200 202p (0.053) (0.001) (0.014) (0.091) (0.001)          

Model 6(i=5)

bi 0.141 -0.114 -0.928 0.703 -0.299 0.000 0.000 0.229 0.200 165p (2.433) (-1.516) (-3.266) (5.040) (-4.466) (3.129)  

Model 6a(i=5)

bi 0.195 -0.139 -0.901 0.622 -0.316   0.000 0.181 0.200 165p (0.001) (0.072) (0.002) (0.000) (0.000)          

Source: Appendix B

The basic regression model 3.2 is being analyzed employing the lagged book to market

ratio as the proxy of expected returns is tangible information and error terms is treated as

the proxy of intangible information. Table 4.10 presents the relationship between firm

returns and book to market ratio with inclusion of book returns. Some key notes are: the

inverse relation of book to market ratio with firm returns once again retained except in

model 1 but there are only half out of total coefficients are significant at 5 percent and the

0.107 (negative) is the highest coefficient followed by 0.071 (negative) in model 3 (i=2)

and model 5 (i=4) respectively. The book returns as an independent variable proved the

positive relationship with returns, 0.164 is the highest value but the model as well as the

coefficient is insignificant followed by 0.078 which is significant (both model and

131

coefficient) in model 6 (i=6) and model 3 (i=2) in order. Four coefficients out of six are

significant at two standard deviations. With these evidences, it is concluded that even

though book returns does not include in the firm returns, it is shown that there is positive

relationship and in some cases the strong relationship with firm level stock

Table 4.10Regression Analysis of Firm Returns on Book-to-Market and Book Returns

This table reports the results of a set of regressions of the firm level stock returns on lagged book to price and book returns (rB(t-i,t)) for the period t-i, to t where ut is the random terms is the proxy of intangible information. The study period covers from 1997:07 to 2010:07. The p-values are presented in the parenthesis. R-square is the coefficient of determination; N is the number of observations and the p-values in the Model Sig. column. The K-S test column indicates the test of normality of the series.

r(t-i, t) = b0 + b1 BP (t-i, t) + b2 riB

(t-i,t) + ui,t

  α b1 b2 Model Sig R-squareK-S Test of Res (p)

N

Model 1(i=0)

bi -0.024 0.052 0.012 0.001 0.030 0.200 430p (0.096) (0.003) (0.420)  

Model 2(i=1)

bi 0.046 -0.003 0.034 0.004 0.036 0.200 305p (0.000) (0.838) (0.002)  

Model 3(i=2)

bi 0.179 -0.107 0.078 0.000 0.100 0.200 285p (0.000) (0.000) (0.000)  

Model 4(i=3)

bi 0.089 -0.025 0.045 0.047 0.026 0.200 232p (0.000) (0.222) (0.014)  

Model 5(i=4)

bi 0.120 -0.071 0.069 0.005 0.049 0.200 212p (0.000) (0.016) (0.002)  

Model 6(i=5)

bi 0.087 -0.061 0.164 0.172 0.022 0.200 158p (0.010) (0.152) (0.071)        

Source: Appendix B

returns. Looking at all the coefficients and the normality of the series, it is shown that the

model 3 (i=2) is the best one where the bi values are relatively strong and significant at 95

percent confidence, the r-square is highest, the model itself is significant and the

observations also are substantial in number (i.e. 285). Thus, it can be said that book to

market ratio is more useful up to 2 lag periods/years which is the more aggressive

findings as compare to the findings of Table 4.9. With the similar procedures, the

usefulness of the other price scaled variables can be calculated.

Table 4.11 gives the detailed analysis of firm returns on price scaled variables with the

extension of fundamental returns for the period July 1997 to July 2010. The regression

coefficients once again proved that there negative relationship between firm returns and

the book to market ratio where as only two coefficients for earnings to price has negative

sign out of eleven regression models for 1 to 5 lag periods. Thus, with the presence of the

fundamental returns measures - the relationship between earnings to price ratio and firm

returns is positive as oppose to decomposition analysis. The values of y1 parameter from

model 1 to model 11 shows the strongest 0.228 (negative) in model 8 (i=4) followed by

model 4 (i=2) where the t-statistics are more than cutoff point at 95 percent confidence

132

level. Contrast to Table 4.9, the predictive power of earnings to price is more than book to

market ratio which can be seen in model 8 (i=4) where the predictive power of earnings to

price is 0.472 unit as compare to 0.228 (negative) unit for book to market ratio. Thus, the

major conclusion of Table 4.11 is: out of fundamentals price scaled variables, only book

to price and earnings to price ratios have strong predictive power and

Table 4.11An Analysis of Firm Returns on Price Scaled Variables with Fundamental Returns Measures

This table presents the regression results of the firm level stock returns on lagged price scaled variables along with the extension of fundamental return measures. From the beginning in the model give below are: book to price, sales to price, cash flow to price and earnings to price ratio and later four variables are: the book returns, sales returns, cash flow returns, earnings returns. All the variables in the model cover 1 lag period to 5 lag period. The study period covers 1997:07 to 2010:07 and the t-statistics are presented in the parenthesis. Coefficient of determination is represented by R-square column; N is the number of observations and the p-values are in Model Sig. column. The K-S test column indicates the test of normality of the series.

r(t-i, t) = y0 + y1BP (t-i,t) + y2SP (t-i.t) + y3CP (t-i,t) + y4EP(t-i,t) + y5.riB

(t-i,t) + y6.riS

(t-i, t) + y7.riC

(t-i, t) + y8. riE

(t-i, t) + ui,t

  α y1 y2 y3 y4 y5 y6 y7 y8 Model Sig R-squareK-S Test of Res (p)

N

(i=1)bi 0.08 0.01 0.00 0.00 -0.03 -0.01 0.00 0.00 0.00 0.000 0.218 0.200 335t (5.01) (0.69) (-4.20) (4.02) (-1.05) (-0.63) (6.94) (1.25) (1.27)  

(i=1)bi 0.07 -0.02 0.00 0.00 0.06 0.00       0.000 0.135 0.200 313t (4.32) (-0.97) (-4.64) (4.55) (1.29) (0.40)  

(i=1)bi 0.06 0.02     -0.02 0.00       0.561 0.005 0.200 380t (3.65) (1.36) (-0.55) (-0.12)  

(i=2)bi 0.21 -0.20 0.00 0.00 0.39 0.05 0.00 0.00 0.00 0.000 0.221 0.200 271t (9.17) (-7.25) (-1.16) (0.71) (6.12) (3.76) (-0.78) (-0.28) (-2.28)  

(i=2)bi 0.18 -0.17     0.29 0.02 0.00   0.00 0.000 0.191 0.200 259t (10.21) (-6.75) (5.56) (1.84) (-0.26) (-2.29)  

(i=3)bi 0.19 -0.15 0.00 0.00 0.26 0.00 0.00 0.00 0.00 0.000 0.234 0.200 235t (6.91) (-4.53) (-1.65) (1.20) (4.18) (0.04) (5.95) (1.11) (-1.23)  

(i=3)bi 0.14 -0.08     0.20         0.005 0.038 0.051 275t (6.01) (-2.86) (2.73)  

(i=4)bi 0.20 -0.23 0.00 0.00 0.47 0.03 0.00 0.00 0.00 0.000 0.151 0.085 232t (4.80) (-4.47) (-1.47) (-0.33) (3.58) (1.39) (2.93) (-1.62) (-0.91)  

(i=4)bi 0.08 -0.09     0.31 0.03       0.001 0.100 0.200 160t (2.80) (-2.35) (3.54) (2.19)  

(i=5)bi 0.06 -0.09 0.00 0.00 0.46 0.05 0.00 0.00 0.00 0.000 0.220 0.200 158t (1.64) (-2.01) (-0.64) (-0.28) (2.81) (2.30) (4.85) (-0.10) (-0.93)  

(i=5)bi 0.04 -0.05     0.28 0.05     0.00 0.027 0.066 0.200 163t (1.16) (-1.10)     (1.66) (2.39)     (-1.41)        

Source: Appendix B

the usefulness of the historical data is proved to be the lagged 2 to 4 years where all the

respective regression coefficients are significant at 5 percent risk level. On the other

hands, the sales to price and cash flow to price ratios have no predictive power for firm

level stock returns in Nepalese market as proved by all the coefficients is zero. Similarly,

the fundamental returns measures have least explanatory power for firm level stock

returns. Among them, the book returns has more explanatory power which can be proved

by seeing the coefficients in 5 lag periods (i.e. 0.055 unit) followed by 2 lag periods (i.e.

0.047 unit). The second most predictive power is of earnings returns but there are least

effects of sales returns and cash flow returns respectively. All the presented regression

133

models are significant at 5 percent risk except model 3 (i=1) where F-test p-value is

0.561. The coefficients of determination values are even lesser than it appeared in Table

4.9 where the four independent variables has 28.9 percent explanatory power for the

changes in firm level stock returns but it is only 23.4 percent followed by 22.1 percent for

3 and 2 lag periods in model 6 and model 4 respectively. The data series for all regression

models are normally distributed as shown by K-S test of residuals and the total numbers

of observations are 380 maximum and 158 minimum for model 3 (i=1) and model 10

(i=5) respectively. The holding period stock returns as dependent variable and the proxy

of intangible information as per the regression model 3.9 (Table 4.10) with its extension

to other price scaled variables are treated as independent variables along with the

fundamental to price variables. Share issuance measure is also included as a separate

independent variable in Table 4.12. This table shows the independent effect of intangible

information for the prediction of future stock returns. Two proxies of intangible

information are selected: the first is as per the Table 4.10 and the next proxy is share

issuance measures. The numbers of observations decreases when the lag period increases.

In Panel A, 17/20 coefficients are significant at 5 percent risk and the relationship

between share issuance measures and the holding period returns is nil during whole

analysis (i.e. from lag 1 to lag 5). The direction of the relationship of intangible

information is negative in most cases. Interestingly, fifth year back database is more

useful than the recent database as seen in model 5 (i=5), the coefficient is 0.335

(negative). With this evidence, the historical information which is behind the curtain in

the recent period has significant predictive power than the recent and explicit information

is the finding of this relationship. Once again it is proved that the book value has no

contribution for calculation of holding period returns has to some extent contribution for

stock return movements but the

Table 4.12Regressions Analysis of Holding Period Stock Returns with Intangible Information

This table reports the regression results where dependent variable is holding period firm level stock returns and the independent variables are: fundamentals to price variables, fundamental returns, intangible information and share issuance measure. The B t-i/Pt-i

is the lagged book to price ratio for the period t-i to t, S t-i/Pt-i is the lagged sales to price ratio for the period t-i to t, C t-i/Pt-i is the lagged cash flow to price ratio for the period t-i to t, and Et-i/Pt-i is the lagged earnings to price ratio for the period t-i to t. α is constant. Other independent variables are: book returns (rB(t-i,t)), sales returns (rS(t-i,t)), cash flow returns (rC(t-i,t)), and earnings returns (rE(t-i,t)) respectively for Panel A to Panel D. The share issuance measure (ι (t-i,t)) and the intangible information measure (rI(B) when considering book to price ratio and book returns, rI(S) when considering sales to price ratio and sales returns, rI(C) when considering cash flow to price ratio and cash flow returns, and rI(E) when considering earnings to price ratio and earnings returns (as per Table 4.10 and its extension to calculate the other intangibles) are other independent variables in this table. Model Sig column indicates the p-values of ANOVA test, R-square is the coefficient of determination; N is the number of observations and K-S test column indicates the test of normality of the series. The study period includes 1997:07 to 2010:07. The t-statistics are presented in parenthesis.

  Α b0 b1 b2 b3 Model Sig R-squareK-S Test of Residuals(p)

N

Panel A: ri(t) = α + b0 BP (t-i) + b1 rB(t-i,t) + b2 rI(B) + b3 ι (t-i,t) + utModel 1(i=1)

bi -0.154) 0.317 -0.108 0.033 0.000 0.000 0.245 0.057 435t (-5.119) (8.705) (-3.794) (1.955) (-3.482)  

134

Model 2(i=2)

bi -0.016 0.174 -0.113 -0.124 0.000 0.000 0.372 0.178 356t (-0.550) (5.584) (-5.293) (-7.437) (-4.299)  

Model 3(i=3)

bi 0.107 0.038 0.050 -0.186 0.000 0.000 0.201 0.200 302t (2.506) (0.903) (1.359) (-4.860) (-3.936)  

Model 4(i=4)

bi 0.304 -0.249 0.049 -0.136 0.000 0.000 0.239 0.200 209t (5.670) (-4.275) (1.350) (-2.487) (-7.227)  

Model 5(i=5)

bi 0.513 -0.495 0.334 -0.335 0.000 0.000 0.291 0.200 173t (6.942) (-5.848) (2.478) (-4.954) (-5.536)        

Panel B: ri(t) = α + b0 SP(t-i) + b1 rS(t-i,t) + b2 rI(S) + b3 ι (t-i,t) + ut Model 1(i=1)

bi 0.027 0.000 0.000 0.020 0.000 0.000 0.253 0.053 445t (1.476) (10.643) (-1.553) (1.187) (-11.322)  

Model 2(i=2)

bi 0.066 0.000 0.000 -0.077 0.000 0.000 0.547 0.054 281t (4.535) (9.971) (5.758) (-6.205) (-13.031)  

Model 3(i=3)

bi 0.074 0.000 0.000 -0.119 0.000 0.000 0.338 0.200 296t (2.881) (8.533) (-0.696) (-3.347) (-7.948  

Model 4(i=4)

bi 0.117 0.000 0.000 -0.060 0.000 0.000 0.218 0.200 238t (3.312) (5.604) (-0.223) (-1.029) (-7.446)  

Model 5(i=5)

bi 0.108 0.000 0.000 -0.104 0.000 0.000 0.224 0.200 176t (2.478) (4.105) (2.239) (-1.545) (-5.804)        

Panel C: ri(t) = α + b0 CP(t-i) + b1 rC(t-i,t) + b2 rI(C) + b3 ι (t-i,t) + utModel 1(i=1)

bi 0.071 0.000 -0.001 -0.023 0.000 0.000 0.157 0.113 406t (4.549) (-0.779) (-1.651) (-1.584) (-8.370)  

Model 2(i=2)

bi 0.065 0.000 0.000 -0.137 0.000 0.000 0.482 0.200 288t (4.427) (4.444) (0.131) (-10.268) (-9.469)  

Model 3(i=3)

bi 0.141 0.000 0.000 -0.180 0.000 0.000 0.190 0.079 302t (5.445) (-0.198) (0.292) (-4.842) (-4.841)  

Model 4(i=4)

bi 0.132 0.000 0.000 -0.150 0.000 0.000 0.217 0.081 215t (4.402) (2.362) (1.439) (-2.749) (-6.283)  

Model 5(i=5)

bi 0.118 0.000 0.000 -0.193 0.000 0.000 0.152 0.200 168t (2.943) (0.722) (-0.757) (-2.839) (-4.073)        

Panel D: ri(t) = α + b0 EP(t-i) + b1 rE(t-i,t) + b2 rI(E) + b3 ι (t-i,t) + utModel 1(i=1)

bi 0.066 0.131 0.000 -0.021 0.000 0.000 0.206 0.100 383t (4.351) (2.746) (0.230) (-1.519) (-9.309)  

Model 2(i=2)

bi 0.090 0.051 -0.001 -0.129 0.000 0.000 0.446 0.065 296t (5.762) (1.215) (-1.489) (-9.823) (-8.884)  

Model 3(i=3)

bi 0.128 -0.042 0.000 -0.136 0.000 0.000 0.440 0.081 219t (6.702) (-0.621) (-1.301) (-5.561) (-8.960)  

Model 4(i=4)

bi 0.142 -0.009 0.000 -0.148 0.000 0.000 0.136 0.200 226t (3.616) (-0.056) (-0.035) (-2.434) (-4.821)  

Model 5(i=5)

bi 0.206 -0.735 0.000 -0.207 0.000 0.000 0.216 0.200 164t (4.333) (-3.181) (0.039) (-3.047) (-4.828)        

relationship is not consistent during the whole periods. In majority of the cases, the

relationship between book to price and firm level stock return is positive as opposite of

earlier findings but it is only retained till model 3 (or, 3 lag periods). The coefficients in

model 4 and model 5 are stronger than others and significant at 5 percent risk level, thus it

is concluded that initially the book to market ratio contributed positively for stock returns

and the magnitude started to decrease later. The plot of values of b0 parameters gives the

U-shape. Panel B and Panel C is the analysis of sales to price and cash flow to price effect

for holding period returns, the coefficients in most cases is zero and close to zero. Thus,

as earlier, it is concluded that the sales to price and cash flow to price ratio have no

predictive power for firm returns. Similar to Panel A, the intangible information has

negative effect for firm returns in this sub-section. In Panel D, 9/20 coefficients are

insignificant at 95 percent confidence. Likewise Panel A, in some cases, the earning to

price effect is positive and negative then after. Earnings returns have no predictive power.

Thus, the most powerful fundamental return is book returns followed by earnings returns

Source: Appendix B

135

is another findings of the analysis. Once again, the intangible information has negative

relation with firm returns proved by the values of parameter b2. In sum, the major findings

of Table 4.12 is the intangible information in majority of the cases pull down the stock

returns and rarely help to boost the firm level stock returns and when the lag period

increases, the strength of the relationship also inversely increases.

D. News and Political Effect on Stock Returns

News for the study is considered as the newspaper headings of the national daily

“Kantipur” and the classification is made as bad news, good news and informational as

per the content analysis approach. The study period covers 6029 days during 16 years, the

news headings related to the stock market for this period constitutes 1683 headings which

are classified in to 536 bad news, 734 good news and 413 informational news. Table 4.13

shows twelve regression models where the regression model 2 is significant at 9 percent

and the rest models are significant at two standard deviations. The yearly database in

Panel A proves the negative effect of bad news for average market returns similarly, the

informational news contents have also inconsistent and minimal effect for stock returns.

The coefficients of bad news and good news are significant at 5 percent but insignificant

for informational news contents. On the other hands, good news has positive and

significant explanatory power for average market returns during the study periods. The

average coefficient of determination in Panel A is 0.58 where as 0.26 for Panel B and

0.12 for Panel C which indicates that the explanatory power of the yearly database is

higher than monthly and daily. In Panel B, various regression models are formed and

analyzed. Model 4 independently shows the negative relationship of bad news with

average market returns. While adding good news in the model, the value of coefficient

again increases and reaches to -0.010 from -0.008. Similarly, the R-square value also

increases from 21.5 percent to 33.1 percent. Under Panel B, number of months varies

from 134 to 151 and the p-values for K-S test shows the series are normally distributed.

This panel also proves the similar findings as Panel A i.e. the negative effect of bad news,

the positive effect of good news and the inconsistent effect of

Table 4.13News Effect on Average Market Returns

This table presents the regression results between average market returns as dependent variable and the newspaper contents are classified into bad news, good news and information only as independent variables. Panel A shows the yearly effects, Panel B indicates monthly effect and Panel C exhibit the daily news effect on average market returns. The study period covers 1994:07 to 2010:07. In the table, Sig. column indicates the p-values of ANOVA test, R-square is the coefficient of determination; N is the number of observations and K-S test column indicates the test of normality of the series. The t-statistics are presented in parenthesis.

rm_avr = α + b0 bXt + b1 gXt + b2 iXt + uiModel Constant bXt gXt iXt Sig. R2 K-S N

136

Panel A: Yearly database

1bi 0.001 -0.014 0.012   0.00 0.710 0.200 16t (0.015) (-4.576) (5.674)  

2bi 0.076   0.005 -0.009 0.09 0.310 0.190 16t (0.424) (2.381) (-1.050)  

3bi 0.004 -0.014 0.012 0.000 0.00 0.710 0.200 16t (0.035) (-4.109) (5.429) (-0.032)        

Panel B: Monthly database

4bi 0.026 -0.008     0.00 0.215 0.200 146t (5.115) (-6.284)          

5bi 0.008 -0.010 0.007 0.00 0.331 0.200 151t (1.447) (-7.746) (6.741)        

6bi 0.001 0.003 -0.002 0.00 0.092 0.063 141t (0.155)   (3.748) (-1.036)      

7bi 0.026 -0.007 0.000 0.00 0.239 0.200 134t (4.822) (-6.118)   (-0.051)      

8bi 0.011 -0.011 0.008 -0.001 0.00 0.424 0.200 145t (1.853) (-9.135) (7.821) (-0.301)        

Panel C: Daily database

9bi 0.001 -0.006     0.00 0.116 0.200 1,331t (5.592) (-13.174)          

10bi 0.000 -0.004 0.003 0.00 0.134 0.126 1,253t (3.042) (-10.473) (8.635)        

11bi 0.000 -0.005 0.001 0.00 0.108 0.064 1,259t (4.582) (-12.097)   (2.438)      

12bi 0.000 -0.004 0.002 0.001 0.00 0.125 0.068 1,209t (3.351) (-9.743) (8.078) (2.152)      

Sources: Data from Appendix C, Appendix D and Appendix F(b)

informational news for the variation of average market returns during the study periods.

The daily database in Panel C also proves the similar conclusion that the daily bad news

adversely affect for the stock returns while daily good news have positive effect for the

market movements and when taking the daily informational news, its contribution seems

marginal and positive. In contrast to the findings in Panel A and Panel B, the daily

informational news effect has significant effect for average market returns even if the

strength is marginal. Thus, the overall conclusion of table 4.13 is that there is negative

effect of bad news contents for the stock market movements where as positive impact of

good news contents and the inconsistent effect of informational news for the market

returns during the whole periods.

The average market index is calculated as the mean of beginning stock market index plus

the closing stock market index and the average market return is calculated similar to the

holding period returns. On the other hands, the mid-July market returns is also calculated

similar to the holding period returns calculations. The analysis in Table 4.14 is classified

into three panels. Panel A shows the analysis of yearly database, Panel B for monthly

database and Panel C describe the relationship between news contents and the market

returns respectively. Regression model 2 among others is insignificant out of twelve

regression models. The average coefficient of determination in Panel A is 0.58 which is

similar to the average stock returns in Panel A of Table 4.13. The findings of this section

137

are: the negative effect of bad news and informational news whereas positive effect of

good news for the stock return movements. In Panel B, the relationships are almost

significant at 5 percent risk level. In most cases, the bad news pushes more than 1 percent

negative changes in stock returns whereas less than 1 percent returns changes and

marginal unit changes (negative) when the newspaper covers informational news. The

number of monthly database in Panel B ranges between 127 months and 149 months and

the average coefficient of determination is 44 percent and the K-S test values indicates

that the series are normally distributed. The coefficients in Panel C indicate that the given

series are normally distributed. All the regression coefficients are significant at 5 percent

risk level. The maximum number of observations is 1689 in model 12. The given

regression models are significant and the coefficient of determination in this panel are

relatively small. The average R-square in this Panel is 3 percent which is very lower than

the similar panel in Table 4.13 (i.e. 12 percent).

Thus, the major conclusion of news effect for stock returns is: there is negative effect of

bad news for stock returns. In most cases one unit of bad news headline leads 0.01 unit

negative change in market returns. The strength of relationship between stock returns and

good news is relatively weaker than bad news but the direction of relationship is

consistently positive i.e. good news leads less than 0.01 unit positive changes in market

returns. The informational news on the other hands has inconsistent and marginal effect

for the stock market movements in Nepalese capital market.

Table 4.14News Effect on Mid-July Market Returns

This table presents the regression results between average market returns as dependent variable and the newspaper contents are classified into bad news, good news and information only as independent variables. Panel A shows the yearly effects, Panel B indicates monthly effect and Panel C exhibit the daily news effect on average market returns. The study period covers 1994:07 to 2010:07. In the table, Sig. column indicates the p-values of ANOVA test, R-square is the coefficient of determination; N is the number of observations and K-S test column indicates the test of normality of the series. The t-statistics are presented in parenthesis.

rm_midjuly = α + b0 bXt + b1 gXt + b2 iXt + ui

Model Constant bXt gXt iXt Sig. R2 K-S NPanel A: Yearly database

1Bi 0.072 -0.020 0.015   0.00 0.750 0.200 16T (-0.926) (-5.837) (6.190)  

2Bi 0.241   0.006 -0.016 0.17 0.240 0.200 16T (1.044) (1.970) (-1.501)  

3Bi 0.141 -0.019 0.015 -0.004 0.00 0.760 0.200 16T (1.036) (-5.122) (6.075) (-0.625)        

Panel B: Monthly database

4Bi 0.031 -0.011     0.004 0.459 0.200 127T (7.308) (-10.294)            

5Bi 0.006 -0.013 0.010 0.00 0.595 0.200 137T (1.196) (-12.212) (11.590)          

6Bi -0.001 0.007 -0.006 0.00 0.228 0.200 141T (-0.094)   (6.289) (-2.581)        

7Bi 0.037 -0.009 -0.004 0.00 0.409 0.054 131T (6.822) (-7.930)   (-2.118)        

138

8Bi 0.019 -0.013 0.009 -0.004 0.00 0.489 0.200 149T (2.995) (-10.241) (8.906) (-1.818)        

Panel C: Daily database

9Bi 0.001 -0.002     0.00 0.026 0.142 1,674T (5.579) (-6.738)            

10Bi 0.000 -0.002 0.001 0.00 0.035 0.200 1,687T (4.534) (-6.297) (4.447)          

11Bi 0.001 -0.002 -0.001 0.00 0.029 0.128 1,673T (5.981) (-6.746)   (-2.220)        

12Bi 0.000 -0.002 0.001 -0.001 0.00 0.036 0.149 1,689T (4.722) (-5.880) (4.708) (-2.211)        

Sources: Data from Appendix C, Appendix D and Appendix F(b)

Table 4.15 and Table 4.16 present the relationship between political leadership and its

effect on market returns. Each table has three panels which explain yearly, monthly, and

daily database analysis from 1994:07 to 2010:07. The dependent variable is capital

market returns where yearly database constitute average market returns shows in Table

4.15 (i.e. annual average, monthly average and daily average) and end period (mid-July)

database consist of end of Nepali fiscal year, month end, and the closing daily market

index shows in Table 4.16. For all dummy analysis, the NC led government is treated as

reference group variable.

Table 4.15Political Leadership Effect on Average Market Returns

The table shows the relationship between the average market returns and the political leadership as dummy variable. D1: CPN-UML led government, D2: Other parties led government, D3: UCPN (M) led government where NC led government is treated as the base dummy variable. Panel A shows the yearly database and its effects, Panel B indicates monthly effect and Panel C exhibit the daily political leadership database and its effect. The dependent variable is average market returns. The study period covers 1994:07 to 2010:07. In this table, Sig. column indicates the p-values of ANOVA test, R-square is the coefficient of determination; N is the number of observations and K-S test column indicates the test of normality of the series. The t-statistics are presented in parenthesis.

rm_ave = α + b1D1 + b2D2 + b3D3 + ui

Model Constant b1D1 b2D2 b3D3 Sig. R2 K-S N

Panel A: Yearly database

1Bi 0.180 -0.283 -0.104   0.318 0.162 0.200 16

T (1.990) (-1.569) (-0.638)          

Panel B: Monthly database

2Bi -0.051 0.074 0.027 0.062 0.000 0.176 0.096 148

T (-3.422) (4.648) (1.386) (3.841)        

Panel C: Daily database

3Bi -0.002 0.003 0.000 0.002 0.000 0.088 0.063 1,239

T (-4.850) (6.863) (-0.478) (4.902)        Sources: Appendix E, Appendix F (a) and Appendix F (b)

Table 4.16Political Leadership Effect on Mid-July Market Returns

The table shows the relationship between mid-July market returns and the political leadership as dummy variable. D1: CPN-UML led government, D2: Other parties led government, D3: UCPN (M) led government where NC led government is treated as the base dummy variable. Panel A shows the yearly database and its effects, Panel B indicates monthly effect and Panel C exhibit the daily political leadership database and its effect. The dependent variable is average market returns. The study period covers 1994:07 to 2010:07. In this table, Sig. column indicates the p-values of ANOVA test, R-square is the coefficient of determination; N is the number of observations and K-S test column indicates the test of normality of the series. The t-statistics are presented in parenthesis.

rm_midJul = α + b1D1 + b2D2 + b3D3 + ui   

139

Model Constant b1D1 b2D2 b3D3 Sig. R2 K-S NPanel A: Yearly database

1Bi 0.185 -0.402 -0.041   0.198 0.220 0.200 16T (1.741) (-1.889) (-0.213)          

Panel B: Monthly database

2Bi -0.058 0.088 0.033 0.068 0.000 0.193 0.086 144T (-3.453) (4.910) (1.539) (3.727)        

Panel C: Daily database

3Bi -0.002 0.003 -0.001 0.002 0.000 0.086 0.111 1,715T (-4.483) (7.005) (-1.887) (4.719)        

Sources: Appendix E, Appendix F (a) and Appendix F (b)

Looking at yearly data analysis in Table 4.15, In Panel A, the CPN-UML led government,

on an average, contributes 0.283 percent negatively in average market returns per year

than those of NC led government while other parties led government contributes 0.104

percent negatively in average market returns per year than those of NC led government.

But, the ANOVA test value (p-value) is much higher than 0.05 thus the fitted model is not

significant even though the series is normally distributed. On the other hand, Panel B and

C give the different evidence that the first, second and third dummies have positive effect

for average stock returns as compare to reference category. Under Panel B, the CPN-

UML, Other parties and UCPN (M) leg government have the positive contribution for

average returns. More specifically, the CPN-UML government, on an average contributes

0.074 percent more than NC government. Similarly, Others parties’ makes on an average

0.027 percent and UCPN (M) makes on an average 0.062 percent more than the NC led

government for the average market returns during 1994:07 to 2010:07 while the daily

database also proves the similar findings. All coefficients in Panel B and C except one

each in both panels are significant at 5 percent significant level and both fitted regression

models are significant at 95 percent confidence. The series are normally distributed and

the numbers of observation are: 148 and 1239 respectively for monthly and daily analysis.

Thus, the major conclusion of this table is: there is lower contribution of the NC led

government for the market growth while CPN-UML and UCPN (M) leadership have on

an average positive contribution for average stock returns.

Further, Table 4.16 exhibit very similar results as Table 4.15 where the fitted regression

model based on year end database is not significant and the monthly and daily data based

regression models are significant at 95 confidence level. The regression coefficients and

its signs are very similar the above results thus, the evidence from Table 4.16 support the

major findings of Table 4.15.

The regression model which contains both the quantitative and qualitative variables is

termed as Analysis of Covariance (ANCOVA) model is shown in Table 4.17. The

140

quantitative variables are: the bad news, good news and the informational news while the

categorical variables are based on political leadership variables as independent variables.

Section A exhibit the average market returns whereas section B shows the end of the

period market returns as dependent variable respectively. Each section constitutes three

ANCOVA models in separate panel. The combination of news count with its

classification and the political leadership with its dummies constitutes 16 years, 153

months and 1245 days of observation for section A and 16 years, 148 months and 1671

days for section B respectively.

Table 4.17A Regression Analysis of Market Returns on News and Political Leadership from 1994:07 – 2010:07

This table presents the regression analysis between market returns (average & mid-July) and the news and political leadership is the dummy variables. D1: CPN-UML led government, D2: Other parties led government, D3: UCPN (M) led government where NC led government is treated as the base dummy variable. Panel A shows the yearly database and its effects, Panel B indicates monthly effect and Panel C exhibit the daily political leadership database and its effect in both sections. Section A is the analysis of average market returns whereas Section B for mid-July market returns. The dependent variables are average market returns and mid-July market returns for Section A and Section B respectively. The bad news, good news, informational news and the political dummies are the independent variables. The study period covers 1994:07 to 2010:07. In this table, Sig. column indicates the p-values of ANOVA test, R-square is the coefficient of determination; N is the number of observations and K-S test column indicates the test of normality of the series. The t-statistics are presented in parenthesis.

Section A: rm_ave = α + b0 bXt + b1 gXt + b2 iXt + b4D1 + b5D2 + b6D3 + ui Model

Pan

el A

: Y

earl

y da

taba

se

1

Pan

el B

: M

onth

ly d

atab

ase

2

Pan

el C

: D

aily

dat

abas

e

3  bi T bi T bi TConstant 0.041 (0.266) -0.028 (-1.656) -0.002 (-3.812)bXt -0.014 (-3.083) -0.009 (-6.032) -0.003 (-7.165)gXt 0.012 (4.269) 0.007 (6.245) 0.002 (8.312)iXt -0.001 (-0.183) 0.000 (0.184) 0.001 (2.081)b4D1     0.036 (2.197) 0.003 (5.500)b5D2 -0.002 (-0.012) 0.017 (0.912) 0.000 (-0.814)b6D3 -0.051 (-0.447) 0.035 (2.099) 0.002 (3.775)Sig. 0.014   0.000   0.000  R2 0.719   0.357   0.169  K-S 0.200   0.200   0.127  N 16   153   1,245  

Section B: rm_midJul = α + b0 bXt + b1 gXt + b2 iXt + b4D1 + b5D2 + b6D3 + ui Model

Pan

el A

: Y

earl

y da

taba

se

1

Pan

el B

: M

onth

ly d

atab

ase

2

Pan

el C

: D

aily

dat

abas

e

3  bi T bi T bi TConstant 0.148 (0.846) -0.012 (-0.748) -0.001 (-3.313)bXt -0.019 (-3.856) -0.013 (-8.944) -0.001 (-2.299)gXt 0.015 (4.759) 0.010 (8.881) 0.002 (6.485)iXt -0.004 (-0.570) -0.004 (-1.889) -0.001 (-2.792)b4D1     0.034 (2.158) 0.003 (6.116)b5D2 0.014 (0.083) 0.021 (1.164) -0.002 (-3.096)b6D3 -0.009 (-0.069) 0.026 (1.624) 0.001 (3.530)Sig. 0.007   0.000   0.000  R2 0.761   0.521   0.124  K-S 0.200   0.200   0.051  N 16   148   1,671  

Sources: Appendix C, Appendix D, Appendix E, Appendix F(a), and Appendix F (b)

The results suggests that, for every addition of bad news in the selected news paper leads

to mean decrease in average stock returns by about 1.4 percent (-0.014) with t-statics

greater than two standard deviation, for every good news, the average stock returns goes

up by 1.2 percent (0.012) which is significant at 5 percent risk level, and the

141

informational news headings has no significant effect for average stock returns which is

shown in Panel A, Section A of Table 4.17. Meanwhile, taking yearly database, the

political leadership is seen as unbiased or it’s not significant at 95 percent confidence.

When taking the monthly database, there is about equal (0.036 for D1 and 0.035 for D3,

both are significant) positive mean effect as compare to the NC led government. On the

other hands, for average stock returns, the others’ parties led government has similar

effect for average stock returns as compare to the reference NC led government.

Regarding the news effect, similar to Panel A, bad news has negative effect and the good

news has positive effect and both are significant even if the average strength is lower than

that of the coefficients in Panel A. While analyzing the daily effect of news and political

leadership effect on average market returns, the strength of the news (bad news, good

news and informational news) is gradually decreases as shown by -0.014, -0.009 and -

0.003 for yearly, monthly and daily database for bad news, respectively. Similarly, 0.012,

0.007 and 0.002 for good news in Panel A, B and C respectively which describes the

decreasing strength of news when it goes yearly to daily pattern but the t-statistics goes

up meaning the confidence level increases when analysis moves from yearly to daily

database. Thus, the major findings of this section are: the bad news has consistent

negative effect and the good news has consistent positive effect for average stock returns

but there is inconclusive effect of informational news for market returns, the daily news

as well as the leadership effect is more stronger than the monthly and yearly effects, in

general, the CPN-UML and UCPN (M) led government, on an average has positive effect

for the growth of stock market returns.

The section B in the Table 4.17 indicates the news and political leadership effect on end

period market returns. The section is divided into three panels namely, Panel A for the

analysis of yearly database, Panel B for the analysis of monthly database, and Panel C for

the analysis of daily database. The news categories and the political leadership dummies

are the independent variables whereas the dependent variable is mid-July or the end

period market returns. The results shows the consistent negative and positive effect of bad

news and good news for market movements and as oppose to the previous findings in

Section A, the effect of informational news is negative for all panels but it is only

significant at model 3 i.e. daily database. The major differences between the average

market returns and the end period market returns as dependent variable for news effect

analysis are: the yearly end period data have strong effects (-0.014 vs. -0.019, 0.012 vs.

0.015, and -0.001 vs. -0.004) than that of average market returns, the similar results for

142

monthly database but both the strength and the confidence level (-0.009 vs. -0.013, 0.007

vs. 0.010, and 0.000 vs. -0.004 (coefficients), and -6.032 vs. -8.944, 6.245 vs. 8.881, and

0.184 vs. -1.889 (t-statistics)) are higher for end period analysis, and the monthly

database explain more about the end period market returns than that of daily database in

Section A. When talking about the leadership effect for end period market returns, it is

proved that the UCPN (M) led government on an average has more strength to boost the

market growth as compare to NC led government (reference category) and the

coefficients for this variable in Section A and Section B have no differences, the mean

effect of other parties led government has negative effect for end period market returns as

compare to NC leg government under daily database (this result is not significant in

Section A), and, there is no significant differences between NC led government and

UCPN (M) led government for market growth taking the monthly series. But, in sum, the

UCPN (M) led government is placed as more supportive government for market

expansion as compare to NC led government. While looking at the monthly series under

Section B, it can also be interpreted as: irrespective of the absence or presence of news

counts with its categories, the presence of CPN-UML led government is estimated to

increase the end period market returns by an average 3.4 percent; the UCPN (M) led

government is estimated to increase the end period market returns by an average 2.6

percent which is significant at less than 5 percent risk level. But, the coefficients in

Section A (Panel B) are 3.6 percent and 3.5 percent respectively for CPN-UML and

UCPN (M) leg government. Thus, the major conclusions of this section are: the monthly

series have more predictive power than yearly and daily, the bad news, good news and

informational news have consistent negative, positive and negative effect for stock returns

respectively, and the CPN-UML led government is proved to be the stock market friendly

government.

From the above analysis, the major conclusions regarding the news effect are: the bad

news are the market growth barriers and the good news are market growth friendly news

categories, and out of the experienced political leadership in Nepalese context, the CPN-

UML leg government is proved as more constructive government for the market growth

as compare to the NC led government.

E. An extended analysis for the news and stock returns: the graphical presentation.

In Figure 4.2 the pattern of total news along with its categories for the period 1994 to

2010 is presented, moreover, the market returns series are also seen in the upper part of

143

the first graph. Even though the trend line of all the series are upward sloping but the

slope of the market returns

Sources: Appendix C and Appendix D

is steeper than the news counts. Except the period 1999 to 2002, the counts of news

headings follow the market returns patterns. During the whole period, two peaks are seen

where the highest is the corresponding of the fiscal year 2008. The end of the period

market returns patterns shows less smooth movement than that of average market returns

Figure 4.2 The figure below present the graphical presentation of the variables: end of the year market returns, average market returns, total news count and its classification as: bad news count, good news count and informational news counts. Further, the graphs in the second row shows the pattern of average market return (yearly) and the mid-July market returns, and the last table shows the graph of the news categories: bad news, good news and informational news and its total counts. The figures are based on the news headings counts from 1994:07 to 2010:07 and the market returns also based on the same time frame.

144

patterns. Among the news categories, in general, the numbers of good news counts are

greater than bad news and the bad news categories are always higher than the

informational news headings in the selected newspaper for the study. From the graphical

presentation, it is proved that there is linear (positive) relationship between news headings

and the stock market returns, and, good news headings dictates the other categories of

news headings in most of the time during the study period.

Source: Appendix F

The spread of the stock market returns is presented in Figure 4.3. The figure is drawn

based on the daily database of stock market, the initial three years (1994 to 1996) has

limited database due to the unavailability of needful secondary data source representing

the year end index. From 1997 to 2010, the graph is based on the daily database. By the

inspection, the year 2007 experienced the most fluctuation followed by the year 2008.

The graph presents clear two cycles during the study period (i.e. from 1997 to 2002 and

from 2006 to 2010) with clear growth, peak, contraction and trough. Similarly, more or

less the same pattern is followed by the financial news counts.

The insight from the Figure 4.2 and 4.3 poses that when the market parameter tends to

move on the upward basis, the number of financial news count on the national daily also

increases and vis-à-vis but the pattern is not reliable when the number of news count does

not follow the market pattern during 1997 to 2002 but it is from 2006 to 2010. With the

same fashion, when the stock market reached to the peak its spread also poses the highest

Figure 4.3 The figure shows the spread of market returns for the period 1994:07 to 2010:07. Daily market returns is shown in y-axis and time in x-axis. The spikes in the figures indicate the range (minimum to maximum) of the returns for each year.

145

ranges for the year 2000 but the similar inference does not retain for the next cycle. For

the period 2006 to 2010, the market peak is seen in 2008 but the highest spread is seen in

2007. Thus, the inference from the above graphical presentation is that there is no reliable

pattern of the variables measured; there is no guarantee of the linear movement of the

stock market returns and correspondingly the market spread; moreover, the analysis of

news and the market returns also does not clarify whether the news leads to market

returns or market leads to the news counts.

4.2 Primary Data Analysis

The primary data analysis has been classified into two parts, the first part is more focus

towards the demographic characteristics of the respondents and the next section discuss

about the procedure of the factor analysis.

A. Profile of Respondents

Table 4.18 given below exhibit the characteristics of the individual respondents in the

Nepalese capital market in relation to their gender, age, occupation, education, level of

investment and experience of investing. Panel A shows majority of the stock investors are

male and Panel B shows majority are middle age group (i.e. 25 to 40 years) investors. By

profession, the businesspersons involvement is in the first place followed by the service

holders whereas the people who has only the business of investing in the stock market are

placed in the third place considered 22.6 percent of the total respondents. Most of the

investors are well educated as evidence that masters degree & above represent about 44

percent and the investors having bachelor degree are about 37 percent of the total

respondents. From the opinion regarding the level of stock investors, the Panel E

indicates that investors with less than 5 lakh of stock investors constitute 31.1 percent

which indicates that majority of stock participants in are small investors followed the

investors with the investment level 10 to 25 lakh. Panel F presents the investment related

work experience of the survey participants. The experience is classified into five

categories: less than 1 year, 1 to 5, 5 to 10, 10 to 17, and above 17 years. The opinion poll

indicates that majority of the respondents have 1 to 5 years of investing experience i.e.

about

Table 4.18Profile of the respondents based on personal characteristics

This table reports the personal characteristics of stock investors. Panel A indicates the gender, Panel B for age, Panel C for occupation, Panel D for education, Panel E for stock investment (size) and Panel F presents the work experience of the respondents. The numbers of respondents included for the survey are 164.

Variables Demographic Characteristics Number Percentage

146

Panel A: Gender

Female 12 7.3 Male 152 92.7 Total 164 100.0

 Panel B: Age of respondents

Below 25 13 7.9 25 to 40 100 61.0 Above 40 51 31.1 Total 164 100.0

 Panel C: Occupation

Business 47 28.7 Farmer 2 1.2 Investor 37 22.6 Service 45 27.4 Student 11 6.7 Teacher 9 5.5 Undisclosed 13 7.9 Total 164 100.0

 Panel D: Education

Up to high school 10 6.1 Intermediate 13 7.9 Bachelor degree 61 37.2 Master degree & Above 72 43.9 Undisclosed 8 4.9 Total 164 100.0

 Panel E: Stock investment (size)

Less than Rs 5 lakh 51 31.1 5 to 10 27 16.5 10 to 25 39 23.8 More than 25 lakh 37 22.6 Undisclosed 10 6.1 Total 164 100.0

 Panel F: Experience

Less than 1 year 9 5.5 1 to 5 years 88 53.7 5 to 10 years 43 26.2 10 to 17 years 14 8.5 Above 17 years 5 3.0 Undisclosed 5 3.0 Total 164 100.0

Source: Responses on survey questionnaire in Appendix I

53.7 percent followed by 26.2 percent for 5 to 10 years of capital market related investing

experience whereas the number of new investors who have less than 1 years of stock

investing practice. The number of total respondent are 164 whereas 13, 10, 8 and 5

respondents do not want to disclose their information regarding occupation, level of stock

investment, education level and the work experience respectively.

Table 4.19Investor Education and Personality Profile

This table shows the frequency distribution for investor education and personality profile. Panel A presents the response on the investor’s initial education for stock investment. Panel B indicates the respondents’ interest to participate in investor education program. The Panel C is for the preference towards the investment consultancy and Panel B shows the self-decided personality type of the respondents. Total 164 respondents are included in the following distribution table.

Panel A: How do you learn the basic, how to invest? From….Options Number PercentageFamily Members 19 11.59 Friend Circle 53 32.32 Myself (learning by doing) 49 29.88

147

Education & Training 41 25.00 Undisclosed 2 1.22 Total 164 100.00

 Panel B: Do you want to participate in investor education program?

Options N Number Percentage RankTraining program 164 56 34.15 2nd

Discussion forum 164 100 60.98 1st

Interactive talk show 164 31 18.90 3rd

Expo/Exhibition 164 26 15.85 4th

 Panel C: Do you want to receive consultancy services?

Options Number PercentageYes 112 68.29 No 41 25.00 Undisclosed 11 6.71 Total 164 100.00

 Panel D: What type of investor are you?

Options Number PercentageCautious 26 15.85 Methodical 25 15.24 Spontaneous 12 7.32 Individualist 91 55.49 Undisclosed 10 6.10 Total 164 100.00

Source: Responses on survey questionnaire in Appendix I

The major inferences from Table 4.18 are: the female stock practitioners have just started

to enter into market, the population of middle age people are higher and the education

level of the investors reliably high whereas the volume of investment indicates majority

are small investors having less than 5 years of experience. The evidence of experience as

of less than one year indicates that lately there is less attraction of investors in the market.

Table 4.20Investor Preferences

This table shows the preference of the stock investors towards the market type in Panel A, return preference in Panel B and the factors influencing investment decision in Panel C. The column indicates the options for each survey questions, the frequency of the investor response and the percentage of responses (response # / total respondent * 100). Total numbers of respondents are 164.

Panel A: Which market do you normally prefer?Options Number PercentagePrimary market 17 10.37Secondary market 41 25.00Both 106 64.63Total 164 100.00

 Panel B: Returns preference

Options Number PercentageCash dividends 37 22.56Increase in market price 92 56.10Stock dividends 28 17.07Others 1 0.61Undisclosed 6 3.66Total 164 100.00

 Panel C: The factors influencing investment decision

Options Number PercentageFamily 11 6.71

148

Friends 36 21.95Relatives 1 0.61Media 82 50.00Brokers 10 6.10Market trend 4 2.44self analysis 15 9.15Undisclosed 5 3.05Total 164 100.00

Source: Responses on survey questionnaire in Appendix I

Investor education and the personality profile is presented in Table 4.19 where the initial

days of the investors when stepping into the market for stock trading is tried to dig in

Panel A. Based on the survey, 32.32 percent respondents learnt from their friend circle

followed by learning by doing (29.88 percent) where as 25 percent respondents learnt

from their education and trainings. Panel B shows the investors’ preference towards the

investor education program where discussion forum is placed in first position as 60.98

percent preferred this option followed by the training program which is liked by 34.15

percent, the interactive talk show is in third rank and expo/exhibition is the least preferred

among the given options. Majority of the investors preferred to receive the investment

consultancy services as shown in Panel C and finally, Panel D compile the responses of

stock investors regarding their self-decided personality type.

The provided personality types are: cautious- exhibit strong desire for financial security and

is overly careful investor, methodical - Investor who believe on research and rarely form

emotional attachment to the investments, spontaneous - Investor form the portfolio with the

latest hot investment, trade frequently so that the trading cost is too high and individualist -

Investor do the sufficient homework and confident in own abilities where individualist

personality type is placed in the first rank (55.49 percent) followed by cautious

personality types (15.85) and the least preferred once is the spontaneous type. Thus, the

survey finding shows that Nepalese stock investors do the sufficient homework and they

are confident in their own ability while investing in the volatile stock market.

Table 4.21Investor trading behavior

This table shows the investor trading behavior: frequency of monthly trading in Panel A, selection of stock brokers Panel B and the reasons of dealing with more than one broker are presented in Panel C. Total 164 respondents are participated for the poll.

Panel A: How often do you trade securities in a month? Options Number Percentage

0 to 2 40 24.392 to 10 86 52.44Undisclosed 38 23.17Total 164 100.00

  Panel B: I usually work with …….. Brokers

Options Number PercentageOne 58 35.37

149

Two 53 32.32Three 25 15.24Four 8 4.88Five 4 2.44Six 1 0.61Seven 1 0.61Undisclosed 14 8.54Total 164 100.00

 Panel C: Reasons of dealing more than one broker?

Options N Number PercentageTo get more information 164 59 35.98To get share certificate asap on buying 164 14 8.54To collect sum asap on selling 164 43 26.22To develop the public relation 164 36 21.95For convenience 164 14 8.54Do not satisfied with service 164 1 0.61Brokers are less informative 164 1 0.61Others 164 2 1.22

Source: Responses on survey questionnaire in Appendix I

Further, the investor preference based on their judgment on the market preferences, return

preferences, and factors influencing investment decisions are presented in Table 4.20.

Based on the market preference, it is seen that secondary market is the preferred one but

majority of the investors select both types of market mechanism for investment activities.

Panel B on the other hands shows the stock return preference of the stock investors.

About 56 percent respondent preferred the increment in market price followed by cash

dividend 22.56 percent whereas stock dividend is preferred by 17.07 percent of the total

respondents. Media and the friend circle mostly influence the investors’ decision making,

in figure, 50 percent and about 22 percent respectively. Interestingly, only the 9.15

percent of the total respondents do the self analysis while making the investment

decisions. Therefore, the major conclusion of this table is: in the secondary market

majority of the stock investors prefer increase in stock price rather than the fundamental

cash dividend and the media plays crucial role for investment decision making followed

by the friend circle of the stock investors.

Table 4.21 presents the investor trading behavior of stock investors namely, the trading

frequency, selection of brokers and the reasons of selecting more than one brokerage firm

respectively. Panel A shows that majority investors trade 2 to 10 times in a month and

about 24 percent trade below 2 times where the number of investors who do not want this

information is about 23 percent. Panel B shows the selection of brokerage firms for

trading where most of the respondents prefer only to work with one brokerage whereas

32.32 percent investors like to work with two brokers at the same time. The reasons of

dealing with more than one broker are tried to indentify in Panel C where majority of the

respondents stress to get more information (35.98 percent) followed by to collect the

150

money as soon as possible while selling and the third reason based on the survey result is

to develop the public relation. To identify these reasons total 164 respondents and their

responses are analyzed.

Investor trading practices on the various aspects like: trading instruction, execution of

order, time limit, practice of setting time limit, and the time period to receive the share

certificate is presented in Table 4.22. Panel A shows that majority investors i.e. 45.73

percent prefer the limit order or instruct the price range for trading followed by prevailing

market price i.e. 37.20 percent. Panel B indicates majority of trading orders are executed

in the specified price and at the specified quantity. About 63 percent of investors specify

the time limit in order as shown in Panel C while the day order (35.37 percent) followed

by week order (28.66 percent) respectively in numeric form presented in Panel D. The

Panel E gives the clear indication that receiving a share certificate is a very difficult task

in a short time period. Most of the investors i.e. about 84 percent indicates that it takes 20

days and above to get the share certificate after the transaction day.

Table 4.22Investor trading practices

This table reports the investor trading practices. The analysis is classified into five panels. Panel A is about the trading instruction for brokers, Panel B seeks the answer for: how often the orders are executed at specified price and quantity? Panel C is about the time limit in order, Similarly, Panel D is about the practice of setting time limit in order and Panel E is about the time to get the share certificate after transaction day. Total 164 responses are included in this analysis.

Panel A: Trading instructionOptions Number PercentageIn prevailing market price 61 37.20In a limit order (price range) 75 45.73At a fixed price 25 15.24Undisclosed 3 1.83Total 164 100.00

Panel B: How often the orders are executed at specified price and quantity?

OptionsAt specified price At specified quantity

Number Percentage Number PercentageFrequently 102 62.20 97 59.15Rarely 53 32.32 57 34.76Never 5 3.05 4 2.44Undisclosed 4 2.44 6 3.66Total 164 100.00 164 100.00

Panel C: Do you specify time limit in order?Options Number PercentageYes 103 62.80No 57 34.76Undisclosed 4 2.44Total 164 100.00

Panel D: The practice of setting time limit in order?Options Number PercentageDay order 58 35.37Week order 47 28.66Month order 1 0.61Open/Good-till-cancelled 1 0.61Undisclosed 57 34.76Total 164 100.00

Panel E: How long did it take to get share certificates after transaction day?Options Number Percentage

151

Within 5 days 2 1.225 to 10 days 11 6.7110 to 20 days 12 7.3220 days and above 138 84.15Undisclosed 1 0.61Total 164 100.00

Source: Responses on survey questionnaire in Appendix I

Table 4.23Sources and costs of funds

This table indicates the sources and cost of fund for investors. Panel A seek the answer for How do you manage funds for investment? and Panel B seeks the rate of interest on borrowing in case the investor borrow fund for investment. The numbers of respondents included for the analysis are 164.

Panel A: How do you manage funds for investment?Sources N Frequency Percentage Personal saving 164 103 62.80 Sale of properties 164 11 6.71 Non-interest paying borrowing 164 20 12.20 Interest paying loans 164 61 37.20 Margin loans 164 42 25.61

 Panel B: Rate of interest on borrowings

Cost of fund Number PercentageBelow 10 percent 4 2.4410 to 15 30 18.2915 to 20 56 34.15More than 20 percent 6 3.66Undisclosed 68 41.46Total 164 100.00

Source: Responses on survey questionnaire in Appendix I

In Table 4.23, under Panel A, five sources of funds: personal saving, sale of properties,

non-interest paying borrowing, interest paying loans and margin loans. The individual

responses shows that about 63 percent investors use their own personal saving followed

by borrowing interest paying loans (37.20 percent), the third source of fund for

investment is margin loans (25.61 percent) whereas the least preferred source of fund is

sale of properties constitute 6.71 percent. Panel B represent the frequency of responses on

the rate of interest on borrowings. Most of the investors do not want to disclose such

information as shown in table that 41.46 percent of total investors in this categories

whereas 35.15 percent respondent indicates that the rate of interest is 15 to 20 percent

followed by 18.29 percent investors choose the option 10 to 15 percent as the cost of fund

for investing.

The major aim of Table 4.24 is to identify five most preferred information which the

investor usually collect then while making investment decisions. Respondents are

requested to rank their preference as 1st, 2nd, 3rd, 4th and 5th. The collected information are

analyzed and presented in the table above which suggest that dividend is placed in 1 st

152

rank, book value per share in 2nd rank, earnings in 3rd rank, average stock price in 4rd

position and the number of equity is placed in the 5 th position. Moreover, Only 2.4

percent of respondents go beyond the given option as management and 0.6 percent

preferred economic growth as major factors influencing investment decisions making.

Thus, the major findings of Table 4.24 is the most important five information that

investors usually consider prior investing are dividends, book value per share, earnings,

average stock prices, and the number of equities.

Table 4.24The preferred information prior investing

This table presents the list of preferred information prior investing. The first column gives the selected list of information generally considers prior to investment decision and the rest column indicates the investors ranking in number and in percentage. The other information if investors considered for making investment are placed in undisclosed row. The numbers of respondents included in this analysis are 164.

VariablesFirst Second Third Fourth Fifth

Number

% Number %Numbe

r% Number %

Number

%

Dividends 89 54.3 26 15.9 17 10.4 6 3.7 5 3.0Book value per share 18 11.0 68 41.5 23 14.0 15 9.1 4 2.4Earnings 30 18.3 31 18.9 64 39.0 9 5.5 1 0.6Average stock prices 4 2.4 6 3.7 14 8.5 41 25.0 10 6.1Cash-flow 1 0.6 1 0.6 11 6.7 13 7.9 15 9.1Age of the firm 3 1.8 - - - - 10 6.1 11 6.7Debt/Equity ratio 5 3.0 5 3.0 9 5.5 24 14.6 14 8.5Private information 3 1.8 3 1.8 6 3.7 6 3.7 18 11.0Number of equity 3 1.8 9 5.5 3 1.8 20 12.2 48 29.3Pol. parties led govt. 1 0.6 1 0.6 3 1.8 4 2.4 20 12.2Undisclosed 7 4.3 14 8.5 14 8.5 16 9.8 18 11.0Total 164 100.0 164 100.0 164 100.0 164 100.0 164 100.0

Source: Responses on survey questionnaire in Appendix I

Table 4.25 presents the investor risk perception on the various issues like: risk preference,

management of risk, optimal number of enterprises for diversification, and the time for

revision of portfolio. Based on the responses of the stock investors in Panel A, the

changes in fundamentals or the firm specific variables is the most important variable for

generating risk in investors mindset. The change in monetary policies is placed in second

position. Similarly, the changes in capital market policies through the end of SEBON is

placed in the third row and changes in macro-economic factors in forth. Finally, news and

the media coverage are placed in the least risk generating factor whereas about 14 percent

respondents are not interested to disclose this information.

Panel B shows the opinion results on the management of the risk on investment where the

option – investing in different sectors and different companies is the most preferred as 77

percent respondents like this option followed by 17.7 percent for investing in same sector

153

and different companies. The least preferred option is investing in only a single company.

In Panel B, the optimal number of companies for well diversification is 5 to 10 companies

(44 percent) followed by 10 to 20 companies (30.5 percent). Panel C in the next part

shows investors generally change their portfolio once in 3 months.

Table 4.25Investor's risk perception

This table presents the investor’s risk perception due to the changes in the micro and macro economic factors along with the news & media effect and due to the role of regulatory authority in Panel A. Panel B collect the opinion of investors for managing risk on investment. Panel C shows the number of optimum companies for better diversification and the Panel D indicates the tentative time taken for the revision of portfolio. In total 164 responses are collected and analyzed for this analysis.

Panel A: Investor’s risk perception

VariablesFirst Second Third Fourth Fifth

Num. % Rank Num. % RankNum

.% Rank Num. % Rank Num. % Rank

Change in macro-economic factor24 14.6 4th 25 15.2 4th 23 14.0 5th 28

17.1

3rd 34 20.7 3rd

Change in fundamentals45 27.4 1st 31 18.9 2nd 25 15.2 4th 30

18.3

2nd 11 6.7 5th

News & Media coverage26 15.9 3rd 20 12.2 5th 28 17.1 3rd 18

11.0

4th 44 26.8 1st

Change in monetary policies32 19.5 2nd 35 21.3 1st 32 19.5 1st 31

18.9

1st 12 7.3 4th

Change in capital market policies (SEBON) 20 12.2 5th 27 16.5 3rd 30 18.3 2nd 31

18.9

1st 37 22.6 2nd

Undisclosed17 10.4   26 15.9   26 15.9   26

15.9

  26 15.9  

Total 164 100   164 100   164 100   164 100   164 100  

Panel B: Managing risk on investment

OptionsNum

. %

Investing in same sector, different companies 29 17.7

Investing in different sectors, different companies 126 76.8

Investing in only a single company 1 0.6

Spontaneous buying 2 1.2

Averaging price 2 1.2

Undisclosed 4 2.4

Total 164 100

Panel C: For diversification, what is the optimal number of enterprises?

Below 5 companies 12 7.3

5 to 10 companies 72 43.9

10 to 20 companies 50 30.5

20 and above companies 24 14.6

Undisclosed 6 3.7

Total 164 100

Panel D: Revision of portfolio

Once in 3 months 67 40.9

Once in 6 months 44 26.8

Once in a year 27 16.5

Do not revise 21 12.8

Undisclosed 5 3.0

Total 164 100

Source: Responses on survey questionnaire in Appendix I

Table 4.26Investor's perception and awareness level

The table shows the investor’s perception and the investor’s level of awareness. Panel A indicates the investor’s

154

perception on MF, CDS, CRA and PMS (where, MF = mutual fund, CDS = central depository system, CRA = credit rating agency, and PMS = portfolio management service). The scale for this panel is: not important, less important, neutral, important and most important (in order). Similarly, Panel B measures the awareness level using the scales as: not aware, less aware, neutral, aware and highly aware respectively. Total numbers of respondents for this analysis are 164.

Panel A: Investor's perception

OptionsMF CDS CRA PMS

Number % Number % Number % Number %Not important 9 5.5 9 5.5 8 4.9 10 6.1Less important 7 4.3 8 4.9 11 6.7 8 4.9Neutral 14 8.5 10 6.1 19 11.6 21 12.8Important 58 35.4 51 31.1 53 32.3 59 36.0Most important 61 37.2 70 42.7 49 29.9 47 28.7Undisclosed 15 9.1 16 9.8 24 14.6 19 11.6Total 164 100 164 100 164 100 164 100

Panel B: Investor's Awareness

OptionsMF CDS CRA PMS

Number % Number % Number % Number %Not aware 25 15.2 17 10.4 33 20.1 24 14.6Less aware 10 6.1 16 9.8 18 11.0 20 12.2Neutral 21 12.8 22 13.4 29 17.7 30 18.3Aware 60 36.6 64 39.0 44 26.8 45 27.4Highly aware 37 22.6 33 20.1 26 15.9 30 18.3Undisclosed 11 6.7 12 7.3 14 8.5 15 9.1Total 164 100 164 100 164 100 164 100

Source: Responses on survey questionnaire in Appendix I

In Panel A, the MF and CDS are considered as the most important mechanism for the

market development where 37.2 percent respondents and 42.7 percent respondents select

the option ‘most important’. Similarly, 32.3 percent for CRA and 36 percent for 36

percent of the total respondents select the option ‘important’ for market growth and

development. Panel B on the other hands, measure the investor awareness level on the

stated MF, CDS, CRA and PMS where most of the investors preferred to select the option

‘aware’ on all of the stated mechanism followed by ‘highly aware’ option but 20 percent

of the total investors state that they are not aware regarding CRA. Thus, the major finding

of this table is that even though investors perceive MF, CDS, CRA and PMS are most

important mechanism for market growth and development but they are not highly aware

on any of them so that it indicates the need of investor education on these specific areas.

Table 4.27Investor Reactions on capital market issues

This table shows the trading strategies in different situations. There are 10 statements/situations are given with the buy/sell/don’t trade option. The last column indicates the number and percentage of the undisclosed respondents. In all rows, total numbers of respondents are 164.

Situations NBuy Sell Don’t trade Undisclosed

Number

% Number % Number % Number %

a) Before the AGM of the enterprise (at the end of the fiscal year) 164 78 47.6 28 17.1 41

25.0

17 10.4

155

b) When the AGM is likely to announce the cash dividend 164 101 61.6 23 14.0 26

15.9

14 8.5

c) When the AGM is likely to announce the right shares 164 49 29.9 55 33.5 40

24.4

20 12.2

d) When the AGM is likely to announce the bonus shares 164 123 75.0 16 9.8 10 6.1 15 9.1

e) When the quarterly reports of the enterprise show increase in profit volume

164 107 65.2 11 6.7 3018.

316 9.8

f) When the enterprise launch the new product/service in the market 164 44 26.8 10 6.1 88

53.7

22 13.4

g) When the enterprise issue bonds in the market

164 17 10.4 30 18.3 9557.

922 13.4

h) When the media frequently cover the positive news of certain enterprise

164 93 56.7 16 9.8 3521.

320 12.2

i) When the media coverage highlight the merger news of the enterprises

164 21 12.8 29 17.7 8551.

829 17.7

j) In case if financial institution, when NRB going to declare crisis ridden institution

164 2 1.2 128 78.0 2012.

214 8.5

Source: Responses on survey questionnaire in Appendix I

The investor reactions regarding the trading strategies on the various market situations are

presented in Table 4.27. The total numbers of respondents are included for this analysis is

164 where most of the investors (i.e. 75 percent) want to buy the stocks when the

upcoming AGM of the enterprise is likely to announce the bonus shares, followed by

about 65 percent investors want to buy the stocks when the quarterly report of the

enterprises show the increase in profit volume. Similarly, when the AGM is likely to

announce the cash dividend about 62 percent investors want to buy those stocks and very

few (only 1.2 percent) investors want to buy stock in case of financial institution, when

NRB is going to declare crisis ridden institution. Under the selling strategy, majority (78

percent) of stock investors want to sell their stock in case of financial institution, when

NRB is going to declare crisis ridden institution, followed by 55 percent of total

respondents want to sell their stocks when the upcoming AGM is likely to announce the

right shares, similarly, 30 percent of total respondents want to sell their stock when the

enterprise issue bonus share in the market. In the next column, the do not trade strategies

and its frequency is presented in the table where majority (58 percent) do not want to

trade when the enterprise issue bonds in the market, followed by 54 percent investors

don’t want to trade when the enterprise launch the new product/service in the market.

Table 4.28 exhibit the investor judgment on the various issues and the evidences of the

previous studies. In all rows, the maximum number of respondents is 160 (in Panel A)

and the minimum is 155 (in Panel B). In Panel A, investors agree on the only one issue

156

i.e. ‘news events lead some investors to react quickly’ with the mean value 1.170 and for

rest four issues, most of the investors raise their opinion towards the disagreement. While

looking at mean figure, the highest in the disagreement section is 1.875 followed by 1.792

and 1.731 and 1.658 respectively. With these opinions, it is concluded that investors are

poses disagreement on the statement ‘investing in IPO is more risky than investing in

secondary market’, ‘seasonal offerings do not maximize the shareholders’ wealth’, ‘the

most frequent trading is harmful for investors; wealth’, and ‘if reliable private

information is available, it would be better to invest in single security’ and they tend to

agree on the statement ‘news events lead some investors to react quickly.’ Under Panel B,

the investor judgment on the evidences of the previous studies is presented where the

likert scale is designed into 4 points. The values allocated for the scale are as: strongly

agree (1), agree (2), disagree (3) and strongly disagree (4). Out of the total respondents

only 96.3 percent investors respond for statements b and d. The mean value if greater than

2 indicates the responses move towards the disagreement section whereas when mean

value is less than 2 indicates the responses move towards the agreement section. The

highest mean value is 2.523 shows that most of the respondents express their

disagreement on ‘high information uncertainty enhance the investor’s overconfidence’

followed by the mean value 2.380 for ‘media effect, market noise, seasonal effect, etc

strongly enhance the investor’s overconfidence’ where as the mean value for ‘stock

market exhibit higher returns following good news and lower on bad news’ is 1.904

indicates the investors are agreed on the essence of the evidence. Thus, based on the

views of the stock investors in Panel B, it’s concluded that investors are agreed on the

evidence ‘stock market exhibit higher returns following good news and lower returns

following bad news’ and disagreed on ‘investors under-react to publicly available

information and overreact to perceived private information’; ‘investors respond

mistakenly in initial phase of the information disclosure’; ‘the media effect, market noise,

seasonal effect, etc strongly influence men investor but not for women’ and ‘high

information uncertainty enhance the investor’s overconfidence’ where the magnitude of

agreement and disagreement are in the order as the statements written above.

Table 4.28 Investor Judgment on various issues and evidences

This table presents the investor judgment on the various issues and evidences of the studies. Panel A incorporates the investor judgment on the various issues with clearly indicating the number of respondents in second column. The mean value is presented in the next column and final column indicates the rate of respondents on each row out of total 164 respondents. Panel B further extend the scale into 4 points and its statements are the evidences of the various studies in the area of capital market in the international arena.

Panel A: Investor judgment on the various issues

157

Statements N MeanAgree Disagree I don't know Total

%Num. % Num. % Num. %

a) Your judgment on "investing in IPO is more risky than investing in Secondary market" (Loughran and Ritter, 1995)

160 1.875 22 13.4 136 82.9 2 1.2 97.6

b) Your judgment on "seasonal offerings do not maximize the shareholders' wealth"

160 1.731 48 29.3 107 65.2 5 3.0 97.6

c) Your judgment on "if reliable private info., it would be better to invest in single security"

158 1.658 60 36.6 92 56.1 6 3.7 96.3

d) Your judgment on "the most frequent trading is harmful for investors' wealth"

159 1.792 42 25.6 108 65.9 9 5.5 97.0

e) Your judgment on "news events lead some investors to react quickly" (Klibanoff, et.al, 1998)

159 1.170 139 84.8 13 7.9 7 4.3 97.0

Panel B: Investor judgment on the various evidences

Statements N Mean

Strongly agree Agree Disagree Strongly disagree Total

%Num. % Num. % Num. % Num. %

a) Your response on "stock market exhibit higher returns following good news and lower on bad news" (Zhang, 2006)

157 1.904 52 31.71 74 45.12 25 15.24 6 3.66 95.7

b) Your response on "media effect, market noise, seasonal effect, etc strongly influence men investor but not for women"(Biais et.al, 2005)

158 2.380 33 20.12 42 25.61 73 44.51 10 6.10 96.3

c) Your response on "high information uncertainty enhance the investor's overconfidence" (Jiang et.al, 2004)

155 2.523 25 15.24 49 29.88 56 34.15 25 15.24 94.5

d) Your response on "investor under-react to public info. and overreact to perceived private information" (Chan, 2003)

158 2.259 31 18.90 66 40.24 50 30.49 11 6.71 96.3

e) Your response on "investors respond mistakenly in initial phase of the information disclosure" (Ikenberry et.al, 1995)

156 2.340 26 15.85 59 35.98 63 38.41 8 4.88 95.1

Source: Responses on survey questionnaire in Appendix I

Apart from structured questionnaire asked for the respondents, some open questionnaires

are also provided to collect the views of respondents without any restrictions so that some

investors raised the issues which they considered the most important factors for market

growth and development. For instance, most of the investors who respond on the open

question stated that political stability is one of the crucial factor for enhancing investor

confidence and market growth followed by the implementation of central depository

system and they perceive that Nepalese stock market is influenced by the big investors.

Similarly, the other issues are: investor awareness, need of proper financial analysis,

investor confidence, the need of modernization, the policy implementation, etc among

others.

B. Factor Analysis

158

This part of the study is directed towards to identify the key components that influence

the stock prices in the market. The factors those affect the stock prices need to be

analyzed while investing or to succeed in the market, the investors needs to look at them

prior to making investment decisions. The factor analysis is based on the survey database

with initially 16 different variables but to maintain the factor analysis procedure, 2

variables are omitted so that the remaining 14 variables are included in the procedure

after the anti-image correlation matrix.

Since, the basic procedure is the preliminary screening of the responses through the

correlation analysis. The factor analysis is designed in such a way that the included

variables should have to optimum level of relationship among the other variables. Table

4.29 shows that there are 120 correlation coefficients where 42 coefficients are significant

at 5 percent risk level. Because of relatively the small sample size, the correlation

coefficients in the table do not meet the perfect requirements of the factor analysis so that

it is essential to increase the number of respondents for the study. By skipping the

correlation matrix analysis, the study jump to next step the measure of sampling adequacy

to overcome the existing limitations.

Table 4.29Correlation Matrix and p-values

This table presents the correlation matrix and the p-values in the succeeding table below. The variable are defined as: analyzing financial statements is not important (X1), analyzing the rate of price changes is an important step for trading securities (X2), brokers usually alter my investment decisions (X3), graphs, lines & charts are useful for stock trading (X4), I always evaluate the company profile & track records of management while investing (X5), I believe that success in stock market depends upon luck (X6), I use the average prices to determine the current prices (X7), I use dividend payment records while buying and selling stocks (X8), the price move in a direction provides insight about future prices (X9), It is important to look at debt and equity structure before investing (X10), News/media largely influence my investment decisions (X11), Political instability is not the major cause of stock market downturn (X12), analyzing high-low prices is important while buying and selling stocks (X13), Macro-economic indicators and monetary policy dictates my investment decisions (X14), my friends recommend/help me to decide most of my investment alternatives (X15), and, I do not perform the proper financial analysis myself while investing (X16). 5 point Likert scale technique is used to collect the responses where 1 indicates ‘strongly agree’ and 5 indicates ‘strongly disagree’. The Pearson’s correlation coefficients are tested at 5 percent level. Total 164 respondents are included in the opinion collection. The coefficients in the table below are p-values. There are 164 responses included for the analysis.

Correlation coefficients X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 14 X15

X2 0.09 1.00X3 (0.06) 0.12 1.00

X4 0.24 0.11 0.03 1.00X5 0.09 (0.11) (0.15) 0.21 1.00X6 0.11) 0.14 0.26 (0.13) 0.31) 1.00

159

X7 0.13 0.04 0.08 0.33 0.05 0.16 1.00X8 0.04 0.28 (0.07) 0.10 0.04 0.16 0.37 1.00X9 0.12 0.05) (0.00) 0.04 0.00 (0.05) 0.03 0.06 1.00X10 0.20 0.02 (0.07) 0.23 0.31 (0.35) 0.04 (0.01) 0.24 1.00X11 (0.01) (0.01) 0.24 0.14 (0.01) 0.17 0.07 0.14 0.10 0.02 1.00X12 (0.02) (0.05) 0.05 0.01 0.13 (0.01) (0.17) (0.01) 0.06 (0.08) 0.30 1.00X13 0.10 0.16 0.24 0.32 0.06 0.04 0.33 0.15 0.20 0.23 0.21 0.06 1.00X14 0.03 0.00 0.10 0.23 0.23 (0.15) 0.13 (0.00) 0.14 0.32 0.04 0.12 0.27 1.00X15 (0.03) 0.24 0.18 0.13 (0.02) 0.17 0.05 0.15 0.14 (0.01) 0.05 0.12 0.18 0.28 1.00X16 0.23 (0.20) (0.08) 0.07 0.27 (0.19) 0.02 (0.12) (0.08) 0.34 0.04 (0.10) 0.01 0.06 (0.25)p-valuesX2 0.14                            X3 0.25 0.09                          X4 0.00 0.11 0.38                        X5 0.15 0.12 0.05 0.01                      X6 0.11 0.06 0.00 0.07 0.00                    X7 0.07 0.34 0.19 0.00 0.28 0.03                  X8 0.31 0.00 0.23 0.12 0.34 0.03 .00                X9 0.09 0.27 0.48 0.34 0.50 0.30 0.30 0.24              X10 0.01 0.40 0.20 0.00 0.00 0.00 0.33 0.47 0.00            X11 0.44 0.45 0.00 0.06 0.47 0.02 0.22 0.05 0.13 0.42          X12 0.40 0.28 0.27 0.45 0.08 0.48 0.03 0.46 0.26 0.19 0.00        X13 0.14 0.04 0.00 0.00 0.24 0.31 0.00 0.04 0.01 0.00 0.01 0.26      X14 0.38 0.49 0.13 0.00 0.00 0.05 0.06 0.49 0.06 0.00 0.32 0.08 0.00    X15 0.36 0.00 0.02 0.07 0.40 0.02 0.29 0.04 0.05 0.46 0.28 0.09 0.02 0.00  X16 0.00 0.01 0.19 0.22 0.00 0.02 0.40 0.09 0.17 0.00 0.33 0.13 0.47 0.27 0.00

Source: Responses on survey questionnaire in Appendix I

In Table 4.30 the diagonal of the MSA table represent the MSA values which are greater

than 0.50, the benchmark value as per Kaiser’s recommendation. The MSA result shows

that the sample is adequate for performing the factor analysis. Thus, there is way out to

proceed to the next step.

Table 4.30Anti-image Correlation Matrix

This table reports the Anti-image correlation matrix. The variable are defined as: analyzing financial statements is not important (X1), brokers usually alter my investment decisions (X3), graphs, lines & charts are useful for stock trading (X4), I always evaluate the company profile & track records of management while investing (X5), I believe that success in stock market depends upon luck (X6), I use the average prices to determine the current prices (X7), I use dividend payment records while buying and selling stocks (X8), the price move in a direction provides insight about future prices (X9), It is important to look at debt and equity structure before investing (X10), News/media largely influence my investment decisions (X11), analyzing high-low prices is important while buying and selling stocks (X13), Macro-economic indicators and monetary policy dictates my investment decisions (X14), my friends recommend/help me to decide most of my investment alternatives (X15), and, I do not perform the proper financial analysis myself while investing (X16).

Anti-image Correlation Matrix

X1 X3 X4 X5 X6 X7 X8 X9 X10 X11 X13 X14 X15 X16

X1 0.674                          

X3 0.004 0.627                        

X4 -0.189 0.059 0.703                      

X5 0.020 0.069 -0.085 0.764                    

X6 0.037 -0.157 0.115 0.217 0.698                  

X7 -0.057 -0.036 -0.258 -0.022 -0.152 0.567                

X8 -0.041 0.151 0.043 -0.069 -0.087 -0.338 0.520              

X9 -0.122 0.074 0.093 0.053 0.064 -0.010 -0.016 0.566            

X10 -0.064 0.026 -0.098 -0.100 0.168 0.088 -0.038 -0.172 0.724          

X11 0.054 -0.223 -0.135 -0.020 -0.124 0.107 -0.180 -0.119 0.038 0.539        

X13 0.001 -0.188 -0.139 -0.009 -0.002 -0.250 0.022 -0.127 -0.125 -0.105 0.738      

X14 0.082 -0.037 -0.086 -0.122 0.113 -0.105 0.086 -0.068 -0.175 0.010 -0.115 0.716    

160

X15 -0.009 -0.152 -0.094 -0.033 -0.177 0.137 -0.145 -0.070 -0.007 0.052 -0.069 -0.235 0.579  

X16 -0.188 0.045 0.040 -0.162 -0.017 -0.050 0.132 0.146 -0.287 -0.094 0.029 -0.028 0.226 0.616

Measures of Sampling Adequacy(MSA)

Source: Responses on survey questionnaire in Appendix I

In the table 4.31, the KMO’s MSA test shows that the measure of correlation pattern in the sample is 0.654 considered as good for the further analysis. Bartlett’s test of Sphericity which is the test of null hypothesis of no correlation among the variables under consideration but it is rejected at 95 per confidence level so that the fundamental requirement for the factor analysis is fulfilled.

Table 4.31KMO and Bartlett's Test

The table presents the Kaiser-Meyer-Olkin Measure of Sampling Adequacy coefficient and Bartlett's Test of Sphericity with approximate chi-square value, degree of freedom and the p-value. The test is performed to confirm the sampling adequacy. The analysis is based on the responses collected from 164 respondents.

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.654

Bartlett's Test of Sphericity Approx. Chi-Square 264.530

 

Df 91

Sig. 0.00

Source: Responses on survey questionnaire in Appendix I

Table 4.32 presents the major aspects of the factor analysis procedure. The initial 14

variables with its factor loadings, extraction, specific variance, and the initial and rotated

Eigenvalues are shown in the table. Each factor loading indicates the relationship between

the corresponding -

Table 4.32An initial solution for factor analysis

This table shows the initial solution of the factor analysis, communalities coefficients of the variables for the data reduction procedure. The first column includes the variables selected for the study. The extraction column indicates the power of variation explained by the corresponding variables. The specific variance is column indicates the variation causes by the other components beyond the corresponding stated variable. The extraction method: Principal Component Analysis (PCA) is used. The components coefficients indicate the correlation between the individual variable and the selected components. The last four rows shows the initial Eigenvalues and the percentage of variance explained by the components. The rotated Eigenvalues as well as percentage of variance explained is also presented in last two rows.

Component Matrix (Initial Solution) Component   Extraction

 

Specific Variance 1 2 3 4 5

X10 It is important to look at debt and equity structure before investing 0.661 -0.265 0.193 -0.078 0.151 0.574 0.426

X4  Graphs, lines & charts of stock market indicators (index, prices, dividends, etc) are useful for stock trading

0.623 0.176 -0.185 0.011 -0.194 0.491 0.509

X14 The macro-economic indicators (GDP, Inflation, Growth, interest rate, etc) and monetary policy dictates my investment decisions

0.569 0.123 0.440 0.108 -0.266 0.615 0.385

X5   I always evaluate the company profile & track records of management while investing 0.536 -0.284 0.021 0.066 -0.312 0.470 0.530

X13  The analysis of high and low prices is important while buying and selling stocks 0.520 0.470 0.085 -0.149 -0.022 0.521 0.479

X1   Analyzing financial statements is important for trading securities 0.418 -0.098 -0.330 -0.080 0.405 0.464 0.536

X6   I believe that success in stock market depends upon luck -0.402 0.573 -0.194 -0.181 0.005 0.561 0.439

X15 My friends recommend/help me to decide most of my investment alternatives 0.083 0.547 0.424 0.284 -0.111 0.578 0.422

161

X3   Brokers usually alter my investment decisions -0.063 0.544 0.266 -0.537 -0.100 0.668 0.332

X7  I use the average prices (6 months, 1 yr, 2 yrs, etc) to determine the current prices 0.406 0.431 -0.520 0.125 -0.182 0.669 0.331

X8  I use dividend payment records while buying and selling stocks 0.138 0.445 -0.479 0.396 0.067 0.607 0.393

X11  Media coverage largely influence my investment decisions 0.125 0.435 -0.043 -0.491 0.221 0.497 0.503

X16  I always perform the proper financial analysis myself while investing 0.390 -0.467 0.217 0.485 -0.003 0.652 0.348

X9 The prices move in a direction (increasing/decreasing) provides insight about future price

0.306 0.179 0.294 0.239 0.733 0.806 0.194

Initial Eigenvalues 2.501 2.173 1.306 1.158 1.039

 

% of Variance Explained 17.865 15.518 9.326 8.268 7.419 Rotated Eigenvalues 2.140 1.668 1.589 1.569 1.209 % of Variance Explained 15.284 11.914 11.350 11.209 8.639

Source: Responses on survey questionnaire in Appendix I

variable and its component, extraction is the power of variance explanation, the specific

variance is the contribution of other components except the corresponding variable, and

the Eigenvalues are the basis for finding the reliable components in the factor analysis

procedures. For example, the variable: X5: “I always evaluate the company profile &

track records of management” explain 47 percent of the total variance through its

aggregate contribution on five components. On the other hand, the specific variance is the

proportion that is not explained by the stated variables. In other words, 53 percent of the

total variation is not covered by X5 variable. The similar interpretation is applicable for

remaining variables and its coefficients in different columns like: X9: the prices move in a

direction provides insight about the future prices extract about 81 percent of total variance

whereas only about 19 percent of the total variation explain by the remaining components

i.e. except stated five components. Initial Eigenvalues in the forth last row indicates the

sum of square of each factor loadings in each component. For instance, the first

component has the capacity to explain the total variance of about 18 percent whereas the

fifth component has 7.42 percent. When the five components combine together that

constitute the total variance explanation power of about 58 percent. When, the factor

analysis proceed to the varimax rotated solution the total variance explaining power

remains constant i.e. 58.396 percent but the individual component’s capacity tend to

changes or come to quite uniform way. The individual Eigenvalues of the rotated

solutions and its percentage of variance explanation become changed. The negative factor

loadings show the negative relationship between the variables and the components.

162

Figure 4.4

The Scree Plot and the determination of number of components greater than 1 Eigenvalues

Source: Responses on survey questionnaire in Appendix I

The justification of the selection of five principal components can be made through two

ways. First one is the straight criterion; under this criterion the number of principal

components is equal to the number of Eigenvalues greater than 1. The next criterion is the

scree plot; the number of components is equal to the number of Eigenvalues greater than

first scree. The Figure 4.4 exhibits the scree plot of the Eigenvalues but the determination

of the reliable components does not clearly shown. Generally, even though the existence

of the different criteria, in most cases both methods determine the same number of

163

components. For this study, five components are determined prioritizing the first criteria

for this study.

Table 4.33 presents the rotated solution with cross loadings in Panel A and the final

rotated solution in Panel B after omitting the three cross loading variables. The factor

loadings are suppressed which are smaller than 0.40 and sorted them as per the retained

components. Finally, only 8 components are retained with three factors. The

independence test is presented in the later part of this section.

The rotated solution in Panel A has 14 variables with 5 components. The results suggest

that there are 3 cross loadings in the table so that the necessary treatment is need to be

done. The best solution for the cross-loading is considered as the omitting such variables

from the remaining factor analysis procedure. The cross-loading variables are omitted

following one by one procedure.

Table 4.33Rotated solution for factor analysis

The table below presents the rotated solution for factor analysis having 5 components and 14 retained variables in Panel A and 3 components and 8 retained variables in Panel B. The PCA method is used for extraction and Varimax with Kaiser Normalization is used as rotation method. The factor loadings are suppressed below 0.40 and ranked in ascending order.

Panel A: Rotated Component Matrix (Rotated Solution)

StatementsComponents

1 2 3 4 5

X14 The macro-economic indicators and monetary policy dictates my investment decisions 0.709 X5   I always evaluate the company profile & track records of management while investing 0.625 X10 It is important to look at debt and equity structure before investing 0.620 X6   I believe that success in stock market depends upon luck -0.525 0.423 X4  Graphs, lines & charts of stock market indicators are useful for stock trading 0.488 0.475 X7  I use the average prices (6 months, 1 yr, 2 yrs, etc) to determine the current prices 0.792 X8  I use dividend payment records while buying and selling stocks 0.731 X3   Brokers usually alter my investment decisions 0.776 X11  Media coverage largely influence my investment decisions 0.661 X13  The analysis of high and low prices is important while buying and selling stocks 0.482 X16  I always perform the proper financial analysis myself while investing 0.723 X15 My friends recommend/help me to decide most of my investment alternatives 0.696 X1   Analyzing financial statements is important for trading securities -0.462 0.405X9 The prices move in a direction (increasing/decreasing) provides insight about future price 0.880

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.Rotation converged in 7 iterations.

Panel B: Rotated Component Matrix

StatementsComponents

1 2 3

X3   Brokers usually alter my investment decisions 0.768 X11  Media coverage largely influence my investment decisions 0.652 X15 My friends recommend/help me to decide most of my investment alternatives 0.587 X8  I use dividend payment records while buying and selling stocks 0.839 X7  I use the average prices (6 months, 1 yr, 2 yrs, etc) to determine the current prices 0.788 X10 It is important to look at debt and equity structure before investing 0.820 X5   I always evaluate the company profile & track records of management while investing 0.677 X9 The prices move in a direction (increasing/decreasing) provides insight about future price 0.457

164

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.Rotation converged in 5 iterations.

Source: Responses on survey questionnaire in Appendix I

After omitting the cross-loading variable from the factor procedure, the Panel B shows

the final rotated solution where 8 variables are retained with 3 components/factors. The

first factor constitutes three variables (X3, X11 and X15) namely, “brokers usually alter

my investment decisions”, “media coverage largely influences my investment decisions”,

and “my friends help/recommend me to decide most of my investment alternatives”.

Similarly, the second factor constitutes two variables: “I use dividend payment records

while buying and selling stocks”, and “I use the average prices to determine the current

prices.” Finally, the third factor includes three variables (X10, X5, and X9) namely, “it is

important to look at debt and equity structure before investing”, “I always evaluate the

company profile & track records of management while investing”, and “the prices move

in a direction provides insight about future prices”. Thus, the most important step in

factor analysis is to name these factors incorporating the features of all the included

variables so that based on these criteria, the study determined the name of these factors

as; the external factor (brokers, media & friends) for the first factor, self-knowledge

(using dividend records & average prices), and the firm specific factor (debt and equity,

company and management profile & price movement). Therefore, the factor analysis

concluded that the external factor, self-knowledge and firm specific factor are the most

important factors among others that directly influence the investment decision making

procedure.

Table 4.34Correlation matrix of the retained variables and factors identified: A verification

This table reports the correlation matrix of the retained variables until the final procedure in Panel A and final outcomes of the factor analysis in Panel B. The Pearson’s correlation coefficients show the degree of relationship between components and the p-values are presented in the row corresponding p below each correlation coefficient.Panel A: Correlation Matrix of variables under consideration: For verification

Panel B: Correlation Matrix of factors identified: For verification

Variables Factor 1 Factor 2 Factor 3 Factors   Factor 1 Factor 2 Factor 3

X3 X11 X15 X7 X8 X9 X10 X5Factor 1

r 1.00    

X3 r 1.00               p      

p                Factor 2

r 0.00 1.00  

X11 r 0.25 1.00             p 1.00    

p 0.00              Factor 3

r 0.00 0.00 1.00

X15 r 0.27 0.16 1.00           p 1.00 1.00  

p 0.00 0.04            

X7 r 0.07 0.10 0.10 1.00        

p 0.38 0.25 0.22          

X8 r -0.03 0.16 0.18 0.37 1.00      

p 0.68 0.05 0.02 0.00        

X9 r 0.04 0.07 0.13 0.04 0.09 1.00    

p 0.60 0.42 0.12 0.65 0.26      

X10 r -0.11 0.07 0.05 0.11 0.00 0.18 1.00  

165

p 0.19 0.37 0.56 0.20 0.98 0.03    

X5 r -0.09 0.05 0.02 0.10 0.05 0.04 0.29 1.00

p 0.26 0.50 0.79 0.23 0.51 0.66 0.00  

Source: Responses on survey questionnaire in Appendix I

The basic condition for factor analysis is to have the optimal relationship among the

variables selected for the study and on the other hands, the factors identified after the

complete procedure should be independent with each other. To verify this basic norm of

the factor analysis, Table 4.34 exhibit the correlation matrix of the variables under

consideration in Panel A and the correlation matrix of the factors identified in Panel B.

The variables under each factor and its correlation are presented in the table which

indicates that there is significant relationship between intra-variables. For instance, 0.27,

0.25 and 0.16 under external factor (factor 1), 0.37 for self-knowledge (factor 2), and

0.29, 0.18 and 0.04 for firm specific factor (factor 3). All these coefficients except one

(0.04) are significant at 95 confidence level. On the other hands, there is no correlation

between the factors as seen in Panel B. Thus, with the evidence of the Table 4.34 it is

concluded that in factor analysis, the intra-variable should be correlated but the inter-

factor should be independent.

4.3 Concluding remarks

The study is conducted to analyze the market information and stock returns. The market

information is considered as the news coverage, the information dissemination by the

enterprises and the concern authorities. It is the general fact that the stock price

movement is persistently correlated with investors’ performance. To proceed into this

broad framework, the study selected two sources of information, the secondary data

sources and primary data sources. For the secondary analysis, despite 176 listed

enterprises as of mid-July 2010 only 146 enterprises are analyzed due to the

unavailability of the organized data sources. In total 826 firm year from 1994 to 2010 are

selected for analysis. In most cases, the secondary data analysis tool is the multiple

regression models with Daniel and Titman (2006) procedure. The variables selected for

the study are: EPS, MPS, size, BPS, sales and cash flow. Further, to analyze the news

effect for stock returns, 1683 financial news headings from the Kantipur daily from

January 1994 to July 2010 are collected and classified them into bad news, good news,

and informational news categories and its effect for stock returns is analyzed employing

the multiple regression models. On top of these analysis, the political leadership effect for

stock returns is analyzed using the dummy regression procedure. The market returns is

166

measured on the daily, monthly and yearly basis with average returns and the end period

return procedure. The primary database, on the other hands, collected using the structured

questionnaire and solely from the active stock investors. The respondents for the primary

data source are 164 stock investors from the Kathmandu valley during the month of

December 2011.

The published data of the NEPSE listed enterprises collectively shows that the firm level

stock returns is negatively affected by the lagged book to market ratio and positively by

the market price to lagged market price variable; there is consistent negative relationship

between sales to price and price to lagged price ratio and consistent positive relation

between stock returns and price to lagged price ratio; the independent effect of price to

lagged price ratio has the substantial explanatory power for firm level stock returns; there

is significant effect of lagged earnings to price ratio up to three lag periods; for the

analysis of firm level stock returns movements only the three years of historical

accounting data are useful to find the signals and to establish the relationship between the

dependent and independent variables; there is fluctuating relation of book to market ratio

with firm returns and there is chances of early losers to win in the later periods; there is

only the book to price and earnings to price ratios have strong predictive power and

usefulness of historical data up to 4 years, on the other hands, the sales to price and cash

flow to price ratios have no predictive power for firm level stock returns in Nepalese

capital market; are some of the major findings of the firm specific secondary analysis.

While taking the news analysis and its effect for stock returns, it is found that the negative

effect of bad news and positive impact of good news for stock returns, and it is also

proved that the informational news has inconsistent effect for stock returns during the

study periods; further, considering the political leadership effect for stock returns it is

found that there is lower contribution of NC government for market growth as compare to

CPN-UML and UCPN (M) government; thus, the major conclusion regarding the news

and political leadership effect for stock returns is that the bad news are the market growth

barriers and good news are the market growth friendly new categories, and the CPN-

UML government is proved as constructive for market growth and compare to NC

government which is proven by the secondary database of the study periods.

The primary data analysis consists of demographic presentation and the factor analysis

which shows that in the secondary market majority of the stock investors prefer to

increase in stock price rather than the fundamental cash dividend and the media plays

crucial role for investment decision making followed by the friend circle of the stock

167

investors; the most important five information that investors usually consider prior

investing are: dividends, book value per share, earnings, average stock prices, and the

number of equities; the investors perceive mutual fund, central depository system, credit

rating agency, and portfolio management services are most important mechanism for

market growth and development but they are not highly aware on any of them so that it

indicates the need of investor education on these specific areas; the investors poses

disagreement on the statement ‘investing in IPO is more risky than investing in secondary

market’, ‘seasonal offerings do not maximize the shareholders’ wealth’, ‘the most

frequent trading is harmful for investors; wealth’, and ‘if reliable private information is

available, it would be better to invest in single security’, and they tend to agree on the

statement ‘news events lead some investors to react quickly’; and, the investors are

agreed on the evidences: ‘stock market exhibit higher returns following good news and

lower returns following bad news’ and disagreed on ‘investors under-react to publicly

available information and overreact to perceived private information’; ‘investors respond

mistakenly in initial phase of the information disclosure’; ‘the media effect, market noise,

seasonal effect, etc strongly influence men investor but not for women’ and ‘high

information uncertainty enhance the investor’s overconfidence’. Further, the factor

analysis found that there are three important factors that influence the market prices. Out

of them, the first factor constitutes three variables namely, “brokers usually alter my

investment decisions”, “media coverage largely influences my investment decisions”, and

“my friends help/recommend me to decide most of my investment alternatives”.

Similarly, the second factor constitutes two variables: “I use dividend payment records

while buying and selling stocks”, and “I use the average prices to determine the current

prices”, and finally, the third factor includes three variables namely, “it is important to

look at debt and equity structure before investing”, “I always evaluate the company

profile & track records of management while investing”, and “the prices move in a

direction provides insight about future prices”. Therefore, with the retained 8 variables

out of initial 16 variables, the factor analysis concluded that the external factor (first

factor), self-knowledge (second factor), and firm specific factor (third factor) are the most

important factors that directly influence the investment decision making procedure.

Further, with the identification of three factors, it is verified that in factor analysis, the

intra-variables are correlated but the inter-factor are independent.

168

Chapter 5

SUMMARY, CONCLUSION AND IMPLICATIONS

5.1 Summary

From the inception of the financial market in the world economy, the financial

communities have been experiencing the varieties of economic turbulences those have

been affecting the market growth and development. The human psychological factors at

the same time directly or indirectly influence the market movements. In the general terms,

the monetary return on the investment is directly connected with the performance of the

enterprises itself. Some evidences documented that firm specific accounting variables

169

which are relatively stable by nature are the major sources of variations whereas other

evidences focused on others. Einhorn, et al. (1978) documented that people have great

confidence in their fallible judgments. The issue of overconfidence in judgment was

further confirmed by Einhorn (1980), investors tend to react slowly the bad news

information by Chan (2003), Mitchell and Mulherin (1994) documented that the number

of news stories and market activities are directly related, among others. Based on these

evidences, the general learning in the investment community is that the events that burst-

out expectedly or unexpectedly that have significant impact for investors’ mindset and

those sudden information plays an important role for individual investment decision

making process.

There has been considerable shift in the literature towards predicting stock returns and to

formulate the forecasting tools and techniques in the recent period because of the fragile

characteristics of the capital market. But, there is still a lack of consensus upon single

model, tools and procedures which explain the future well. Some evidences shows that

stock return is divided into two components as the selectivity and risk (Fama, 1972),

stock returns changes as per the changes in expected future dividends (Campbell, 1991),

cash-flow news effect the stock returns (Vuolteenaho, 2002), the book to market ratio and

cash flow yield has the most significant positive impact on stock returns as documented

by Chan, et.al (1991), and Daniel and Titman (2006) proposed that stock return is a

function of tangible and intangible returns. These empirical evidences focused toward the

stock returns decomposition in one or other way which helps to identify the dimensions

that help to explain the variation of the returns. But, these evidences are not based on the

single procedure. Nowadays, stock returns forecasting became a central issues in finance

and the numerous studies have been conducted to scan its signals. While talking about the

behavioral studies, De Long, et al. (1990) documented that the overreaction of stock

prices is due to the news, price bubbles and the expectations. Likewise, sophisticated

investors can earn superior returns by taking advantage of under-reaction and

overreaction without bearing extra risk (Barbaris, et al., 1998), Similarly, Daniel and

Titman (2000) stressed on overconfidence and concluded that asset prices are influenced

by investor overconfidence. Sun and Wei (2011) further documented that investors are

overly sensitive to intangible information when they need to make more subjective

judgments. On the other hands, Banz (1981) showed the size effect, size and book-to-

market equity effect (Fama and French (1992), Daniel and Titman (1997)) and on the

contrary, Kothari, et al. (1995) documented the relationship between book-to-market

170

equity and returns which seems weaker and less consistent. These diversified evidences

proved the controversy among the previous studies.

The studies in the Nepalese context are smaller in volume as of the growth of the capital

market. The positive relation between stock returns and size whereas inverse relation

between stock returns and market-to-book value by Pradhan (1993). The positive relation

of stock returns with earning yield and size whereas negative relation with book-to-

market ratio and cash flow yield and book-to-market value is found to be more

informative (Pradhan and Balampaki, 2004).

Even though, the financial economists and investors have been spending considerable

time and efforts to find the best investment strategies which generate the abnormal

returns, but the reliable one is yet to be found. Several studies have confirmed that the

firm level fundamental variables are useful in explaining the stock returns patterns and

the future price movements but there is lack of consensus while identifying single most

predictive accounting variable. Beyond the fundamental growth measures that carry the

tangible information of the past performance of the enterprises, the unexplained

information that can be treated as the intangible information which is relatively difficult

to identify its signals. For instance, the media coverage and its effects, lagged effects, past

performance and overconfidence, investor sentiments, political effect, and so on. Some

field evidences shows the momentum effect (Jagadeesh and Titman, 1993), book-to-

market anomaly (Rosenberg, et al., 1985), higher profitability earn higher average stock

returns (Haugen and Baker, 1996), the leverage effect (Bhandari, 1988), etc. These

evidences on the various issues in the developed capital market help to establish the

relationship among the variables in the small and emerging stock market in the local

context.

The study analyzes the market information and its effect for stock price movements. To

establish this relationship, the study deals with the following issues: what is the

relationship between past tangible information and future returns? Is there relationship

between past intangible returns and future returns? Whether low book-to-market firms

have the higher accounting growth rates? Is there association between book-to-market

equity and future returns? Do the stock prices overreact to the past performance? What is

the most predictable fundamental measure in stock exchange? What are the news effects

on stock returns? What is the bad news effect? What is the good news effect? What is the

informational news effect? Does the political leadership effects on Nepalese stock market

171

movements? What are the effects of NC led government? CPN-UML led government?

and, UCPN (M) led government? What are the opinions of Nepalese stock investors on

investment alternatives, decision making, market prices and stock returns?, and, what are

the factors affecting investment decision making in equity investment?, among others are

the issues of the study.

The analysis of the issues stated above help to achieve he basic objective of the study

which is to analyze the market information and stock returns in Nepalese stock market.

The specific objectives includes - evaluate the relationship between stock returns and

fundamental measures, to determine the news effects – bad news, good news and

informational news, on stock returns, to examine the political leadership effects on stock

returns, to determine the factors affecting stock investment in Nepalese stock market, and

to examine the investor opinions on various issues of capital market like: investor

education and personality type, preferences, trading behavior and practices, sources of

funds for investment, risk perception, level of investor awareness, investor reactions and

judgments on previous findings of the similar studies.

The research design employed for the study consists of descriptive and causal-

comparative research design. Both the secondary and the primary sources of database are

used to collect the necessary data for the analysis. The analysis is carried out by using

daily, monthly and yearly database starting from mid-July 1994 to mid-July 2010 because

of the availability of annual reports as per the Nepali fiscal year. The study sticks to this

time limit because of limited availability of organized and needful database at source. The

primary data has been collected through the structured questionnaire from the active stock

investors in Nepalese stock market after personally meeting them in different trading

floor in Kathmandu valley.

Based on the availability of the required nature of secondary database, only 146

enterprises out of 176 are selected. For the opinion collection, the sample size is

considered as 364 stock investors because of the undefined pollution of Nepalese stock

investors. The final structured questionnaires were distributed to them and 164 filled up

questionnaire were received from the investors thus the response rate is about 45 percent.

The study employs the econometric models and the usual statistical tools for data

analysis. The basic variables used for the study are: stock returns (NEPSE returns),

earnings per share, market price per share, cash dividend as at end of each fiscal year,

size, book value per share, sales revenue, and, cash flows. Other variables also employed

172

for the analysis which are: price scaled fundamental variables – book to price, sales to

price, earnings to price and cash flow to price ratios, the individual variables’ returns

during the lagged periods like – book returns, sales returns, cash flow returns, and

earnings returns, tangible and intangible components of the stock returns, composite share

issuance measure, news coverage/headings – bad news, good news and informational

news, and the political leadership and its dummy variables are employed for the analysis.

The secondary data analysis shows that the firm level stock returns is negatively affected

by the lagged book to market ratio and positively by the market price to lagged market

price variable; the consistent negative relationship between sales to price, and price to

lagged price ratio for stock returns, and consistent positive relation between stock returns

and price to lagged price ratio; the independent effect of price to lagged price ratio has the

substantial explanatory power for firm level stock returns movement; there is significant

effect of lagged earnings to price ratio up to three lag periods; for the analysis of firm

level stock returns movements only the three years of historical accounting data are useful

to find the signals and to establish the relationship between the dependent and

independent variables; there is fluctuating relation of book to market ratio with firm

returns and there is chances of early losers to win in the later periods; there is only the

book to price and earnings to price ratios have strong predictive power and usefulness of

historical data up to 4 years, on the other hands, the sales to price and cash flow to price

ratios have no predictive power for firm level stock returns in Nepalese capital market;

are some of the major findings of the firm specific secondary data analysis. While taking

the news counts and its effect for stock returns, it is found that the negative effect of bad

news and positive impact of good news for stock returns, and it is also proved that the

informational news has inconsistent effect for stock returns during the study periods;

further, considering the political leadership effect for stock returns, it is found that there is

lower contribution of NC government for market growth as compare to CPN-UML and

UCPN (M) government during the study period.

The major findings of the study are as follows:

i. The profile analysis of four price scaled variables and eight other accounting

variables for the period mid-July 1997 to mid-July 2010 shows that the movement

of majority of the selected variable exhibit the downward movement. More

specifically, only three out of twelve selected variables indicate the upward

173

movements namely, market equity, market to price ratio and the sales revenue

during the study periods.

ii. The correlation coefficients of the variables considered for the study presents that

among the total correlation coefficients, nine sets of variables which have no

significant correlation and the remaining nineteen pairs have significant positive

correlation at 95 percentage confidence level.

iii. The firm level stock returns is negatively affected by the lagged book-to-market

ratio and positively by market price to lagged market price ratio but the

relationship between returns and the book to lagged book values is inconclusive.

iv. There is consistent negative relationship between sales to price and price to lagged

price ratio and consistent positive relation between firm level stock returns and

price to lagged price ratio whereas inconclusive relation and least effects of lagged

sales to price and sales to lagged sales ratio for stock returns.

v. The price to lagged price ratio has the substantial explanatory power for firm level

stock returns among the selected fundamental ratios for the study during the study

periods.

vi. For the firm level stock returns, there is significant effect of lagged earnings to

price ratio up to three years. In other words, for the analysis of firm level stock

returns movements only the three years of historical accounting data are useful to

find the signals and to establish the relationship between the dependent and

independent variables.

vii. There is fluctuating relation of book to market ratio with firm returns and there is

chances of early losers to achieve in the later periods when taking the analysis of 5

lag periods.

viii. Even though book returns does not included in the firm returns, it is shown that

there is positive relationship between them and in some cases the strong

relationship with firm level stock returns.

ix. Out of fundamentals price scaled variables, only book to price and earnings to

price ratios have strong predictive power and the usefulness of the historical data

is proved to be the lagged 2 to 4 years where all the respective regression

coefficients are significant at 5 percent risk level. On the other hands, the sales to

price and cash flow to price ratios have no predictive power for firm level stock

returns

174

x. The intangible information in majority of the cases pull down the stock returns

and rarely help to boost the firm level stock returns and when the lag period

increases, the strength of the relationship also inversely increases.

xi. There is negative effect of bad news contents for the stock market movements

whereas the positive impact of good news contents and the inconsistent effect of

informational news for the market return.

xii. There is negative effect of bad news for stock returns. In most cases one unit of

bad news headline leads 0.01 unit negative change in market returns. The strength

of relationship between stock returns and good news is relatively weaker than bad

news but the direction of relationship between good news and returns is

consistently positive i.e. good news leads less than 0.01 unit positive changes in

market returns. The informational news on the other hands has inconsistent and

marginal effect for the stock market movements in Nepalese capital market.

xiii. Relatively, there is lower contribution of the NC led government for the market

growth while CPN-UML and UCPN (M) leadership have on an average positive

contribution for average stock returns during the study period.

xiv. In general, the CPN-UML and UCPN (M) led governments; on an average has

positive effect for the growth of stock market returns based on the assumptions of

this study.

xv. The CPN-UML led government is proved to be the stock market friendly

government. Thus, the CPN-UML leg government is proved a constructive

government for the market growth compares to the NC and UCPN (M) led

government during the study period.

xvi. The bad news has consistent negative effect and the good news has consistent

positive effect for average stock returns but there is inconclusive effect of

informational news for market returns, the daily news as well as daily leadership

effect is more stronger than the monthly and yearly effects.

xvii. The monthly series have more predictive power than yearly and daily series, the

bad news, good news and informational news have consistent negative, positive

and negative effect for stock returns respectively.

xviii. The bad news are the market growth barriers and the good news are market

growth friendly news categories, and out of the experienced political leadership in

Nepalese context, the CPN-UML leg government is proved as more constructive

government for the market growth.

175

xix. A survey results shows in the secondary market majority of the stock investors

prefer to increase in stock price rather than the fundamental cash dividend and the

media plays crucial role for investment decision making followed by the friend

circle of the stock investors.

xx. Five most important information that investor usually consider prior investing are:

dividends, book value per share, earnings, average stock prices, and the number of

equities.

xxi. The stock investors perceive mutual fund, central depository system, credit rating

agency, and portfolio management services are most important mechanism for

market growth and development but they are not highly aware on any of them so

that it indicates the need of investor education on these specific areas.

xxii. The stock investors poses disagreement on the statement ‘investing in IPO is more

risky than investing in secondary market’, ‘seasonal offerings do not maximize the

shareholders’ wealth’, ‘the most frequent trading is harmful for investors; wealth’,

and ‘if reliable private information is available, it would be better to invest in

single security’, and they tend to agree on the statement ‘news events lead some

investors to react quickly’.

xxiii. The stock investors are agreed on the evidence that ‘stock market exhibit higher

returns following good news and lower returns following bad news’ and they also

agreed on the evidences: ‘investors under-react to publicly available information

and overreact to perceived private information’. For, ‘investors respond

mistakenly in initial phase of the information disclosure’; ‘the media effect,

market noise, seasonal effect, etc strongly influence men investor but not for

women’ and ‘high information uncertainty enhance the investor’s overconfidence’

majority of respondents showed their disagreement.

xxiv. Further, the factor analysis found that there are three important factors that

influence the market prices. Out of them, the first factor constitutes three variables

namely, “brokers usually alter my investment decisions”, “media coverage largely

influences my investment decisions”, and “my friends help/recommend me to

decide most of my investment alternatives”. Similarly, the second factor

constitutes two variables: “I use dividend payment records while buying and

selling stocks”, and “I use the average prices to determine the current prices”, and

finally, the third factor includes three variables namely, “it is important to look at

debt and equity structure before investing”, “I always evaluate the company

176

profile & track records of management while investing”, and “the prices move in a

direction provides insight about future prices.”

xxv. Therefore, the factor analysis concluded that the external factor (first factor), self-

knowledge (second factor), and firm specific factor (third factor) are the most

important factors that directly influence the investment decision making

procedure.

xxvi. Lastly, with the identification of three factors, it is verified that in the factor

analysis, the variables under the identified factors are correlated among each other

but the inter-factor are independent.

5.2 Conclusions

The major conclusions of the study are based on the primary and secondary database

during the study period mid-July 1994 to mid-July 2010 are as follows.

I. While taking analysis of 5 years of lag periods, it is proved that only three years of

historical accounting database are useful to find the market signals and to establish

the relationship between the dependent and independent variables.

II. Out of the four price-scaled variables it is proved that the book to price and the

earnings to price ratios have strong predictive power for firm level stock returns

and there is no predictive power of cash flow to price and sales to price ratios for

firm level stock returns.

III. The study also concludes that there is negative effect of bad news, positive effect

of good news, and inconsistent effect of informational news for stock returns

while taking the news effect analysis from mid-July 1994 to mid-July 2010.

IV. Further, based on the assumptions of the study, the analysis of political leadership

effects shows that CPN-UML led government is proved as a market friendly

government among others.

V. Finally, a survey results conclude that there are three important factors that

influence the stock price movements namely the external factor, self-knowledge,

and firm specific factor where intra-variables under a factor are correlated but the

inter-factor are independent.

5.3 Implications

The major implications of the study can be pointed as follows.

177

Firstly, the evaluation of relationship between the stock returns and the firm specific

fundamental measures gives some insight about the usefulness of the accounting variables

for analyzing the market prices. Secondly, the growing attraction of the media people in

the economic activities promotes to disclose news, analysis, discussions, talks, etc. At the

same time, it is essential to identify its effects for the future activities. Thus, the study

determined the news effects on stock returns which is a useful factor for investment

decision makings process. Thirdly, in most cases, the politics dictates the economic

activities. In light of this general impression, the study examined the political leadership

effects on stock returns, which is assumed to be a useful insight for the market

participants. Fourthly, the study also determines the factors affecting investment decision

making. The implication of this finding is to motivate the stock investors doing some

needful preparatory studies prior investing which help to protect them from the major

investment pitfalls, and finally, the area of the study would be interesting to academic

researchers and to the future researches.

The study introduces certain variables and procedures to analyze the market information

and stock returns. Some accounting growth measures exhibit consistent relationship with

others in the analysis but some of the selected variables are not. The findings of the study

are based on the proved consistent relationship between the variables. But, the study

constrained to incorporate the further analysis for rest of the variables while drawing the

conclusion due to time and resources. Thus, the docile and less representative variables in

this study might also explain better in the future studies by changing procedures, the

study periods, the submission and omission the variables, etc. With these conceptions of

research work, the study would like to promote future researches in this area. Thus,

keeping in mind ‘the research is a continuous process’, it is expected that there would be

substantial attraction of the researchers, market participants, readers, and financial

scholars for stock market studies in near future.

Bibliography

Andersen, T.G., Bollerslev, T. and Cai, J. (2000). Intraday and interday volatility in the

Japanese stock market. Journal of international financial markets, institutions and

money, Vol.10, pp. 107-130.

178

Armen, H., Opler, T. and Titman, S. (2001). The debt-equity choice, Journal of Financial

and Quantitative Analysis, Vol. 36, pp. 1–24.

Asness, C.S. (1994). The power of past stock returns to explain future stock returns,

Manuscript, June.

Asness, C.S., Porter, R.B. and Stevens, R.L. (2000). Predicting Stock Returns Using

Industry-Relative Firm Characteristics. Working Paper

Bachelier, L. (1900), "Théorie de la spéculation", Annales Scientifiques de l’École

Normale Supérieure 3 (17): pp. 21–86.

Baker, M. and Wurgler, J. (2002). Market timing and capital structure. The Journal of

Finance, Vol. 57 (1), pp. 1-32.

Banz, R.W. (1981). The relationship between returns and market value to common

stocks. Journal of Financial Economics, Vol. 6, pp. 103-126.

Banz, R. and Breen, W. (1986). Sample dependent results using accounting and market

data: Some evidence. The Journal of Finance, Vol. 41, pp. 779-793.

Barber, B.M. and Odean, T. (2000). Trading is hazarous to your wealth: The common

stock investment performance of individual investors. The Journal of Finance,

Vol.LV, No.2, pp. 773-806.

Barber, B.M. and Odean, T. (2001). Boys will be boys: Gender, Overconfidence, and

Common stock investment. The Quarterly Journal of Economics,(February),

pp.261-292.

Barber, B.M. and Odean, T. (2008). All that glitters: The effect of attention and news on

the buying behavior of individual and institutional investors. The Journal of

Finance, Vol.21, No.2, pp. 785-818.

Barberis, N., Shleifer, A. and Vishny, R. (1998). A model of investor sentiment. Journal

of Financial Economics, Vol. 49, pp. 307-343.

Baskota, N. P. (2007). Stock Price Volatility in Nepal. Unpublished M. Phil. Dissertation,

Office of the Dean, Faculty of Management, Tribhuvan University.

Basu, S. (1977). Investment performance of common stocks in relation to their price

earnings ratios: A test of the efficient market hypothesis, The Journal of Finance,

Vol. 32, pp. 663-682.

Berk, J., Green, R.C. and Naik, V. (1999). Optimal investment, growth options, and

security returns. The Journal of Finance, Vol. 54 (5), pp. 1553-1607.

Bernard, V.L. and Thomas, J.K. (1989). Post-earnings-announcement drift: Delayed price

response or risk premium? Journal of Accounting Research, Supplement Vol. 27,

pp. 1-48.

179

Berry, T.D. and Howe, K.M. (1994). Public information arrival. The Journal of Finance,

Vol. XLIX, No.4, pp. 1331-1346.

Bhandari, L. C. (1988). Debt/equity ratio and expected common stock returns: Empirical

evidence. The Journal of Finance, Vol. 43, pp. 507-28.

Biais, B., Hilton, D., Mazurier, K. and Pouget, S. (2005). Judgmental Overconfidence,

Self-Monitoring, and Trading Performance in an Experimental Financial Market,

Review of Economic Studies, Vol. 72 (2), pp. 287-312.

Black, F. (1972). Capital market equilibrium with restricted borrowing. Journal of

Business, Vol. 45, No. 3, pp. 444-454.

Black, F. (1986). Noise. The Journal of Finance, Vol. 41, pp. 529–543.

Black, F. and Scholes, M. (1973). The pricing of options and corporate liabilities, Journal

of Political Economy, Vol. 81, pp. 373-399.

Black. F., Jensen, M.C. and Scholes, M. (1972). The capital asset pricing model: Some

empirical tests. Studies in the Theory of Capital Market, pp. 79-124.

Bogan, V. (2006). Stock market participation and the internet”, Journal of Financial and

Quantitative Analysis, forthcoming.

Boyd, J. H., Hu, J. and Jagannathan, R. (2005). The stock market's reaction to

unemployment news: why bad news is usually good for stocks. The Journal of

Finance, Vol. LX, No.2, pp.649-672.

Brau, J.C. and Fawcett, S.E. (2006). Initial public offerings: An analysis of theory and

practice. The Journal of Finance, Vol. LXI, No.1, pp. 399-436.

Braun, P.A., Nelson, D.B. and Sunier, A.M. (1995). Good news, bad news, volatility, and

betas. The Journal of Finance, Vol.50, No.5, pp.1575-1603.

Brav, A. and Lehavy, R. (2003). An empirical analysis of analysts' target prices: Short-

term informativeness and Long-term dynamics, The Journal of Finance, Vol.

LVIII, No.5, pp.1933-1967.

Brav, A., Geczy, C. and Gompers, P.A. (2000). Is the abnormal return following equity

issuances anomalous? Journal of Financial Economics, Vol. 56, pp. 209-249.

Brennan, M.J. and Subrahmanyam, A. (1995). Investment analysis and price formation in

securities markets. Journal of Financial Economics, Vol.38, pp.361-381.

Brown, S.J. and Warner, J.B. (1985). Using daily stock returns: The case of event studies.

Journal of Financial Economics, Vo. 14, pp. 3-31.

Camerer, C. and Lovallo, D. (1999). Overconfidence and excess entry: An experimental

approach. The American Economic Review, Vol. 89, No.1, pp. 306-318.

180

Campbell, J.Y. (1991). A variance decomposition for stock returns. Economic Journal,

Vol. 101, pp. 155-173.

Campbell, J.Y. (2000). Asset pricing at the millennium. The Journal of Finance, Vol. 55,

pp. 1515-1567.

Campbell, J.Y. and Hentschel, L. (1992). No news is good news: An asymmetric model

of changing volatility in stock returns. Journal of Financial Economics, Vol. 31,

No.3, pp. 281-318.

Carhart, M. (1997). On persistence in mutual fund performance. The Journal of Finance,

Vol. 52, pp. 57–82.

Chan, W.S. (2003). Stock price reaction to news and no-news: drift and reversal after

headlines, Journal of Financial Economics, Vol. 70, pp. 223-260.

Chan, K. C., Chen, N., and Hsieh, D.A. (1985). An exploratory investigation of the firm

size effect, Journal of Financial Economics, Vol.14, pp. 451-471.

Chan, L.K.C., Hamao, Y. and Lakonishok, J. (1991). Fundamentals and stock returns in

Japan. The Journal of Finance, Vol. 46 (5), pp. 1739-1764.

Chan, L.K.C., Lakonishok, J. and Sougiannis, T. (2001). The stock market valuation of

research and development expenditures. The Journal of Finance, Vol. 56 (6), pp.

2431-2456.

Chen, N., Roll, R., and Ross, S.A. (1986). Economic forces and the stock market, Journal

of Business, Vol. 59, pp.383-403.

Cochran, W. G. (1977). Sampling techniques (3rd ed.). New York: John Wiley & Sons.

Courtault, et.al (2000). Louis Bachelier on the centenary of Theorie De La Speculation.

Mathematical Finance, Vol.10, No.3, Pp.341-353.

Cutler, D.M., Poterba, J.M. and Summers, L.H. (1989). What moves stock prices?

Journal of Portfolio Management, Vol.15, No.3, pp. 4-12.

Daniel, K.D. and Titman, S. (1997). Evidence on the characteristics of cross-sectional

variation in common stock returns. The Journal of Finance, Vol. 52 (1), pp. 1-33.

Daniel, K.D. and Titman, S. (1999). Market efficiency in an irrational world. Financial

Analysts’ Journal, Vol. 55, pp. 28-40.

Daniel, K. and Titman, S. (2006). Market reactions to tangible and intangible information.

The Journal of Finance, Vol. 61 (4), pp. 1605-43.

Daniel, K.D., Hirshleifer, D. and Subrahmanyam, A. (1998). Investor psychology and

security market under- and over-reactions. The Journal of Finance, Vol. 53 (6),

pp. 1839-1886.

181

Daniel, K.D., Hirshleifer, D. and Subrahmanyam, A. (2001). Overconfidence, arbitrage,

and equilibrium asset pricing. The Journal of Finance, Vol. 56 (3), pp. 921-965.

Davis, J.L. (1994). The cross-section of realized stock returns: the Pre-COMPUSTAT

evidence. The Journal of Finance, Vol. 49 (5), pp. 1579-1593.

DeBondt, W.F.M. and Thaler, R.H. (1987). Further evidence on investor overreaction and

stock market seasonality. The Journal of Finance, Vol. 42 (3), pp. 557-581.

DeBondt, W.F.M. and Thaler, R.H. (1985). Does the stock market overreact? The Journal

of Finance, Vol. 40 (3), pp. 793-808.

DeLong, J. B., Shleifer, A., Summers, L. and Waldmann, R.J. (1990). Positive feedback

investment strategies and destabilizing rational speculation. The Journal of

Finance, Vol. 45 (2), pp. 375-395.

Devas, J.L., Fama, E.F. and French, K.R. (2000). Characteristics, covariances, and

average returns: 1929 to 1997. The Journal of Finance, Vol. 55 (1) pp. 389-406.

Dorner, A.W. (2005). Stock market reactions to financial information. Journal of Human

Resource Costing & Accounting, Vol. 9, Issue: 2, pp. 94-111.

Doskeland, T.M. and Hvide, H.K. (2011). Do individual investors have asymmetric

information based on work experience? The Journal of Finance. Vol. LXVI, No.3,

pp.1011-1041.

Easley, D. and O’Hara, M. (1992). Time and the process of security price adjustment. The

Journal of Finance, Vol. 47, pp. 577–604.

Eaves, J. and Williams, J. (2010). Are intraday volume and volatility U-shaped after

accounting for public information? American Journal of Agriculture Economics,

Vol. 92, Issue: 1, pp. 212-227.

Ederington, L.H. and Lee, J.H. (1993). How markets process information: News releases

and volatility, The Journal of Finance, Vol. XLVII, No.4, pp.1161-1191.

Einhorn, H. J. (1980). Overconfidence in judgment. New Directions for Methodology of

Social and Behavioral Science, Vol. 4, pp. 1–16.

Einhorn, H. J. and Hogarth, R.M. (1978). Confidence in Judgment: Persistence of the

Illusion of Validity. Psychological Review, Vol. 85, No. 5, pp. 395-416.

Engelberg, J.E. and Parsons, C.A. (2011). The causal impact of media in financial

markets. The Journal of Finance, Vol. 66 (1), pp. 67-97.

Epstein, L.G. and Schneider, M. (2008). Ambiguity, information quality, and asset

pricing. The Journal of Finance, Vol. LXIII, No.1, pp.197-228.

Fama, E. (1965). The behavior of stock-market prices. The Journal of Finance, Vol.38,

No.1, pp. 34-105.

182

Fama, E.F. (1972). Components of investment performance. The Journal of Finance, Vol.

27 (3), pp. 551-567.

Fama, E.F. (1998). Market efficiency, long-term returns, and behavioral Finance, Journal

of Financial Economics, Vol. 49, pp. 283-306.

Fama, E.F. and French, K.R. (1992). The cross-section of expected stock returns. The

Journal of Finance, Vol. 47 (2), pp. 427-465.

Fama, E.F. and French, K.R. (1993). Common risk factors in the returns on stocks and

bonds. Journal of Financial Economics, Vol. 33, pp. 3-56.

Fama, E.F. and French, K.R. (1995). Size and book-to-market factors in earnings and

returns. The Journal of Finance, Vol. 50 (1), pp. 131-156.

Fama, E.F. and French, K.R. (1996a). The CAPM is wanted, dead or alive. The Journal of

Finance, Vol. 51 (5), pp. 1947-1958.

Fama, E.F. and French, K.R. (1996b). Multifactor explanations of asset pricing

anomalies. The Journal of Finance, Vol. 51 (10), pp. 55-84.

Fama, E.F. and French, K.R. (1997). Industry costs of equity, Journal of Financial

Economics, Vol. 43, pp. 153–193.

Fama, E.F. and French, K. (2002). The equity premium. The Journal of Finance, Vol. 57

(2), pp. 637-659.

Fama, E.F. and French, K.R. (2008). Dissecting anomalies. The Journal of Finance, Vol.

63 (4), pp. 1653-1676.

Fama, E.F. and MacBeth, J. (1973). Risk, return and equilibrium: Empirical tests. Journal

of Political Economy, Vol. 81(3), pp. 607-636.

Fang, L. and Peress, J. (2009). Media coverage and the cross-section of stock returns. The

Journal of Finance, Vol. 64 (5), pp. 2023-2052.

Foucault, T., Sraer, D. and Thesmar, D.J. (2011). Individual investors and volatility. The

Journal of Finance, Vol. LXVI, No. 4, pp. 1369-1406.

Francis, J. C. (1986). Investments: Analysis & Management (ed.), McGraw-Hill Book

Company.

French, K.R. (1980). Stock returns and the weekend effect. Journal of Financial

Economics, Vol.8, pp. 55-69.

Gervais, S. and Odean, T. (2001). Learning to be overconfident. The Journal of Finance,

Vol.14, No.1, pp.1-27.

Gitman, L. and Vandenberg, P. (2000). Cost of capital techniques used by major U.S.

firms: 1997 vs. 1980. Financial Practice and Education, Vol.10, pp. 53–68. 

183

Goh, J.C. and Ederington, L.H. (1993). Is a bond rating downgrade bad news, good news,

or no news for stockholders? The Journal of Finance, Vol.48, No.5, pp.2001-

2008.

Greenwald, B. and Stein, J. (1988). The task force report: The reasoning behind the

recommendations, The Journal of Economic Perspectives, Vol. 2, pp. 3-23.

Grinblatt, M. and Moskowitz, T.J. (2004). Predicting stock price movements from past

returns: the role of consistency and tax-loss selling. The Journal of Financial

Economics, Vol. 71, pp. 541-579.

Gurun, U.G. and Butter, A.W. (2012). Don't believe the hype: Local media slant, local

advertising, and firm value, The Journal of Finance, Vol. LXVII, No. 2, pp. 561-

597.

Hasbrouck, J. (1991). Measuring the information content of stock trades. The Journal of

Finance, Vol. XLVI, No.1, pp.179-207.

Haugen, R. A. and Baker, N. L. (1996). Commonality in the determinants of expected

stock returns, Journal of Financial Economics, Vol. 41, pp. 401-39.

Hertzberg, A. Liberti, J.M. and Paravisin, D. (2010). Information and incentives inside

the firm: Evidence from Loan Officer rotation. The Journal of Finance, Vol.

LXV, No.3, pp.795-828.

Hirshleifer, D. (2001). Investor psychology and asset pricing. The Journal of Finance.

Vol. LVI, No.4, pp.1533-1597.

Hirshleifer, D. Lim, S.S. and Teoh, S.H. (2009). Driven to distration: Extraneous events

and underreaction to earnings news. The Journal of Finance, Vol. LXIV, No.5,

pp.2289-2325.

Hong, H. and Stein, J.C. (1999). A Unified Theory of Underreaction, Momentum

Trading, and Overreaction in Asset Markets, The Journal of Finance, Vol. 54 (6),

pp. 2143-2184.

Hong, H., Lim, T. and Stein, J.C. (2000). Bad news travels slowly: Size, analyst

coverage, and the profitability of momentum strategies. The Journal of Finance,

Vol.LV, No.1, pp.265-295.

Ikenberry, D., Lakonishok, J. and Vermaelen, T. (1995). Market underreaction to open

market share repurchases. Journal of Financial Economics, Vol. 39, pp. 181-208.

Jaffe, J., Keim, D.B. and Westerfield, R. (1989). Earning yields, market values, and stock

returns. The Journal of Finance, Vol. 44 (1), pp. 135-148.

184

Jagadeesh, N. and Titman, S. (1993). Returns to buying winners and selling losers:

Implications for stock market efficiency, The Journal of Finance, Vol. 48, pp. 65-

91.

Jagannathan, R. and Wang, Z. (1996). The CAPM is alive and well, The Journal of

Finance, forthcoming 51.

Jegadeesh, N. and Titman, S. (1993). Returns to buying winners and selling losers:

Implications for stock market efficiency. The Journal of Finance, Vol. XLVIII,

No.1, pp. 65-91.

Jiang, G., Lee, C.M.C. and Zhang, G.Y. (2004). Information Uncertainty and Expected

Returns. Unpublished paper.

Kaniel, R., Saar, G. and Titman, S. (2008). Individual investor trading and stock returns.

The Journal of Finance, Vol. LXIII, No.1, pp. 273-310.

Klibanoff, P., Lamont, O. and Wizman, T.A. (1998). Investor reaction to salient news in

closed-end country funds. The Journal of Finance, Vol. 53 (2), pp. 673-699.

Kothari, S. P., Shanken, J. and Sloan, R. (1992). Another look at the cross-section of

expected stock returns, Mimeo, University of Rochester.

Kothari, S.P., Shanken, J. and Sloan, R.G. (1995). Another look at the cross-section of

expected return. The Journal of Finance, Vol. 50 (1), pp. 185-224.

Kothari, S. P., Lewellen, J. W. and Warner, J. B. (2006). "Stock Returns, Aggregate

Earnings Surprises, and Behavioral Finance," Journal of Financial Economics,

79(3), 537-68.

La Porta, R. (1993). Expectations and the cross-section of stock returns, Mimeo, Harvard

University

La Porta, R. (1996). Expectations and the cross-section of stock returns. The Journal of

Finance, Vol. 51 (5), pp. 1715-1742.

Porta, R., Lakonishok, J., Shleifer, A. and Vishny, R. (1997). Good news for value stocks:

Further evidence on market efficiency. The Journal of Finance, Vol.LII, No.2, pp.

859-874.

Lakonishok, J., Shleifer, A. and Vishny, R.W. (1994). Contrarian investment,

extrapolation and risk. The Journal of Finance, Vol. 49 (5), pp. 1541-1578.

Lee, C.M.C., Ready, M.J. and Seguin, P.J. (1994). Volume, Volatility, and New York

Stock Exchange Trading Halts. The Journal of Finance, Vol. XLIX, No.1, pp.

183-214.

185

Linter, J. (1965). The valuation of risk assets and the selection of risky investments in

stock portfolios and capital budgets. Review of Economics and Statistics, Vol. 47,

pp. 13-37.

Loughran, T. and Ritter, J. (1995). The new issues puzzle. The Journal of Finance, Vol.

50 (1), pp. 23-52.

Lusardi, A. and Mitchell, O. S. (2006). Financial literacy and planning: Implications for

retirement well-being. Pension Research Council WP 2006-1.

Maheu, J.M. and McCurdy, T.H. (2004). News arrival, jump dynamics, and volatility

components for individual stock returns. The Journal of Finance, Vol. LIX, No.2,

pp. 755-793.

Malmendier, U. and Tate, G. (2008). Who makes acquisitions? CEO overconfidence and

the market's reaction, Journal of Financial Economics, Vol. 89 (1), pp. 20-43.

Markowitz, H. (1952). Portfolio selection. The Journal of Finance, Vol. 7, pp. 77-91.

Martin, G. S. and Puthenpurackal, J. (2008). Imitation is the sincerest form of flattery:

Warren Buffett and Berkshire Hathaway,Working paper, American University—

Kogod School of Business.

Merton, R.C. (1974). On the pricing of corporate debt: The risk structure of interest rates,

The Journal of Finance, Vol. 29, pp. 449-470.

Merton, R.C. (1987). A simple model of capital market equilibrium with incomplete

information. The Journal of Finance, Vol. XLII, No.3, pp. 483-510.

Michaely, R. Thaler, R.H. and Womack, K.L. (1995). Price reactions to dividend

initiations and omissions: Overreaction or Drift? The Journal of Finance, Vol. L,

No.2, pp.573-608.

Mitchell, M.L. and Mulherin, J.H. (1994). The impact of public information on the stock

market. The Journal of Finance, Vol. XLIX, No.3, pp. 923-950.

Odean, T. (1998). Volume, volatility, price, and profit when all traders are above average.

The Journal of Finance, Vol. LIII, No.6, pp. 1887-1934.

Odean, T. (1999). Do Investors Trade Too Much? The American Economic Review, Vol.

89, No.5, pp.1279-1298.

Pastor, L. and Stambaugh, R.F. (2003). Liquidity risk and expected stock returns. Journal

of Political Economy, Vol. 111, No. 3, pp. 642–685.

Poterba, J.M. and Summers, L.H. (1988). Mean reversion in stock prices: Evidence and

Implications, Journal of Financial Economics, Vol. 22 (1), pp. 27-59.

Pradhan, R.S. (1993). Stock merket behaviour in a small capital market: A case of Nepal.

The Nepalese Management Review, Vol. 9 (1), pp. 23-49.

186

Pradhan, R.S. and Balampaki, S.B. (2004). Fundamentals of stock returns in Nepal.

SEBON Journal, Vol. 1 (1), pp. 8-24.

Prasai, B. K. (2010). CAPM Anomalies and Pricing of Equity: A Case of Nepal.

Unpublished M. Phil. Dissertation, Office of the Dean, Faculty of Management,

Tribhuvan University.

Rendleman, R.J., Jones, C.P. and Latane, H.A. (1982) Empirical anomalies based on

unexpected earnings and the importance of risk adjustment, Journal of Financial

Economics, Vol. 10, pp. 269–287.

Ritter, J.R. (1988). The buying and selling behavior of individual investors at the turn of

the year. The Journal of Finance, Vol. XLIII, No.3, pp. 701-717.

Rosenberg, B., Kenneth, R., and Lanstein, R. (1985). Persuasive evidence of market

inefficiency, Journal of Portfolio Management, Vol. 11, pp. 9-11.

Russo, J.E. and Schoemaker, J.H. (1992). Managing Overconfidence, Sloan Management

Review, Vol.33, No.2, pp.7-17.

Schwert, G.W. (1989). Why does stock market volatility change over time? The Journal

of Finance, Vol. XLIV, No.5, pp. 1115-1153.

Sharpe, W.F. (1964). Capital asset prices: A theory of market equilibrium under

conditions of risk. The Journal of Finance, Vol. 19, pp. 425-442.

Stattman, D. (1980). Book values and stock returns. The Chicago MBA: A Journal of

Selected Papers, Vol. 4, pp. 25-45.

Sun, L. and Wei, K.C.J. (2011). Intangible information and analyst behavior. Paper

presented in seminar at the Hong Kong University of Science and Technology,

Hong Kong.

Tetlock, P.C. (2007). Giving content to investor sentiments: The role of media in the

stock market. The Journal of Finance, Vol. LXII, No.3, pp.1139-1168.

Tumarkin, R. and Whitelaw, R.F. (2001). News or Noise? Internet postings and stock

prices. Financial Analysts Journal, Vol.57, No.3, pp.41-51.

Urich, T. and Wachtel, P. (1981). Market response to the weekly money supply

announcements in the 1970s. The Journal of Finance, Vol. XXXVI, No.5,

pp.1063-1072.

Van Rooij, M., Alessie, R. and Lusardi, A. (2007). Financial literacy and stock market

participation, NBER Working Paper 13565.

Vassalou, M. and Xing, Y. (2004). Default risk in equity returns. The Journal of Finance,

Vol. LIX, No.2, pp.831-868.

187

Vega, C. (2006). Stock price reaction to public and private information. Journal of

Financial Economics, Vol.82, pp.103-133.

Veronesi, P. (2000). How does information quality affect stock returns? The Journal of

Finance, Vol.LV, No.2, pp.808-837.

Vuolteenaho, T. (2002). What drives firm level stock returns? The Journal of Finance,

Vol. 57 (1), pp. 233-264.

Wang, F.A. (2001). Overconfidence, Investor Sentiment, and Evolution, Journal of

Financial Intermediation, Vol. 10 (2), pp. 138-170.

Watanbe, M. (2008). Price volatility and investor behavior in an overlapping generations

model with information asymmetry. The Journal of Finance, Vol. 63 (1), pp. 229-

272.

Weinstein, N. D. (1980). Unrealistic Optimism About Future Life Events. Journal of

Personality and Social Psychology, Vol. XXXIX, No. 5, pp. 806–20.

Worthington, A.C. (2006). Political cycles and risk and return in the Australian stock

market, Menzies to Howard, School of Accounting & Finance, University of

Wollongong, Working Paper 6.

Zarowin, P. (1989). Does the stock market overreact to corporate earnings information?

The Journal of Finance, Vol. 44 (5), pp. 1385-1399.

Zhang, X.F. (2006). Information Uncertainty and Stock Returns. The Journal of Finance,

Vol. 61 (1), pp. 105-136.