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Identifying macroeconomic determinants of daily equity
market returns
An Australian study
Stefan Mero
Bachelor of Economics (Hons)
2016
This thesis is presented for the degree of Master of Philosophy at
The University of Western Australia
Business School Accounting and Finance
Abstract
Understanding macroeconomic risk is a fundamental aspect of economic and financial
decision-making. More recently, attention has turned to identifying macroeconomic
variables as risk factors (Chen, Ross & Roll 1986; Chan, Karceski & Lakonishok
1998; Flannery & Protopapdakis 2002).
Most research, to date, has focused on the relationship between macroeconomic data
values and stock market prices over long time horizons. This study extends the
existing Australian stock market based literature by examining the relationship
between macroeconomic news and stock market returns/return volatility at daily a
level, in an event study framework. The study covers the period before, during and
after the Global Financial Crisis in 2008 to determine whether the effects of news
differ during different phases of stock market activity.
In the stock market boom leading up to the Crisis, higher than expected overnight
cash rate news was found to have a negative impact on stock returns that disappears
in the subsequent period of subdued stock market price growth after the Crisis.
Macroeconomic fundamentals - such as unemployment, the consumer price index and
real gross domestic product - matter only after the onset of the Crisis. Over the whole
period, consumer sentiment and real gross domestic product surprises are the only
macroeconomic variables to impact stock market volatility.
10 August 2016 i
Contents
1 Introduction .................................................................................................... 7
1.1 Theoretical, Empirical and Industry Perspectives ............................................ 7
1.2 Thesis Contribution ........................................................................................ 10
1.3 Thesis Structure .............................................................................................. 14
2 Literature Review ......................................................................................... 16
2.1 Theoretical Background ................................................................................. 16
2.2 Australian Studies ........................................................................................... 20
2.2.1 Australian Stock Market Returns ................................................................... 20
2.2.2 Australian Stock Market Return Volatility ..................................................... 30
2.2.3 The Australian Stock Market and Efficient Market Hypothesis .................... 31
2.3 Foreign Studies ............................................................................................... 35
2.3.1 Foreign Stock Markets and Macroeconomic Surprises .................................. 35
2.3.2 Business Cycles and Macroeconomic Factor relationships with Stock
Markets ........................................................................................................... 41
2.4 Conclusions from the Literature ..................................................................... 42
3 Hypothesis ..................................................................................................... 46
3.1 Unemployment ............................................................................................... 49
3.2 Balance of Trade............................................................................................. 52
3.3 Retail Sales ..................................................................................................... 54
3.4 Producer Price Index ...................................................................................... 56
3.5 Consumer Price Index .................................................................................... 58
3.6 Real Gross Domestic Product ......................................................................... 60
3.7 Overnight Cash Rate....................................................................................... 62
3.8 Consumer Sentiment ...................................................................................... 64
4 Methodology .................................................................................................. 67
4.1 Returns ............................................................................................................ 67
4.2 Surprises (Unexpected Components of Announcements) .............................. 67
4.3 Control Variables............................................................................................ 68
4.4 Returns Estimation ......................................................................................... 70
4.5 Volatility Estimation ...................................................................................... 72
5 Data ................................................................................................................ 75
5.1 Stock Market Indices ...................................................................................... 75
5.1.1 Stationarity of Stock Returns .......................................................................... 79
10 August 2016 ii
5.2 Macroeconomic Surprises .............................................................................. 80
5.2.1 Forecasts ......................................................................................................... 80
5.2.2 Announcements .............................................................................................. 82
5.2.3 Surprises ......................................................................................................... 84
5.3 Control Variables .......................................................................................... 102
6 Results .......................................................................................................... 110
6.1 Continuous Model Results ........................................................................... 110
6.2 Dummy Variable Based Model Results ....................................................... 115
6.3 Continuous Model Results: Pre- and Post-Global Financial Crisis .............. 120
6.4 Dummy Variable Based Model Results: Pre- and Post-Global Financial
Crisis ............................................................................................................. 125
6.5 Robustness Tests .......................................................................................... 132
6.6 Summary and Discussion of Results ............................................................ 133
7 Conclusion ................................................................................................... 141
7.1 Thesis Contribution ...................................................................................... 141
7.2 Main Results ................................................................................................. 142
7.3 Limitations and Possible Extensions ............................................................ 147
7.4 Final Conclusion ........................................................................................... 149
8 References.................................................................................................... 152
9 Appendices .................................................................................................. 163
9.1 Appendix A – Structure of Macroeconomic Announcement Data and
Dates ............................................................................................................. 163
9.2 Appendix B – Model Fitting ......................................................................... 166
9.2.1 Fitting ARMA for Stock Market Return Modelling ..................................... 166
9.2.2 Fitting GARCH/EGARCH for Stock Market Return and Time Varying
Volatility Modelling ..................................................................................... 168
9.3 Appendix C – Robustness Tests ................................................................... 171
9.3.1 All Ordinaries Index Based Regressions ...................................................... 171
9.3.2 Single Macroeconomic Variable Regressions .............................................. 184
9.3.3 Alternate Break-Point Regressions ............................................................... 210
10 August 2016 iii
Tables
Table 1 Australian Literature Review Summary ................................................ 32
Table 2 Foreign Literature Review Summary .................................................... 39
Table 3 ASX 200 Index Total Daily Returns –Summary Statistics ................... 78
Table 4 All Ordinaries Index Total Daily Returns – Summary Statistics .......... 79
Table 5 Augmented Dickey-Fuller Unit Root Tests - No Drift or Trend ........... 80
Table 6 Money Market Services Consensus Macroeconomic Forecasts ........... 81
Table 7 Other Macroeconomic Forecasts ........................................................... 82
Table 8 Macroeconomic Announcement Values ............................................... 83
Table 9 Summary Statistics for Macroeconomic Surprises ............................... 84
Table 10 Real GDP Growth – ADF Test and Akaike Information Criterion ....... 95
Table 11 Consumer Sentiment – ADF Tests and Akaike Information Criteria . 100
Table 12 Continuous EGARCH model results based on full period sample ..... 111
Table 13 Dummy variable EGARCH model results based on full period
sample ................................................................................................. 116
Table 14 Continuous EGARCH model results: Pre- and Post-GFC .................. 120
Table 15 Dummy variable EGARCH model results: Pre/Post-GFC ................. 125
Table 16 Summary of Results by Macroeconomic Variable.............................. 132
Table 17 Summary of Results Surviving Robustness Tests ............................... 133
Table 18 Summary of Results - Returns............................................................. 151
Table 19 Summary of Results - Return Volatility .............................................. 151
Table 20 Macroeconomic Announcement Date Structure ................................. 164
Table 21 Raw Macroeconomic Announcement Data Series Structure .............. 164
Table 22 AIC - All Ordinaries Total Returns ARMA Regression ..................... 166
Table 23 Q-Statistics on ARMA Model Squared Standardised Residuals......... 167
Table 24 ARCH LM Test on ARMA Squared Residuals .................................. 167
Table 25 Continuous Model using All Ordinaries Index based Returns ............ 172
Table 26 Dummy Variable Model using All Ordinaries Index .......................... 175
Table 27 Continuous Model using All Ordinaries Index: Pre- and Post-GFC ... 178
Table 28 Dummy Variable Model using All Ordinaries Index: Pre- and Post-
GFC ..................................................................................................... 181
Table 29 Unemployment Dummy Variable based Regression: Post-GFC ........ 185
Table 30 Retail Sales Dummy Variable based Regression: Pre-GFC ................ 187
Table 31 Producer Price Index Continuous Regression ..................................... 189
Table 32 Consumer Price Index Continuous Regression ................................... 191
Table 33 Consumer Price Index Continuous Regression: Post-GFC ................. 193
10 August 2016 iv
Table 34 Consumer Price Index Dummy Variable based Regression: Post-
GFC ..................................................................................................... 195
Table 35 Real GDP Dummy Variable based Regression ................................... 197
Table 36 Real GDP Dummy based Regression: Pre- and Post-GFC ................. 199
Table 37 Overnight Cash Rate Continuous Regression: Pre-GFC ..................... 201
Table 38 Overnight Cash Rate Dummy Variable based Regression: Pre-GFC . 203
Table 39 Consumer Sentiment Index Continuous Regression ........................... 205
Table 40 Consumer Sentiment Index Dummy Variable based Regression........ 207
Table 41 Consumer Sentiment Index Dummy Variable based Regression: Post-
GFC ..................................................................................................... 209
Table 42 Continuous Model Results using Alternate Breakpoints: Pre- and Post-
GFC ..................................................................................................... 211
Table 43 Dummy Variable Model Results using Alternate Breakpoints: Pre- and
Post-GFC ............................................................................................. 214
10 August 2016 v
Figures
Figure 1 ASX 200 Index Total Daily Returns ..................................................... 77
Figure 2 Australian All Ordinaries Index Total Daily Returns ........................... 78
Figure 3 Unemployment Rate Surprises .............................................................. 86
Figure 4 Balance of Trade Surprises .................................................................... 88
Figure 5 Retail Sales Surprises ............................................................................ 90
Figure 6 Producer Price Index Surprises ............................................................. 92
Figure 7 Consumer Price Index Surprises ........................................................... 93
Figure 8 Real GDP Surprises ............................................................................... 96
Figure 9 Interest Rate Surprises ........................................................................... 98
Figure 10 Consumer Sentiment Surprises ........................................................... 101
Figure 11 Brent Crude Oil One-Month Futures Prices and Returns .................... 103
Figure 12 Lagged US Standard and Poor’s 500 Index Returns ........................... 104
Figure 13 Term Spread - Australian Commonwealth Government Bonds ......... 107
Figure 14 5-Year Australian Corporate Bond Default Spread ............................ 109
Figure 15 ASX 200 Index - Sector Composition ................................................. 134
Figure 16 ASX 200 Index - Total Daily Returns ................................................. 136
Figure 17 Westpac-Melbourne Institute Consumer Sentiment Index ................. 137
Figure 18 Consumer Sentiment Surprises ........................................................... 138
Figure 19 EGARCH Normal Distribution Quantile Plot ..................................... 170
10 August 2016 6
Acknowledgements
My supervisors, Professor Richard Heaney and Dr. Joey Wenling Yang, spent many
hours reading, editing, analysing data and advising me. I am thankful for their efforts,
patience and ability to stimulate a creative learning and research environment. My
research is a richer tapestry of findings on account of Professor Heaney’s ability to
encourage creative thinking, and his guidance on research design. Dr Yang has
improved my understanding of financial econometrics, academic conventions in
drafting research and skills in managing the scope of research.
My editor, Eleanor Mulder and my father, Jonn Mero, also spent hours reading,
editing and providing useful comments on my drafting. My employer, Greg
Watkinson, also provided useful suggestions regarding my written communication of
ideas and document structure. These people improved the readability of my thesis
immeasurably.
Finally, I would like to thank Adam Hearman and Robyn Oliver, for minimising the
administrative burden and thereby making my experience as a research student at the
university all the more pleasant.
10 August 2016 7
1 Introduction
Understanding macroeconomic risk, in order to price individual assets, is a
fundamental aspect of economic and financial decision-making.1 More recently,
attention has turned to identifying macroeconomic variables as risk factors (Chen,
Ross & Roll 1986; Chan, Karceski & Lakonishok 1998; Flannery & Protopapdakis
2002). A well-functioning financial system should incorporate important
macroeconomic information in stock prices and returns quickly and rationally, or in
the words of Fama (1970, p.383), the market should be ‘semi-strong form’ efficient.
The returns on an equity market index in an economy with a well-functioning
financial system should, therefore, respond quickly to important macroeconomic risk
factors because the index is comprised of individual firms. The effect of these risk
factors should be identifiable in short run returns. The idea that certain
macroeconomic variables are risk factors in stock market returns is well accepted
from a theoretical, empirical and industrial perspective.
1.1 Theoretical, Empirical and Industry Perspectives
From a theoretical perspective, the share price of individual firms that are used to
construct a stock market index, within an economy, are affected by broader economic
conditions. This is because economic conditions generally affect the expected future
earnings and dividends of individual firms. In addition, expected future earnings and
dividends are related to the firm’s share price through the required rate of return
(Gordon 1962; Campbell & Shiller 1988). The required rate of return itself may also
be affected by macroeconomic factors (Ross 1976). This means any variable affecting
1 For example see Markowitz 1952, Treynor, cited in Ross 1976, p.341, Sharpe 1964, Lintner 1965,
Fama & French 1992, Black 1972, Ross 1976, Jagannathan & Wang 1996.
10 August 2016 8
earnings, dividends, or the required (or expected) future rate of a firm’s return, also
affects a broad market index made up of firms. Market indices should therefore
respond to macroeconomic news (Fama 1981; Schwert 1990).
From an empirical perspective, the link between macroeconomic variables and stock
returns is supported by evidence from major economies, such as the United States
(US), Europe and Japan. In the US, Fama (1981) observed that future real activity,
measured by industrial production and real Gross National Product (GNP), eliminated
the explanatory power of inflation when included as a variable in a regression used
for explaining stock returns. Schwert (1990) confirmed the relationship between
growth rates in future production and stock returns, which was discovered by Fama
(1981) using 100 years of data. An early application of Arbitrage Pricing Theory
(APT) by Chen, Ross & Roll (1986) identified macroeconomic variables and non-
equity asset returns as risk factors when explaining equity returns. Industrial
production, changes in risk premia, the term structure of the yield curve and also
inflation were found to be significant explanatory factors. Cheung and Ng (1998)
concluded that future real GNP growth has a significant positive influence on US
stock returns. Ratanapakorn & Sharma (2007) showed money supply, industrial
production, inflation, the Japanese Yen/US dollar exchange rate and short-term
interest rates are positively related to US stock returns, while long-term interest rates
are negatively related to stock returns. The relationship between US stock prices and
the two variables - industrial production and long-term interest rates - is also found in
a later study by Humpe & Macmillan (2009). However, in contrast to Ratanapakorn
& Sharma (2007), Humpe & Macmillan (2009, p.118) found a negative relationship
between inflation and stock returns.
10 August 2016 9
In Europe, Asprem (1989) examined the relationship between the major stock index
and macroeconomic variables in ten different countries. Interest rates, inflation,
imports and (perhaps surprisingly) employment were shown as negatively related to
stock prices, whereas changes in future industrial production and broad money supply
were shown as positively related to changes in stock prices. The results of the
cointegrating techniques employed by Cheung and Ng (1998, p.293), for German
stock returns, indicated future real GNP growth has a significant positive influence
on returns.
Following the spectacular rise of the Tokyo Stock Exchange (TSE) leading up to
1990, Mukherjee and Naka (1995) studied the long-term relationship between
Japanese macroeconomic variables and TSE index based returns. Their model found,
in the long run, local currency depreciation, money supply, industrial production, and
short-term interest rates are positively related to stock market returns, while inflation
and long-term interest rates are negatively related. Cheung & Ng (1998) reported
similar results for Japan, finding lagged money supply and future real GNP are both
positively related to Japanese stock market returns. Humpe and Macmillan (2009)
also documented Japanese stock returns have a positive relationship with industrial
production. Unlike Mukherjee and Naka (1995), Humpe and Macmillan (2009, p.118)
found Japanese returns have a negative relationship with the money supply in Japan.
Turning to an industrial perspective, Australian financial media coverage on the share
market frequently and continuously attributes changes in daily returns to
macroeconomic variables, such as employment:
‘Shares on Thursday fell for a fourth straight day, but ended well off the day's
lows, thanks to strong jobs data …’ (Cauchi 2015)
10 August 2016 10
Real GDP:
‘Stocks ended near unchanged today after a rally spurred by surprisingly strong
growth in the nation's economy…’ (Australian Associated Press 31 October 2003)
Inflation and interest rates:
‘The Australian share market is expected to open strongly tomorrow with
financial markets awaiting key inflation figures that will offer clues surrounding
possible interest rate rises…’ (Carter 2007).
This highlights it is generally accepted that certain macroeconomic variables are risk
factors in Australian stock returns. In this study, I seek to identify and quantify the
effect of such risk factors in Australian stock returns. I will also assess whether their
effects on stock returns differ between rapid and more subdued periods of stock price
growth, as theory and evidence suggest this may be the case (Shiller 2003;
Binswanger 2004).
1.2 Thesis Contribution
The most common methods employed to detect macroeconomic risk factors in stock
returns are based on APT, present value, or cointegration models, which tend to focus
on the relationships between macroeconomic variables and stock market returns over
time horizons far longer than one day. Few studies examine the effect of the
unexpected component of macroeconomic announcements (news or surprises) on
stock market returns over the short run. Most existing research tends to focus on the
relationship between announced macroeconomic data values and stock market prices
over the long run. The distinction between announced macroeconomic data and news
is an important one. Announced macroeconomic data is the reported value for a
macroeconomic variable, typically by a statistical agency or other sovereign authority.
10 August 2016 11
Macroeconomic news, however, is the difference between the market participant's
expected value of the macroeconomic variable and the announced value. I assume if
there is no difference, then no news exists – news by definition must be new
information, or in other words, a ‘surprise’. Assuming stock markets are efficient, and
an estimate of the market participant’s expected value of a macroeconomic variable
reflects broader market expectations, news associated with this variable should
quickly cause stock market prices to change if it is a risk factor. Any information that
is not new is (by assumption) already reflected in the current market price (Fama
1970, 1991). Those studies that have incorporated news or ‘innovations’ typically do
so within a cointegrating framework, which again, have a focus on long run
relationships (Cheung & Ng 1998; Humpe & Macmillan 2009). Additionally, a
minority of the macroeconomic risk factor research uses the ‘event study’
methodology, which focuses on a ‘window’ of time around an event, such as a
macroeconomic announcement, to examine the extent to which macroeconomic news
is incorporated in prices.
Studies examining the effect of news on short run stock returns, for a fairly
comprehensive set of macroeconomic variables, have been carried out for the US,
United Kingdom (UK) and also for some European markets (Wasserfallen 1989;
Becker, Finnerty and Friedman 1995; Flannery & Protopapadakis 2002). Australian
studies examining the effects of macroeconomic variables on returns over short time
periods (daily) have, to date, focused on a limited number of macroeconomic
variables (Singh 1993; Singh 1995; Brooks et al 1999; Kim & In 2002; Akhtar et al
2011; Hasan & Ratti 2012). Akhtar et al (2011) found a relationship between
consumer sentiment and Australian stock market returns, and Hasan and Ratti (2012)
found a relationship between oil prices and Australian stock returns. While Kim and
In (2002) found Australian stock return volatility was higher on real GDP
10 August 2016 12
announcement days, the relationships between short run stock market returns and
fundamental macroeconomic variables, such as unemployment, the consumer price
index (CPI) and output, are yet to be established in Australia.2
My literature review yields eight macroeconomic variables as candidates for
examination: (1) unemployment, (2) balance of trade, (3) retail sales, (4) producer
price index (PPI), (5) CPI, (6) real Gross Domestic Product (GDP), (7) overnight cash
rates and (8) consumer sentiment. I examine the effects of these variables on stock
market index returns and volatility using the event study methodology, undertaken
within a regression framework. The regression framework is an exponential
generalised autoregressive conditional heteroscedasticity (GARCH) specification. To
construct macroeconomic surprises, announcement data is sourced from the
Australian Bureau of Statistics (ABS), the Reserve Bank of Australia (RBA) and
Westpac-Melbourne Institute. The corresponding expected values for the
announcements are either sourced from Money Market Services (MMS) Australia or
modelled using an autoregressive integrated moving average (ARIMA) model, which
is based on prior observations of announcements. A time series of surprises is then
constructed for each macroeconomic variable as the difference between the
announcements and their corresponding expected value.
I employ two different variants of the models used to explain returns. The first variant
uses continuous macroeconomic surprise values and attempts to measure the
sensitivity of returns/return volatility to the change in magnitude of macroeconomic
surprises. Put another way, this variant of the model measures the per cent change in
returns/return volatility per one per cent of error in macroeconomic forecasts. The
second model assigns a dummy variable to each macroeconomic announcement day,
2 Kim and In (2002, p.578) found some evidence that real GDP news days are positively related to
futures returns, but not stock returns based on spot prices.
10 August 2016 13
which is equal to one only if the macroeconomic surprise is not equal to zero and zero
otherwise. This captures the average effect of macroeconomic surprises on stock
returns. An additional dummy variable is assigned to macroeconomic surprises
assumed to be ‘bad’ news, which captures additional information on whether bad
news has a different effect to good news.
Good and bad news in this context does not relate to presupposed effects on returns,
rather, it is an assumed perception of whether the news is a good or bad sign for the
economy. My assumption of what constitutes good and bad surprises follows Kim
(2003, p.619) for all variables except cash rates and consumer sentiment, which were
not included in his study. For cash rates, I assume the perspective of a leveraged
entity, and in this case, higher cash rates are deemed bad news because of higher
interest payments and capital costs more generally. For consumer sentiment, I assume
the perspective of an entity that relies on sales activity. Higher levels of consumer
sentiment mean good news because of higher consumer spending and, therefore,
higher sales.
The continuous model finds robust relationships for the CPI and consumer sentiment.
The dummy variable based model detects robust relationships for unemployment, the
CPI, real GDP and the overnight cash rate. These results largely corroborate findings
in the reviewed literature that conclude real GDP has, in particular, a strong positive
relationship with stock market returns.
My study benefits from access to data with a large number of observations falling
over a period of prolonged stock market expansion, contraction and subsequent
subdued growth following the Global Financial Crisis (GFC) in 2008. This enables
me to examine whether relationships differ during these phases of stock market
activity, as is empirically observed by Binswanger (2004) in foreign markets.
10 August 2016 14
With the exception of the overnight cash rate, all of the significant macroeconomic
variables appear to explain stock market returns only in the period following the GFC.
Lagged stock returns appear to play a greater role than fundamental macroeconomic
factors during the stock market boom leading up to the GFC. This is consistent with
Binswanger’s (2004, p.248) finding that fundamentals cease to explain stock prices
during stock market booms. Amongst the variables with significant coefficients, all
news assumed to be good economic news is associated with increased returns, while
all news assumed to be bad economic news is associated with decreased returns. Real
GDP and the consumer sentiment index significantly influence return volatility both
before and after the GFC. Real GDP news exhibits asymmetric effects, which shows
good news is more important than bad news.
1.3 Thesis Structure
In Chapter 2, I review the relevant literature. I begin by reviewing some of the most
relevant theories, such as the APT, the efficient markets hypothesis (EMH) and the
event study methodology. An overview of studies on the influence of macroeconomic
variables, specific to Australian share market returns and return volatility, follows.
Foreign studies, specifically examining the effect of macroeconomic surprises on
stock markets, are also reviewed because of the similarity of their research design
with my study. I finish by drawing conclusions from the literature that impact my
research design.
The relationships between macroeconomic variables and stock returns, observed in
the literature, are reserved for discussion in Chapter 3. In this chapter, I outline my
null hypotheses with respect to the effect of each of the macroeconomic variables'
associated surprises on Australian stock returns, along with details of alternative
hypotheses. Chapter 4 explains how data is processed and how the econometric
10 August 2016 15
models are used to test for relationships between macroeconomic variables and stock
returns. The data sources and their characteristics are one of the most important parts
of my study. These are presented and discussed at some length in Chapter 5. Chapter
6 is a discussion of the results vis-à-vis the hypothesis chapter, followed by an
overview of robustness tests and a discussion on the results surviving the robustness
tests. Conclusions are drawn in Chapter 7, which also highlights some areas for future
research.
10 August 2016 16
2 Literature Review
My literature review begins by outlining some of the theories most relevant to my
study. I note the issues found in the literature that studies these theories, and their
implications for this study. Studies that examine the relationship between
macroeconomic variables and the stock market in Australia are reviewed with a focus
on the aspects relevant to my study. Foreign studies that examine the relationship
between macroeconomic variables and stock markets, specifically using
macroeconomic surprises, are also discussed in a similar way. Finally, conclusions
are drawn from the literature, and the contribution of my thesis is explained.
2.1 Theoretical Background
Below are some well-established theories and a description of their relevance to my
study.
Arbitrage pricing theory proposes the expected return on a particular asset can be
explained by risk premia that are associated with a number of macroeconomic risk
factors, as well as the risk free rate of return (Ross 1976). Macroeconomic risk factors
are identified using a statistical process. If the price of an asset differs from that
predicted by the model, an arbitrage opportunity exists, and in an efficient market,
this is rapidly taken advantage of. My study has parallels to the multifactor model
proposed by Ross (1976) because it identifies potential macroeconomic risk factors
and uses them to explain returns. Ross’s (1976) focus, however, is on stock market
returns in excess of the risk free rate, whereas my study examines the return on the
stock market index without deducting the risk free rate. Also, unlike Ross (1976), I
examine whether prices react quickly to the unexpected component of macroeconomic
risk factor announcements.
10 August 2016 17
The efficient market hypothesis (EMH) is important to my study because it relates to
the speed and degree to which financial market prices incorporate new information or
surprises. My study is based on the assumption this happens rapidly in an efficient
stock market. If a macroeconomic announcement has an effect on stock returns, then
I expect this to be reflected in stock prices on the day of the announcement.
The EMH postulates a capital market is efficient if prices always ‘fully reflect’ all
available information (Fama 1970, p.383). To establish the point at which this
hypothesis breaks down, Fama (1970) reviewed tests based on three subsets of
information:
weak form tests based on historical prices;
semi-strong form tests based on obviously available (public) information, such
as company and economic announcements; and
strong-form tests based on privately available information.
No important empirical evidence was found to disprove the hypothesis that security
prices reflected the first two information sets. However, limited evidence that went
against the hypothesis, tested on the strong-form set of information, was found. Semi-
strong form tests, in particular, are concerned with the speed at which prices adjust to
publicly available information. Tests based on company and macroeconomic
announcements indicated prices reacted at the time of the announcement.
Additionally, there is some evidence to suggest prices moved in anticipation of the
announcement, and these movements appeared to be unbiased. My study proceeds on
the assumption stock prices incorporate the semi-strong form information set.
The cost of getting prices to reflect information is not always zero, and this is
explicitly accounted for in Fama’s 1991 study. Consequently, prices are hypothesised
to reflect information, often to the point where marginal profits, made from acting on
10 August 2016 18
information, are offset by the marginal cost. Additionally, Fama (1991, p.1575)
emphasised a test of market efficiency requires an equilibrium asset price determined
by a model that itself may be the source of pricing errors. A rejection of the EMH
could be a result of a bad pricing model and/or market inefficiency. This was referred
to as the joint-hypothesis problem. The weak, semi-strong and strong form tests were
replaced by the following three classifications of research identified in the literature:
tests for return predictability;
event studies; and
test for private information.
Tests for return predictability and event studies are the most relevant to my study, and
so, the most pertinent of Fama’s (1991) findings on these tests are outlined below.
Aside from testing the EMH, tests for return predictability are important in the
formulation of the models used in my study when establishing a relationship between
stock returns and macroeconomic announcements. To maximise the possibility of
detecting such relationships, the effect of other variables that affect or ‘predict’ returns
needs to be both accounted and controlled for when testing.
Tests for return predictability, reviewed in Fama (1991, p.1578), included tests for
autocorrelation (that is, the effect of past returns on future returns). The studies found
significant positive autocorrelation, and it was more prevalent in those market indices
with many small stocks. This point is important when considering whether a stock
index with many smaller capitalised stocks should be used in a study of stock returns.
This issue is revisited when selecting a stock return index. With respect to market
efficiency, however, Fama (1991, p.1609) noted the predictable part of returns was
only a small proportion of the variance, and could not warrant a conclusion of
substantial market inefficiency. Incidentally, the studies reviewed also showed
10 August 2016 19
differences between return variances for trading and non-trading hours. This is linked
to differences in the flow of information collected during trading and non-trading
hours.
The findings on autocorrelation and differing variances around non-trading hours,
such as holidays, indicate these effects should be and are controlled for in my study.
Fama’s (1991, p.1586) review acknowledged tests based on the volatility of returns
are a useful way to show expected returns can vary through time. The tests reviewed,
however, could not explain whether this variation is rational (and therefore efficient)
or a result of irrational bubbles. Early literature reviewed provided evidence of several
cases of return seasonality, in addition to periods such as holidays. These included the
day-of-the-week effect, intra-day effects and the January effect. This evidence
highlights such effects may also need to be controlled for.3
With respect to market efficiency, Monday, holiday and end-of-month effects were
small compared to the bid-ask spread of the average stock, while the January effect
was small in relation to the bid-ask spread of small stocks. Fama’s (1991, p.1587)
view was these effects are market microstructure anomalies, and so, observation of
these effects need not necessarily result in the rejection of the hypothesis of market
efficiency.
Event studies, reviewed in Fama (1991, p.1601), benefited from the use of high
frequency data, which allowed a more precise measurement of the speed at which
stock prices respond to a given event. It also substantially assisted the study to
overcome the joint-hypothesis problem, particularly when stock price responses were
large and concentrated in a few days. This is because the issue of finding an asset-
pricing model that correctly measures daily returns is not so critical for statistical
3 This is controlled for in my model by the use of holiday dummy variables. See Section 5.3.
10 August 2016 20
inference when the abnormal returns are very pronounced and expected return
variation is small. The typical result from these studies is stock prices appear to adjust
within a day of event announcements. This suggests day-to-day changes in stock
prices are suitable for my study. This speed of adjustment was viewed to be consistent
with the hypothesis of market efficiency.
Event studies still do not entirely resolve issues relating to market uncertainty and the
joint hypothesis problem. A common finding in the review of event studies is the
dispersion of returns increases around information events. Event studies only explain
the average variation around events, while the residual variation is unexplained. It is
not, therefore, possible to determine whether the remaining increase in variation is a
rational reaction to uncertainty about new fundamentals or irrational over/under-
reaction, and thus, indicative of inefficiency. This suggests using a model that
accounts for changes in the variance of returns, around the macroeconomic
announcements in this thesis, will result in a more precise test for responses in returns
(and thus, market efficiency) to public announcements.
2.2 Australian Studies
A review of studies on Australian stock market returns is detailed below. This is
followed by a review of studies specifically dealing with stock market return
volatility. In light of the importance of the EMH to my thesis, an additional Australian
stock market study testing the hypothesis is outlined at the end of this section. A
summary of these Australian studies is presented in Table 1.
2.2.1 Australian Stock Market Returns
Gultekin (1983) tests the relationship between stock returns and inflation in a number
of countries including Australia. This study is motivated by the Fisher hypothesis that
10 August 2016 21
the real rate of return on assets is independent of the expected inflation rate in efficient
markets (that is, nominal returns on assets will vary one for one with expected
inflation). Nominal returns were regressed on expected inflation, which was
approximated by the realised inflation rate at the beginning of the return holding
period.4 No significant relationship was found between the two variables in Australia.
These findings indicate, in Australia, inflation realised in a past quarter has no effect
on the nominal returns realised in the subsequent quarter.
Jaffe (1984) tests stock market data for four countries, including Australia, for a
‘week-end’ effect. Other studies, typically based on US data, had found returns were
abnormally high on Friday and abnormally low on Monday. Returns were, therefore,
regressed on dummy variables, representing each trading day. The hypothesis that
returns were equal on each trading day was rejected. Tuesdays were found to have
significantly lower mean returns than all other days.
Jaffe (1984, p.4) hypothesises the effect may be a result of the timing difference
between the Australian stock exchange and the New York stock exchange. The New
York exchange tended to experience its lowest mean returns on Mondays, and by
then, Monday trading in Australia had already closed. A regression of the differences
in returns, between the Australian market and lagged values of the US market on day-
of-the-week dummy variables, found days of the week have unequal effects on the
differential, providing evidence to support the hypothesis. This result highlights the
importance of lagging US return values when testing for these relationships with the
Australian market.
4 For countries other than Australia in this study expected inflation was also estimated using
ARIMA models and derived from short-term interest rate data.
10 August 2016 22
Additional tests are carried out in Jaffe’s study (1984) to determine whether any part
of the day-of-the-week effect found in Australia is independent of those observed in
the US market. The results provided evidence that at least part of the day-of-the-week
effect is unique to the Australian market. Such effects should thus be controlled for
in studies of Australian stock returns.
Singh (1993) conducts a study on the response of Australian stock prices to money
supply announcements. His treatment of the money supply data is of particular
interest for my study because it involves modelling the expected and unexpected
component of a macroeconomic announcement. This is in addition to using surveyed
Money Market Services (MMS) forecasts. Singh (1993, p.48) used money supply
forecasts sourced from MMS Australia for broad money (M3). Multiple ARIMA
models were used for forecasting, so only subsequent models incorporated previously
unavailable information as it became available with each passing day. This ensured
ARIMA forecasts on each day were based only on information available at that time.
This avoided biasing the forecasts toward the actual announced outcomes, which
would have been the case if the unavailable future data were used to fit an ARIMA
model on each day historically.
The announcements detailing the money supply’s preliminary estimates were sourced
from RBA press releases for both narrow money (M1) and M3. Changes in stock price
indices are regressed on both expected and unexpected changes in money supply,
using ARIMA forecasts in one particular case and MMS forecasts in another. At
conventional levels of statistical significance, the results showed no significant
relationship between changes in stock prices and money supply changes. The survey-
based forecasts appear to be somewhat more reflective of market expectations than
ARIMA based forecasts and produced higher absolute values of t-statistics, despite
10 August 2016 23
neither forecast producing statistically significant results at conventional levels
(Singh 1993, p.50). There is, therefore, some support for the use of survey forecasts
over modelled forecasts, though this is a moot point given the lack of strong statistical
support.
Singh (1995) examines the role of current account deficit announcements on a number
of financial markets, including the Australian stock market. Changes in stock prices
were modelled as a function of expected and unexpected announcements, with the
expected component being represented by forecasts. Including dummy variables in
the model controlled for day-of-the-week effects. Monthly announcements of the
current account balances are sourced from the ABS. A survey of the expected value
of the current account deficit was sourced from MMS. An ARIMA model was, again,
used to model expected and unexpected components of announcements as in Singh
(1993). The expected component, based on the MMS survey data, was found to have
an insignificant effect on stock returns, while the unexpected component had a
negative effect significant at the 10 per cent level. Day-of-the-week effects are found
to have no significant effect. When undertaken with ARIMA forecasts as opposed to
MMS data, the analysis produced comparable results but reported slightly lower t-
values. Again, these findings are consistent with Singh’s (1993, p.51) previous
suggestion that MMS surveyed forecasts contain more information than ARIMA
forecasts (based only on past values).
Brooks et al (1999) test the effect of unexpected current account deficit (CAD) and
GDP announcements, including revisions, on daily observations of the All Ordinaries
share price index. An ARIMA model is used to decompose the initial announcements
into their expected and unexpected components, by producing one-step-ahead
forecasts representing the expected component, and forecast errors representing the
10 August 2016 24
unexpected component. An Ordinary Least Squares (OLS) model, regressing returns
on the unexpected component of announcements and revisions, is used to test for
significant effects. The results suggested CAD and GDP announcements and
revisions have no significant effect on returns. The results were unchanged when the
announcements and revisions were separated into good news (positive sign) and bad
news (negative sign) announcements.
An additional point to note in Brooks et al (1999, p.199) is the discussion regarding
the use of MMS forecast survey data in Australia compared to ARIMA models for
forecasting. They advocate the latter on the basis that survey data suffers from the
effects of ‘herding behaviour’, survival bias and its reliance on median expectations
in survey forecasts. They argue the use of the median is inappropriate given there is
no reason to expect the marginal investor to hold the median expectation.
This view is contrary to that of Singh (1993 and 1995), who supports survey-based
data on the basis it has more explanatory power than ARIMA forecasts. Although
none of these studies detected any significant relationships, using either MMS or
ARIMA forecasts, Singh (1993 & 1995) noted some evidence (outlined above) in
favour of the MMS forecasts. In light of Singh’s findings, survey data is generally
preferred over ARIMA forecasts in this study, although it is important to note, the
discussion that occurred in the literature. Where MMS data is not available, I consider
ARIMA forecasts the next best option.
Kim and In (2002) create a model to explain returns in the Australian stock market.
The model uses returns in foreign stock markets, and macroeconomic announcements
in Australia and overseas, as explanatory variables. Daily Australian stock returns
were based on the ASX All Ordinaries index returns, while the Standard and Poor’s
(S&P) 500, FTSE 100 and Nikkei 225 based returns are used to represent the US, UK
10 August 2016 25
and Japanese markets respectively. The study used a bivariate Glosten-Jagannathan-
Runkle (GJR) GARCH model and two-step estimation procedure. Their model makes
specific allowance for asymmetry in stock return relationships with explanatory
variables. This is an important aspect of their analysis. Unemployment, the CPI and
GDP announcements are used from both the Australian and US economy. Dummy
variables for holiday periods are also included.
The model indicates that return volatility in Australia is significantly higher on
announcement days for Australian real GDP. The model also reports a significant
positive relationship between Australian and US/UK returns, and a significant
negative relationship between Australian and Japanese stock market returns. Shocks
in the UK and Japanese stock market have a significant positive impact on the
volatility of the Australian market. US and Australian GDP announcements, as well
as holidays, were positively related to volatility. Asymmetry terms in the model were
significant, and tests of the model residuals found no remaining sign bias, indicating
the model adequately captures the asymmetric effect.
Groenewold (2003) tests for a structural break in the relationship between the
Australian stock market and real GDP, resulting from financial deregulation in
Australia, using a vector autoregression (VAR) framework and plotting impulse
response functions (IRFs). Returns are based on the All Ordinaries (non-cumulative)
price index. Real GDP, valued at 1999/2000 prices, is used to represent real output.
The control variables used include the term spread on government bonds, which is
calculated as the difference between 10 year yields on Commonwealth Government
bonds and three-month rates on Treasury notes. The default spread is calculated as
the difference between five-year yields on Commonwealth Government bonds and
State Treasury bonds.
10 August 2016 26
Over the full sample, the VAR model and IRFs found lagged output growth, term
spread and default spread had no significant effect on stock returns. In terms of
precedence, the VAR model results showed causality ran from stock market returns
to output growth (that is, stock market returns from two quarters prior caused positive
output growth. This is consistent with the theory that share market returns are a
leading indicator of output (Fama 1981; Campbell & Shiller 1988). In the pre-
deregulation period, the VAR model results still found lagged output growth, term
spread and default spread had no significant effect on stock returns at the conventional
levels of statistical significance. In the post-deregulation period, the VAR results
found lagged default spreads and term spreads had a significant negative effect on
stock market returns. As found in the results for the whole period, stock market returns
from two quarters prior caused positive output growth.
The impulse response functions and R-squared values for the VAR models suggest
any influence that output has on stock returns has weakened post-deregulation. The
implication of these findings is the relationship between the real economy and the
share market had, if anything, weakened after opening the economy to international
capital flows. In isolation, this study creates an a priori expectation that real GDP
announcements affect stock market returns. The findings on both default and term
spreads justify the inclusion of these variables as control variables in a model of
Australian stock returns.
Groenewold (2004) computed fundamental share prices in Australia, based on real
GDP, using a structural vector autoregressive (SVAR) model over the period 1959 to
1999. He found positive real GDP shocks positively affected stock prices, supporting
the theory that the real value of firms is the net present value of expected dividends
(Groenewold 2004, p.660). Over the period of relatively subdued stock market price
10 August 2016 27
growth from 1988 to 1993, his computed fundamental share prices indicated stock
market prices were not too far from fundamental values. However, they departed
substantially from fundamentals in the period prior (from around 1970 to 1987), and
from around 1994 to 1999 when stock market price growth was strong. This suggests
relationships between macroeconomic fundamentals and stock market returns may
differ between strong and subdued periods of stock market price growth.
Kim (2003) investigated the effects of US and Japanese macroeconomic news
announcements on the stock markets of Australia, the US and Japan. The All
Ordinaries index observations for open, high, low and close were used to calculate
Australian market returns. Macroeconomic announcements, and surveyed
expectations of these announcements, were sourced from MMS International so the
unexpected components (or ‘news’) could be estimated by deducting expectations
from the announcements. Exponential GARCH (EGARCH) models were estimated
using Australian stock returns. Using dummy variables controlled for holidays. The
effect of macroeconomic announcements was captured using dummy variables to
indicate only those announcements with news content. These were announcements
where the announced value was not equal to the surveyed expectation. Asymmetric
properties of return and return volatility responses were captured through inclusion of
dummy variables to indicate ‘bad’ news. These were based on the sign of unexpected
components in the announcement.
Most of the US macroeconomic news announcements had a significant effect on
Australian returns. Those with a positive effect on returns included retail sales growth,
unemployment, PPI and CPI-based inflation. With respect to balance of trade, GDP
growth, retail sales growth, unemployment, PPI and CPI-based inflation, bad news
had a significantly negative effect on returns.
10 August 2016 28
All of the US news announcements also had a highly significant effect on Australian
return volatility. News announcements that increased volatility included US balance
of trade, GDP growth, unemployment and CPI based inflation. Retail sales and PPI
news announcements reduced volatility. Bad news, with respect to GDP growth, retail
sales growth, unemployment and the PPI, had a negative effect and reduced volatility.
Bad news, with respect to balance of trade and CPI, increased volatility.
In contrast, few of the Japanese macroeconomic news announcements had a
significant effect on returns; only the Japanese CPI and bad unemployment news had
a positive effect on Australian returns.
For return volatility, however, approximately half of the Japanese announcements had
a significant effect. Australian return volatility was positively affected by the Japanese
wholesale price index, CPI and bad trade balance news. Trade balance news in
general, as well as bad wholesale price index news, had a negative effect on volatility.
The study indicates the effects of US news announcements on Australian stock returns
are more important than the effects caused by Japanese news announcements. A
variable capturing the effect of US announcements (such as US stock returns) will
therefore, likely be useful in a model explaining Australian stock returns.
Chaudhuri and Smiles (2004) tested the long-term relationship between real
Australian stock prices and real macroeconomic variables, including GDP, private
consumption, money-supply and oil prices. Their study is similar to mine in many
respects, but it is based on vector error correction modelling (VECM) that focuses on
relationships over the long run.
The All Ordinaries index was used for Australian stock price data, while seasonally
adjusted M3 money supply, GDP and private personal consumption expenditure was
10 August 2016 29
sourced from the Organisation for Economic Co-operation and Development (OECD)
Main Economic Indicator database. The world oil price index was converted to
Australian dollars using the Australian-US dollar exchange rate. Their model of
Australian returns also included US, Japanese and New Zealand market returns as
explanatory variables. The base error correction model found lagged real GDP
growth, private consumption, M3 money supply and oil prices had a highly significant
role in explaining real stock price variation over an extended period. Concurrent and
lagged US stock price indices are highly significant, and they played a dominant role
in explaining long-term real stock price variation. Similar, but much weaker, effects
were also found for New Zealand stock price indices, while the Japanese index
showed no significant effects.
This study further supports the argument that US stock returns are an important
variable to include in a model explaining Australian returns; whereas Japanese returns
are not.
Akhtar et al (2011) examined the effect of consumer sentiment news on Australian
stock market returns. The Westpac-Melbourne Institute consumer sentiment index
was used to approximate investor sentiment. Returns calculated using the Australian
All Ordinaries index were regressed on dummy variables, representing negative and
positive changes in the consumer sentiment index and the Morgan Stanley Capital
Index (MSCI) (world stock market index), to control for the impact of international
factors. Negative changes in the consumer sentiment index had a significant negative
effect on returns, while positive news was found to have no effect, confirming a
negativity bias in relation to ‘bad’ news. This finding suggests the consumer
sentiment index should be included in my study as a macroeconomic variable of
interest.
10 August 2016 30
Hasan & Ratti (2012) studied the relationship between oil price shocks and volatility
in Australian stock market returns. Their study was based on sector level returns, as
opposed to a broad stock market index or returns.
Stock market indices for ten industry sectors in Australia were used to calculate a
series of ten different returns. The excess return, over Australian 90-day bank
accepted bills, was calculated for each series. Oil price volatility was based on one-
month futures prices of West Texas Intermediate crude oil because they were
considered less noisy than spot prices. A GARCH-in-mean specification was used to
model the relationship between excess returns and volatility in each industry. Excess
returns for each sector were modelled as a function of excess returns on the market
overall, as well as excess oil returns and oil return volatility. They found that oil prices
were negatively related to overall market returns, and oil return volatility was also
negatively related to overall market return volatility.
While most sectors showed that an increase in oil returns also meant a decrease in
excess returns, the energy and materials sectors’ excess returns moved in the same
direction as those of oil. Increased volatility in oil returns reduced volatility of equity
market returns for around half of the sectors (including energy and materials), while
significantly increasing equity market return volatility in the financial sector. Hasan
& Ratti’s (2012) results suggest oil returns are an important variable in explaining
Australian stock market returns.
2.2.2 Australian Stock Market Return Volatility
Kearns & Pagan (1993) examined and attempted to explain volatility in Australian
stock market index data from 1875 to 1987. A variety of models, including EGARCH,
were used to model volatility over the period. The EGARCH model was found to
explain more of the variation in returns, than either GARCH or the rolling 12-month
10 August 2016 31
standard deviation model, while the rolling 12-month standard deviation model was
superior to GARCH in this respect. Tests for sign asymmetry, between positive and
negative shocks on volatility, revealed only weak evidence of negative shocks having
a greater effect on return volatility. The high values of the lagged coefficients in the
volatility models indicated a strong persistence of shocks. This suggests a model of
returns/return volatility should account for autocorrelation in volatility.
Kearney & Daly (1998) examined the relationship between stock market volatility
and a number of macroeconomic variables, including inflation, interest rates,
industrial production, the current account balance and money supply. They employed
a conditional volatility model based on the absolute values of errors between their
returns model and realised returns. Lagged industrial production volatility (over 3
months) reported a negative relationship with stock market return volatility. Volatility
in wholesale price inflation, over one month, increased stock return volatility, as did
interest rate volatility over the same lag horizon. These findings indicate the absolute
values or size of changes in macroeconomic variables, as opposed to the direction of
changes, are important in explaining Australian stock market return volatility.
2.2.3 The Australian Stock Market and Efficient Market Hypothesis
Sadique & Silvapulle (2001) tested stock returns in several countries, including
Australia, for the presence of long memory in returns. Their study was based on the
theory that in efficient markets, arbitrage opportunities are quickly taken advantage
of and decrease the correlation between successive returns in the market. Three
procedures were used to test for long memory, including rescaled range analysis, the
Geweke and Porter-Hudak (GPH) test, and a frequency and time domain score test.
These tests found no significant evidence of long memory in the Australian stock
10 August 2016 32
market, indicating the EMH could not be dismissed. This result suggests the
Australian stock market is, at least, weak form efficient.
An overview of the Australian literature that was reviewed is outlined in Table 1.
Table 1 Australian Literature Review Summary
Study Observation
Period Method Data Frequency Results
Gultekin (1983)
Relationship
between expected
inflation and
stock market returns
January 1947
- December 1979
OLS
Regression
International Monetary Fund Australian Stock
Market Index and CPI
based Inflation
Quarterly
No significant relationship found between expected
inflation and stock market
returns
Jaffe (1984)
Day-of-the-week effect on stock
market returns
March 1973 -
November
1983
OLS
Regression and
F-Tests
Statex Actuaries Stock
Market Index and
Composite S&P 500 US stock market index and
week day dummy
variables
Daily
Days of the week were found to
have unequal effects on returns. Evidence suggested this was
partly due to correlation with
day-of-the-week effects in the US and partly due to factors
unique to the Australian market
Kearns &
Pagan (1993)
Returns asymmetry and
persistence of
shocks in Australian stock
market volatility
1875 -1987
GARCH,
EGARCH, 12 month
rolling
standard deviation
Sydney and Australian
Stock Exchange All Ordinaries Index
Monthly
Weak evidence of sign
asymmetry in the effect of stock market shocks on stock price
volatility was found. Shocks
appeared to be strongly persistent, and return volatility
is greater than that in the US,
particularly in more recent history
Singh (1993)
Relationship
between money supply and stock
returns
January 1976
- June 1987
OLS
Regression
Statex Actuaries Stock
Market Accumulation
Index, All Industrials, All Ordinaries, Banks
and Finance and
Transport indices, MMS M3 money supply
forecasts, RBA press
releases for M1 and M3, ARIMA forecasts based
on RBA press releases
Daily
No significant relationship was
found between stock returns and
money supply. Some evidence was found suggesting surveyed
expectations were more
reflective of market expectations than ARIMA base
forecasts
Singh (1995)
Relationship between current
account deficit
and stock returns
July 1985 -
October 1991
OLS
Regression
Dividend corrected
Statex Actuaries Accumulation index,
week day dummy
variables, MMS current account balance
forecasts, monthly ABS
current account balance
announcements,
ARIMA forecasts based on ABS current account
balance announcements
Daily
Results did not find any
significant relationship between
stock returns and the current account balance. Evidence was
found that suggested surveyed
forecasts contain more
information than ARIMA based
forecasts
10 August 2016 33
Kearney & Daly
(1998)
Relationship between stock
market volatility
and inflation, interest rates,
industrial
production, current account
balance and
money supply
January 1972
- January
1994
Absolute
value conditional
volatility
and GLS estimated
ARCH
ASX All Industrial Index, index of
industrial production,
OECD index of wholesale prices,
current account balance,
Australian-US Dollar exchange rate, RBA 3-
month bank accept bill
interest rate, dummy variables representing
1987 stock market crash
and monthly seasonal dummy variables
Monthly
It was shown that conditional
stock market volatility was
directly related to the conditional volatility of
wholesale price inflation and
interest rates. Industrial production, current account
balance and money supply were
indirectly related
Brooks et al
(1999)
The effect of announcements
and revisions in
GDP and current account balance
on stock returns
January 1989
- December 1993
ARIMA/O
LS
All Ordinaries Index
and announcements of
current account balance and GDP values,
ARIMA forecasts based
on current account balance and GDP
announcements.
Daily
Current account balance and GDP news announcements and
revisions were found to have no
significant effect on returns. The results were unaffected by
separating out negative and non-
negative news.
Sadique &
Silvapulle
(2001)
Tests for long-
term memory in
stock market returns
January 1983 - December
1998
Rescaled
range analysis,
GPH test,
frequency and time
domain
score test
Australian aggregate
stock price index Weekly
No significant long memory of
stock market shocks could be found indicating the efficient
market hypothesis could not be
dismissed
Kim & In
(2002)
Spill over effects
from international
stock market returns,
employment,
CPI and GDP announcement
days on
Australian stock market returns
July 1991 - December
2000
GJR GARCH
with two
step
estimation
procedure
ASX All Ordinaries Index, S&P 500, FTSE
100, Nikkei 225, Australian and US
employment, CPI and
GDP announcement day dummy variables
Daily
A significant positive relationship was found between
Australian and US/UK returns
while a significant negative relationship was found between
Australian and Japanese returns.
US and Australian GDP announcements had a positive
effect on volatility, as did Australian holidays. Shocks in
the UK and US stock market
also had a significant positive impact on Australian stock
market volatility. The model
also found asymmetry terms were significant, indicating
negative and positive shocks
had a different effect on conditional volatility
Groenewold
(2003)
Relationship between the
stock market
returns and real output, term
spreads, and
default spreads
pre- and post-
financial
deregulation
Quarter 1,
1978 - Quarter 2,
2001
VAR and
impulse response
functions
All Ordinaries Index,
Real GDP, term spread between 10 year
Commonwealth
Government Securities and 3-month Treasury
notes, default spread
between 5 year Commonwealth
Government Securities
and 5 year State Government Treasury
Bonds
Quarterly
Pre-deregulation lagged stock
returns were found to have a
significant effect on themselves. Post-deregulation lagged term
spreads and default spreads had
a significant negative effect on stock returns. Impulse response
functions (IRFs) found stock
market return shocks on themselves died out in around
1.5 years. IRFs showed output
growth had a weak negative effect on stock returns pre
deregulation, but a negligible
effect post deregulation
10 August 2016 34
Groenewold
(2004)
Relationship
between the real
stock market prices and real
output
Quarter 4,
1959 - Quarter 1,
1999
SVAR and
impulse response
functions
All Ordinaries Index
divided by the GDP
deflator and Real GDP
Quarterly
The effects of shocks to both output and share prices in the
model die out quickly, but more
slowly so for share price shocks. A positive output shock had a
positive effect on both real
output and real share prices. A positive stock market shock
initially depressed output but
the effect was only temporary. Share prices appeared to be
undervalued for most of the
1970s and overvalued for most of the 1990s
Kim (2003)
Spill over effects
of US and Japanese news
on the Australian
stock market
January 1991 - May 1999
Moving
average
EGARCH
All Ordinaries Index
high, low, open and close prices, MMS
Surveyed forecasts of
US balance of trade, real GDP, retail sales,
unemployment rate, PPI
and CPI, MMS surveyed forecasts of
Japanese trade balance,
current account balance, unemployment, money
supply, wholesale price
index and CPI, dummy variables representing
holidays
Daily
US news announcements that
increased volatility included
balance of trade, GDP growth, unemployment and CPI
inflation. Retail sales and PPI
news announcements reduced volatility. Bad news with
respect to GDP growth, retail
sales growth, unemployment and the PPI were negatively
related to volatility Japanese
CPI and bad unemployment news was positively related to
Australian returns. Australian
return volatility was positively related to the wholesale price
index, CPI and bad trade
balance news. Trade balance news on average and bad
wholesale price index news was
negatively related to volatility
Chaudhuri &
Smiles (2004)
The effect of aggregate
economic
activity on the Australian stock
market
Quarter 1, 1960 -
Quarter 4,
1998
VECM
All Ordinaries Index, OECD seasonally
adjusted Main Economic Indicators;
M3 money supply,
GDP, private personal consumption
expenditure and world
oil price converted using AUD/USD
exchange rate
Quarterly
Lagged Real GDP, real private
consumption, real M3 money supply, and real oil prices were
found as highly significant in
explaining real stock price variation over the long run
Akhtar et al
(2011)
Negatively
biased effect of
consumer sentiment
announcements
on Australian stock returns
June 1992 -
December 2009
OLS
Regression
All Ordinaries Index,
MSCI World Index,
Westpac Melbourne Institute consumer
sentiment index,
Dummy Variables signifying negative
index changes
Daily
Negative changes in the
consumer sentiment index had a
significant negative effect on returns while positive news had
no effect
Hasan & Ratti
(2012)
The effect of oil
price shocks on Australian stock
return volatility
March 2000 - December
2010
GARCH-
in-mean
Australian stock market indices for 10 GICS
sectors, one-month
West Texas Intermediate crude
future prices, and
excess stock market index returns over 90
day bank accepted bills
Daily
Oil prices were negatively
related to overall market returns
and volatility. Most sectors' excess returns were negatively
related to those of oil, however
energy and materials were positively related. Increased oil
return volatility reduced return
volatility for around half of the sectors in the study including
energy and materials, while
financial sector return volatility increased
10 August 2016 35
2.3 Foreign Studies
2.3.1 Foreign Stock Markets and Macroeconomic Surprises
My literature review found four foreign studies that specifically examined the effect
of macroeconomic surprises on stock returns. The foreign literature gives us an
additional insight into which methods and data are likely to produce informative
results. They also assist in the development of hypotheses, which are detailed in
Chapter 3.
Wasserfallen (1989) examined the relationship between stock returns and
macroeconomic surprises in the UK, Switzerland and West Germany separately. His
study used OLS regression with distributed lags and quarterly returns based on a
prominent stock market index, present in each country at the time. He used the
Frankfurter Allgemeine Zeitung index for West Germany, the Swiss National Bank
index for Switzerland and the Financial Times Ordinary index for the UK. Surprises
are calculated as the difference between realised values and ARIMA forecasts
(residuals), for a number of macroeconomic variables, including real GNP, industrial
production, the unemployment rate, consumer prices, money supply, monetary base,
real exports, import prices, nominal and real interest rates, real investment, nominal
and real wages, and foreign exchange rates. His results indicated the explanatory
power of his regressions was very low. For West Germany, unexpected changes in
nominal interest rates, and consumer and import prices, have a negative relationship
with returns. That is, an unexpected increase (decrease) in these factors is associated
with decreased (increased) stock returns. Conversely, unexpected changes in money
supply have a significant positive relationship with stock market returns. In
Switzerland, unexpected real consumption was the only variable to have a relationship
10 August 2016 36
with returns, and that relationship was negative. The UK study found only unexpected
nominal wages had a relationship with real returns that was negative.
Becker, Finnerty and Friedman (1995) examined the relationship between
macroeconomic surprises and stock market returns in the UK. They employed OLS
regression on high frequency (half hourly) returns using the Financial Times Stock
Exchange (FTSE) 100 index. Macroeconomic surprises were calculated using MMS
surveyed data on the current account, industrial production, money supply (M0), PPI,
public sector borrowing requirement, retail price index, retail sales, unemployment,
and visible trade to represent expectations. Their results showed that higher than
expected visible trade and current account surprises were positively related to stock
returns. Heavier than expected government borrowing was negatively related to stock
returns.5 All other variables had no statistically significant effect.
Flannery and Protopapadakis (2002) studied the effects of seventeen macroeconomic
‘surprises’, and announcement days on stock returns, spanning the 16 years from 1980
in the US. A GARCH model was used to estimate returns as a function of surprises,
and the volatility of returns as a function of macroeconomic announcement days.
Volatility was of more interest because they thought it likely the impact of
macroeconomic news on returns was time varying, in terms of strength and sign.
Although an announcement could cause a change in stock returns, it meant the
response of returns to a given type of announcement could, at times, be negative and
at other times be positive. Volatility captured the magnitude or ‘size’ of the effect
rather than the sign.
5 Becker, Finnerty and Friedman (1995) defined surprises as the actual announced value less the
expected value which is opposite to the definition in my paper.
10 August 2016 37
Accordingly, Flannery and Protopapadakis (2002) assessed macroeconomic variables
to determine whether they were ‘risk factors’ in the stock market by also modelling
volatility as a measure of risk. Their study used the weighted index of the New York
Stock Exchange-American Stock Exchange-National Association of Securities
Dealers Automatic Quotation System (NYSE-AMEX-NASDAQ) to calculate returns
and base trading volumes. Seventeen macroeconomic variables of interest were
examined, including the CPI, PPI, money supply (M1 and M2), employment,
unemployment, balance of trade, home sales, housing starts, industrial production,
personal income, personal consumption, retail sales, interest rates, consumer credit,
construction spending and real GNP. Money supply surprises and announcement days
were the only variables to affect both the level and the volatility of returns. Surprises
had a negative effect on the level of returns, while announcement day dummies had a
positive effect on the volatility of returns. The PPI and CPI surprises negatively
affected the level of returns, while the balance of trade, employment, home starts and
real GNP announcements days had a positive impact on return volatility.
Kim (2003) produced regression results for the effects of US and Japanese
macroeconomic news announcements on their own respective stock markets. The
Dow Jones Industrial index and Nikkei 225 index were used to represent the stock
market returns of the US and Japan respectively. Observations for open, high, low
and close index prices were used to calculate market returns. MMS International
surveyed expectations of macroeconomic announcements were deducted from the
announcements themselves to estimate the unexpected components (or ‘news’). The
same EGARCH regression framework for Kim’s (2003) Australian stock return
model (outlined in Section 2.2.1) was used for estimating the stock returns.
10 August 2016 38
The regression results showed that US returns were negatively related to both good
and bad US balance of trade surprises, and positively related to bad retail sales
surprises. US return volatility was decreased by good balance of trade news, real GDP
news, retail sales news and bad unemployment news announced for the US. Bad
balance of trade and PPI news announced for the US increased US return volatility.
For Japan, the regression results showed that good Japanese trade balance news, bad
money supply news and bad CPI news decreased Japanese returns. Good current
account balance news and bad trade balance news increased returns.6 Japanese return
volatility is decreased by good unemployment and CPI news, and bad trade balance
news. Good trade balance news, current account balance news, bad unemployment
and CPI news and any wholesale price index news increases Japanese return
volatility.
An overview of some of the first foreign literature on the effect of macroeconomic
announcement surprises on stock returns is summarised in Table 2.
6 For Japan, bad news announcements for trade balance are when it is lower than expected. Bad
news announcements for money supply are when it is higher than expected.
10 August 2016 39
Table 2 Foreign Literature Review Summary
Study Country Observation Period Method Data Frequency Results
Wasserfallen (1989)
Relationship between nominal
returns and macroeconomic
surprises
West Germany
1977 - 1985
OLS distributed
lag regressions
and ARIMA
forecasts
Frankfurter Allgemeine Zeitung Index, real GNP,
industrial production, unemployment rate, consumer prices, money supply, monetary base, real exports,
import prices, nominal interest rate, real interest rate,
foreign exchange rates
Quarterly
Consumer price, import prices, and nominal
interest rate all had a significant negative
relationship with returns, while M1 money supply had a significant positive relationship
Switzerland
Swiss National Bank Index, real GNP, industrial production, real consumption, real investment,
consumer prices, money supply, monetary base, real
exports, import prices, nominal interest rate, real interest rate, foreign exchange rates
Only real consumption had a significant negative relationship with returns
United Kingdom
Financial Times Ordinary Index, Real GNP, industrial production, consumer prices, nominal wages, real
wages, M1, monetary base, real exports, import prices,
nominal interest rates, real interest rate, foreign exchange rates
Only nominal wages had a significant negative relationship with real returns
Becker, Finnerty and
Friedman (1995)
Relationship between surprises in macroeconomic
announcements and UK equity
market returns
United Kingdom July 1986 -December 1990
OLS Regression
FTSE 100 Index, MMS surveyed expectations and actual announcements from Economic Trends (UK) for
current account, industrial production, M0, PPI, public
sector borrowing requirement, retail price index, retail sales, unemployment and visible trade
Half hourly
Higher than expected visible trade and current account balances were positively related to
returns. Heavier than expected government
was negatively related to returns. All other variables had no statistically significant effect
Flannery and
Protopapadakis (2002)
Relationship between macroeconomic announcement
days and surprises in
announcements and US equity market returns
United States January 1980 -
December 1996 GARCH
NYSE-AMEX-NASDAQ market index from Centre for Research in Security Prices, lagged dividend to price
ratio, lagged three-month Treasury bill yields, lagged 10
year Treasury bond term premium on three-month bills, lagged default premium between Moody's BAA and
AAA seasoned corporate bond indices, surprises based
on MMS surveyed expectations and announcements on balance of trade, consumer credit, construction
spending, CPI, employment, unemployment, new home
sales, housing starts, industrial production, leading indicators, M1, M2, personal consumption, personal
income and PPI
Daily
Higher than expected consumer and producer price indices were negatively related to the
level of returns. The announcement days for
balance of trade, unemployment/employment, home starts, M1, M2 and real GNP were
positively related to the volatility of returns
10 August 2016 40
Kim (2003)
Spill over effects of US and
Japanese news on the
Australian stock market
United States
January 1991 –
May 1999
Moving average
EGARCH
Dow Jones Industrial Index high, low, open and close
prices, MMS Surveyed forecasts of US balance of trade,
real GDP, retail sales, unemployment rate, PPI and CPI
Daily
US returns were negatively related to both good and bad US balance of trade surprises
and positively related to bad retail sales
surprises. US return volatility was decreased by good balance of trade news, real GDP
news, retail sales news and bad unemployment
news. Bad balance of trade and PPI news increased US return volatility
Japan
Nikkei 225 Index high, low, open and close prices,
MMS surveyed forecasts of Japanese trade balance, current account balance, unemployment, money supply,
wholesale price index and CPI, dummy variables
representing holidays
Good trade balance news, bad money supply
news and bad CPI news decreased returns.
Good current account balance news and bad trade balance news increased returns. Return
volatility is decreased by good unemployment
and CPI news, and bad trade balance news. Good trade balance news, current account
balance news, bad unemployment and CPI
news, and any wholesale price index news increases return volatility
10 August 2016 41
2.3.2 Business Cycles and Macroeconomic Factor relationships with Stock Markets
Binswanger (2004) studied the relationship between macroeconomic fundamentals
and stock returns in Canada, Japan and an aggregate economy consisting of four
European G-7 countries. He found that the fundamental relationship between stock
returns and real GDP shown in other research disappeared during the stock market
boom of the 1980s. He concluded there is support for the hypothesis that speculative
bubbles in the stock market were an international phenomenon affecting major
economies during the 1980s and 1990s.
Andersen et al (2007) extend the literature by using high-frequency futures market
data available on a tick-by-tick basis to quantify the effect of US macroeconomic
news announcements on global stock, bond and foreign exchange markets. One of the
key findings from this study was that stock markets react differently to
macroeconomic news depending on the stage of the business cycle. Over the full
sample, including both a period of expansion and contraction, they found almost no
significant equity market responses to economic news. Once the sample was split into
expansion and contraction periods they found that equity markets responded
negatively to positive real economic shocks during expansions while responding
positively during contractions.7
Velinov and Chen (2015) examined whether macroeconomic fundamentals explained
stock prices in France, Germany, Italy, Japan, the UK and US with a special emphasis
on the period following the GFC. In all countries they found that stock prices fell back
7 Many additional papers studying various aspects of macroeconomic announcement surprises and
asset returns can also be found. Some examples include Bomfim (2003), Andersen (2003),
Brenner, Pasquariello, and Subrahmanyam (2009) and Rangel (2011).
10 August 2016 42
toward their fundamental values (approximated by industrial production) after the
GFC.
2.4 Conclusions from the Literature
The literature reviewed for examining the effect of macroeconomic variables on
Australian stock returns covers a period from 1947 to 2010. There appears to be a
dearth of Australian literature dealing with the effect of macroeconomic news or
surprises on Australian stock market returns. Kim and In (2002) made an early attempt
to detect a relationship between stock returns and announcement days for Australian
real GDP, the CPI and unemployment. While they detected a significant relationship
between real GDP announcement days and return volatility, no other relationship
were established. Additionally, their study was not based on surprises, but only
announcement days. More recent studies include Hasan and Ratti (2012), covering
the period 2000 to 2010, and Akhtar et al (2011), covering the period 1992 to 2009.
However, their focus is on the effect of a single macroeconomic variable on
Australian stock returns, rather than the effect of a variety of macroeconomic
surprises. My thesis aims to provide a contemporary analysis of the effect of a variety
of macroeconomic announcements commonly cited in the media, taking advantage of
the long high quality data sets now available, such as MMS surveys and the S&P ASX
200 index (discussed further below). I will employ an extensive set of macroeconomic
variables that are noted in the literature.
While a number of methods have been used to model Australian returns, the
EGARCH model in Kim’s (2003) model was one of the most successful in terms of
explanatory power. This approach has the benefit of modelling both returns and return
volatility simultaneously, which is of interest in return studies, as highlighted in the
theoretical and Australian literature (Fama 1991; Kearns & Pagan 1993; Kearney &
10 August 2016 43
Daly 1998). Foreign studies support the use of ARCH or GARCH type models.
Kearns and Pagan (1993, p.174) found EGARCH had superior explanatory power to
GARCH and other specifications used. Their study also presented evidence that
volatility persists in Australian returns, and so, GARCH type models that control for
these autocorrelation effects should produce more statistically robust results than
basic OLS models. This specification thus far appears to be a good candidate for my
analysis of returns.
Fama (1991, p.1601) noted the event study process, using high frequency data (such
as daily observations), allows for a more precise measurement of the speed at which
stock prices respond to a given event, such as macroeconomic announcements. The
process also assists in overcoming the joint-hypothesis problem. This supports the use
of daily data in an event study process for analysing the effect of macroeconomic
announcements on Australian stock market returns.
The ASX All Ordinaries index is the most utilised Australian stock price index in the
literature reviewed, as evidenced by its use in eight of the fifteen studies reviewed
above. I note, however, this index has been restructured to include a large number of
smaller capitalised firms from 2000 onward. This gives rise to the thin trading issues
raised in Fama (1991, p.1579), so my preference is to use the All Ordinaries index
only for checking the robustness of results. The S&P ASX 200 index gives less weight
to smaller capitalised firms and has maintained a consistent structure since its
inception. My study benefits from having access to this data series, over a time frame
that falls over a period of prolonged stock market expansion, contraction and
subsequent subdued growth following the GFC. This allows me to examine whether
relationships are different during these phases of stock market activity because the
10 August 2016 44
literature suggests this may be the case (Binswanger 2004, Andersen et al (2007),
Velinov & Chen 2015).
Two of the studies examining the Australian stock market found US returns (S&P
500) to have significant effects on Australian returns. Jaffe (1984) found evidence to
suggest negative average Monday returns in the US were correlated with the lowest
mean returns occurring on Tuesday in Australia, due to time zone differences. Kim
and In (2002, p.578) similarly found lagged US stock prices had a positive
relationship with Australian prices, affecting both the mean and variance.
The most commonly studied macroeconomic variables in the Australian literature
include inflation, balance of trade (or more specifically the CAD), interest rates, GDP,
employment and oil prices. Each of these variables was included in at least two of the
studies. Based on their prominence in the literature, I consider these variables good
candidates for inclusion in my model of stock returns, to ensure a comprehensive
study of macroeconomic risk factors whilst maintaining a parsimonious model. The
interest rate used is the overnight cash rate because its values are announced
periodically (rather than being continuously updated), which lends itself well to the
study of surprises. Retail sales and the PPI were included in my study on account of
good quality surveyed forecasts being available from MMS, and because they had
been included in foreign studies using macroeconomic surprises (Becker, Finnerty &
Friedman 1995, Flannery & Protopapadakis 2002). Consumer sentiment was
included, following the success of Akhtar et al (2011), who found it significant in
explaining Australian returns.
Singh (1993 & 1995) found evidence to suggest MMS survey data has information
content superior to naive ARIMA models of expected values for macroeconomic
announcements. In the foreign literature that examined the effect of macroeconomic
10 August 2016 45
surprises, the use of MMS survey forecasts was associated with models more
successful in terms of explanatory power than those employing ARIMA forecasts
(Kim 2003). Accordingly, I opt to use MMS survey data as forecasts in my modelling,
wherever possible, and I use ARIMA forecasts only where MMS data is insufficient.
The exception here is money supply, for which MMS data is no longer available, and
which reported disappointing results when ARIMA forecasts were used (Singh 1993).
For this reason, I exclude money supply as a macroeconomic variable in my study.
The findings of Sadique & Silvapulle (2001) suggest the Australian stock market is,
at least, weak form efficient, which is an encouraging starting point from which to
proceed because my study relies on the Australian stock market being efficient.
10 August 2016 46
3 Hypothesis
As outlined in Section 2.3.2: Conclusions from the Literature, the macroeconomic
variables examined in my study are unemployment, balance of trade, retail sales, the
producer price index, the consumer price index, real GDP, the overnight cash rate and
the consumer sentiment index.
My null hypothesis for each macroeconomic variable is that the surprise component
of related announcements has no effect on aggregate stock returns. As shown in the
literature review (Chapter 2), there are quite a number of conflicting theories and
empirical results that attempt to explain the relationship between each of the
macroeconomic variables in my study and stock market returns. Flannery and
Protopapadakis (2002, p.752) raised the possibility of such conflicts, and highlighted
that the impact of specific macroeconomic variables might vary with economic
conditions. This meant that the effect of macroeconomic risk factors might change
depending on the stage of the business cycle. Despite this, they emphasised that
economically important surprises should be associated with returns that are
abnormally large in absolute value. The models set out in Chapter 4 have been
designed to capture any relationships with abnormally large absolute values in returns.
My null hypothesis is, therefore, that no macroeconomic variables are economically
important.
Alternative theories and evidence propose that macroeconomic variables do affect
stock market returns but, all too often, explain the relationship as a time invariant
effect. Allowing for time varying stock market volatility responses (for each
macroeconomic variable) allows a greater chance of detecting economically
10 August 2016 47
important relationships with returns, in the event that the direction of the effect is time
varying.
The dummy-based model assigns a dummy variable to macroeconomic surprises that
are not zero, and a dummy variable to any ‘bad’ news that depends on the sign of the
observed value for the macroeconomic surprise. The surprise is defined in equation
(1) below:
, 1 , ,( )k t t k t k tSurprise E Announcement Announcement
Where:
1 ,( )t k tE Announcement is the forecast value for period t prior to period t ; and
,k tAnnouncement are the realised values of each of the k announcements at
time t .
In my analysis, I also refer to surprises for each macroeconomic variable as good or
bad for ease of discussion. This is because the sign of surprises (negative or positive)
has a different meaning depending on the variable in question. For example, a
negative sign could indicate good news for some variables and bad news for others. I
must emphasise good and bad news, in this context, does not relate to presupposed
effects on returns, but instead, assumed perceptions of what is good or bad news for
the economy.8 My assumption of what constitutes good and bad surprises follows
Kim (2003, p.619) for all variables except cash rates and consumer sentiment, which
are not included in his study. For the former, I assume the perspective of a leveraged
entity so higher cash rates are bad news (in terms of higher interest payments). For
the latter, I assume the perspective of an entity that relies on sales activity so high
8 The words news and surprises are used interchangeably, although strictly speaking news is only
the value of surprises that are not equal to zero. In practice, it is rare for surprises to equal exactly
zero, as this means expectations forecasted the announcement precisely.
10 August 2016 48
levels of consumer sentiment mean good news (in terms of better buying conditions
and sales).
To summarise, a positive value of surprises resulting from equation (1), for
unemployment, the PPI, CPI and overnight cash rate, is assumed to be good news.
This is based on the assumption that when announced values of these variables are
low (relative to expectations), it is typically seen as good news. Announced values of
those variables that are low (relative to expectations) result in a positive surprise value
(according to equation (1)). All negative observations in the set of t surprises, for
unemployment, the PPI, CPI and overnight cash rate, are coded with the number one
for the bad news dummy and a zero otherwise. I must also emphasise the bad news
dummies are not multiplied by the surprise value.
A positive value of surprises resulting from equation (1), for retail sales, real GDP,
balance of trade and the consumer sentiment index, is interpreted differently. For
these variables, a positive value is assumed to be bad news. This is based on the
assumption that announced values of those variables that are low (relative to
expectations) are typically seen as bad news. Thus, according to equation (1), lower
than expected announced values produce a positive surprise value, which is
interpreted as bad news. For retail sales, real GDP, balance of trade and the consumer
sentiment index, all positive values are coded with the number one for the bad news
dummy and a zero otherwise. Again, the bad news dummies are not multiplied by the
surprise value.
One final complication is that changes in one macroeconomic variable can be a
‘proxy’ for changes in another macroeconomic variable. For example, in Australia,
CPI news can influence expectations of changes in the overnight cash rate due to the
inflation targeting objective of monetary policy. There are many possible
10 August 2016 49
permutations of these interrelationships between macroeconomic variables, which
may also change depending on the phase of the business cycle. In light of this, I only
outline some of the most noted hypotheses on these relationships covered in the
literature.
3.1 Unemployment
My null hypothesis is that unemployment surprises (or news) have no effect on returns
or return volatility. Kim and In (2002, p.578) showed that Australian employment
announcement day dummies (as distinct from values) had no significant explanatory
power in Australian stock market return regressions over the period 1991 to 2000.
This finding is directly relevant to my thesis because the Australian employment
announcements used by Kim and In (2002, p.574) follow the same release schedule
as the unemployment announcements used in my study. In the foreign studies that use
the macroeconomic surprise methodology, Kim (2003) assessed the effect of US and
Japanese unemployment announcement surprises on stock return levels, and found
US unemployment announcements had no effect on US market returns. Such was also
the case in Japan; he found no significant effect between Japanese unemployment
announcement days and Japanese market returns. Flannery and Protopapadakis (2002,
p.766) confirmed Kim’s finding that there were no significant effects between US
unemployment announcements and the level of US returns over a longer, but earlier,
period. Becker, Finnerty and Friedman (1995, p.1206) found UK unemployment
announcements had no effect on UK aggregate stock returns. In West Germany,
Wasserfallen (1989, p.622) observed no relationship between unemployment values
and West German stock market returns. The null hypothesis is set out as follows:
H1: No relationship between unemployment news and stock returns/return
volatility
10 August 2016 50
The finding that there is no relationship between employment/unemployment
announcements and returns could possibly be a result of some studies failing to
capture time varying responses to unemployment news. That is, if stock returns do, in
fact, change in response to unemployment news, but the sign of the effect changes
over time, the effect may be undetectable in return levels but detectable in squared or
absolute values of returns. Time varying responses are more likely to be the case if
significant relationships are found between news and volatility (or absolute returns).
Theories supporting an alternative hypothesis, that unemployment is negatively
related to returns, are as follows. Asprem (1989, p.595) initially presumed
employment (unemployment) would be positively (negatively) related to real activity,
and thus, positively (negatively) correlated with stock returns.
Boyd, Hu & Jagannathan (2005, p.650) found the effect of unanticipated increases in
unemployment on stock returns is dependent on the state of the economy; that is,
whether the economy is expanding or contracting. The reasoning is unemployment
news contains information on corporate earnings/dividend growth expectations,
meaning unemployment news can be a proxy for growth expectations. They found an
unanticipated increase in unemployment often precedes slower growth, particularly
during contractions. The subsequent lower growth in corporate cash flows equates to
lower stock prices and returns. The alternative hypothesis, based on these theories, is
set out below:
H1a: Good unemployment news (unexpected decrease in unemployment)
increases stock returns and vice versa for bad unemployment news
The same authors also recognise another alternative hypothesis that unemployment
and stock returns are positively related. Asprem (1989, p.595) reasoned employment
(unemployment) may increase (decrease) only in the later stages of a boom period,
10 August 2016 51
and by that time, earnings expectations, and thus stock prices, are starting to decline.
That is, stock prices are based on the period ahead, while unemployment relates to the
present. Boyd, Hu & Jagannathan (2005, p.650) formed a view that both future
interest rate information and information on earnings/dividend growth are implicit in
unemployment news. The future interest rate information appears to dominate
corporate earnings/dividend growth information during expansion. They reasoned
this is because an unanticipated rise in unemployment may signal an expectation that
future interest rates will decline in response, and as a result, stock prices (expressed
as the present value of corporate cash flows) are higher. This is on account of a lower
discount rate. These theories give rise to the alternative hypothesis set out below:
H1b: Good unemployment news (unexpected decrease in unemployment)
decreases stock returns and vice versa for bad unemployment news
In turn, these theories suggest that unemployment news, both good and bad, affects
stock return volatility. Kim (2003, p.625) found, in the US, bad unemployment news
reduces return volatility. This gives rise to the following hypothesis:
H1c: Bad unemployment news (unexpected increase in unemployment) decreases
stock return volatility
In Japan, Kim (2003, p.626) found evidence to the contrary. Bad unemployment news
reduced increased Japanese return volatility, while good unemployment news
decreased it. The alternative hypothesis, based on this evidence, is set out below:
H1d: Good unemployment news (unexpected decrease in unemployment)
decreases stock return volatility, and vice versa for bad unemployment news
10 August 2016 52
3.2 Balance of Trade
My null hypothesis is that balance of trade surprises have no effect on returns or return
volatility. Brooks et al (1999) and Singh (1995) both examined whether there is any
relationship between the Australian current account balance and Australian stock
returns, but they found no evidence to suggest one. In the Japanese market, Kim’s
(2003) study did not show any relationship between Japanese balance of trade
surprises and Japanese stock returns. Flannery and Protopapadakis’s (2002) study, did
not find any evidence that the balance of trade surprises and US returns were related.
The null hypothesis is set out as follows:
H2: No relationship between balance of trade surprises and stock
returns/return volatility
Evidence exists to support the alternative hypothesis that balance of trade surprises
have a positive relationship with returns, in the sense that bad news reduces returns.
In the UK, Becker et al (1995, p.1206) found higher than expected current account
balances, which I assume to be good news, in the Australian context, increased
returns. In Japan, Kim (2003, p.626) also found good current account surprises
increase returns. The alternative hypothesis, based on this evidence, is set out below:
H2a: Good balance of trade news (unexpected increase in balance of trade)
increases stock returns, and vice versa for bad balance of trade news
Other evidence shows the sign of the relationship between balance of trade news (or
surprises) and returns can be positive or negative, suggesting an alternative hypothesis
that balance of trade news increases return volatility, as opposed to returns. In Kim’s
(2003, p.624) study US balance of trade surprises, both good and bad, had a negative
effect on US returns, highlighting the lack of consistency around the sign of the
10 August 2016 53
relationship between balance of trade surprises and returns. This indicates it may be
the absolute value of returns or volatility that is affected, as opposed to returns. More
particularly, he also found good balance of trade surprises decreased US return
volatility, while bad news increased it. Flannery and Protopapadakis (2002, p.766)
found that US balance of trade announcement days, in general, were positively related
to US return volatility over an earlier/longer period than examined by Kim (2003).
This adds additional support to the hypothesis that balance of trade news affects return
volatility. The alternative hypothesis based on this evidence is set out below:
H2b: Good balance of trade news (unexpected increase in balance of trade)
decreases stock return volatility and vice versa for bad balance of trade news
In Japan, Kim (2003, p.626) found that good current account balance surprises (larger
than expected) increase return volatility. Assuming this is driven by the balance of
trade, the alternative hypothesis, based on this evidence, is as follows:
H2c: Good balance of trade news (unexpected increase in balance of trade)
increases stock return volatility
Kearney and Daly (1998, p.603) found evidence of another alternative hypothesis.
They reported a negative relationship between the conditional volatility of the current
account deficit (negative balance of trade) and the conditional volatility of stock
returns in Australia. The alternative hypothesis, based on this evidence, is set out
below:
H2d: A negative relationship exists between the size of balance of trade surprises
and stock return volatility.
10 August 2016 54
3.3 Retail Sales
My null hypothesis for retail sales is that they have no effect on Australian stock
market returns. Becker et al’s (1995, p.1206) UK study found no relationship between
returns and retail sales announcements, although it must be kept in mind, they did not
separate the effects of good and bad news. The null hypothesis is set out below:
H3: No relationship between retail sales surprises and stock returns/return
volatility
Economic theory-based alternatives support a relationship between retail sales and
stock market returns via the indirect effect of consumption on stock returns through
real GDP. As highlighted in Chapter 1, one of the most regular empirical results is
that expected and actual output, measured using indicators such as industrial
production, real GNP, or GDP, are positively related to stock returns. Retail sales are
viewed as an economic indicator, on account of being a measure of consumption
(Stock & Watson 1989, p.390). Consumption is a component of the Keynesian model
of aggregate expenditure (Keynes 1936). The most basic representation of the model
predicts consumption is positively related to income and output.
Another possibility is that more emphasis should be placed on future expected real
GDP than current real GDP. The permanent income hypothesis emphasises a positive
relationship between changes in expectations of future income and changes in
consumption spending (Friedman 1957). Changes in consumption, therefore, may be
seen as a forecaster of changes in future income and output, and so one would expect
consumption to be positively related to measures of output, such as real GDP. In turn,
this links consumption to stock returns, through the real GDP/stock market return
relationship. The alternative hypothesis, based on this theory, assumes retail sales are
10 August 2016 55
a measure of consumption, which is a component of real GDP.9 This hypothesis,
therefore, assumes retail sales affect stock returns, through the hypothesised positive
real GDP/stock return relationship discussed in Section 3.6 below. The alterative
hypothesis for the retail sales and stock return relationship based on these theories is
outlined as follows:
H3a: Good retail sales news (unexpected increase in retail sales) increases stock
returns and vice versa for bad retail sales news
An opposing alternative is offered by neoclassical economic theory, which
characterises output (production) as either being allocated to current consumption or
investment, this implies that lower rates of current consumption mean an increased
rate of savings, investment, capital accumulation and higher potential future output
(Solow 1956, Swan 1956). Additionally, if borrowing supports current consumption,
it is future consumption, and thus expenditure and output, that may be adversely
impacted through future interest and principal repayments.
Under these circumstances, rates of consumption that are considered too high could
conceivably be related to lower levels of expected real GDP growth and thus stock
returns over the long run. Kim’s (2003, p.624) results for the US stock market provide
evidence supporting this alternative hypothesis showing that bad (lower than
expected) retail sales news announcements increase US returns. An alternative
hypothesis based on this theory and evidence is set out below:
H3b: Good retail sales news (unexpected increase in retail sales) decreases stock
returns and vice versa for bad retail sales news
9 Multicollinearity is not an issue in this study design because real GDP announcements are made on
different days to retail sales announcements and also because this study focuses on the differences
between expected and actual values of each variable which can move more independently than the
levels of the variables themselves.
10 August 2016 56
With respect to stock market volatility, Kim (2003, p.624) also found evidence to
support an alternative hypothesis of a relationship with retail sales in the US. Good
US retail sales announcements decreased stock return volatility. The alternative
hypothesis based on this evidence is as follows:
H3c: Good retail sales news (unexpected increase in retail sales) decreases stock
return volatility
3.4 Producer Price Index
My null hypothesis for the producer price index is that it has no effect on Australian
stock market returns. In the US, Kim (2003) found the PPI had no effect on the level
of returns in the stock market. Using UK data, Becker et al (1995) could not detect
any relationship between PPI announcement surprises and stock returns. Flannery and
Protopapdakis (2002) found no significant relationship between the US PPI and US
return volatility. The null hypothesis is set out below.
H4: No relationship between retail sales surprises and stock returns/return
volatility
Assuming that the producer and consumer price index are an identical measure of
inflation, the alternative hypotheses on the relationship between the consumer price
index and stock returns (outlined in Section 3.5) should hold for the producer price
index. Tiwari (2012, p.1571) sets out a theory where producer prices are normally set
as a mark-up over wage costs that are driven by consumer prices. In turn, consumer
prices are set by consumer demand. In these circumstances, consumer price changes
should precede producer price changes, and assuming both price indices contain the
same information, the stock market should react to consumer prices but not producer
prices.
10 August 2016 57
In the same study, however, Tawari (2012, p.1571) highlighted that it is equally
plausible for producer price changes to precede consumer price changes in response
to ‘cost push’ shocks such as imported input price shocks.10 The theory that PPI
changes precede CPI changes, combined with the generalised Fisher hypothesis that
postulates a one for one relationship between inflation and stock returns (outlined in
Section 3.5), gives rise to the alternative hypothesis below:
H4a: Good producer price index news (unexpected decrease in the producer price
index) decreases stock returns, and vice versa for bad producer price index news
Flannery and Protopapadakis (2002, p.766) found evidence to support an additional
alternative hypothesis for returns. Using a longer and earlier period than Kim (2003),
they showed the US PPI had a negative relationship with the level of returns on the
US market. Tiwari’s (2012) theory that PPI changes precede CPI changes, combined
with the ‘proxy effect’ hypothesis (outlined in Section 3.5), gives rise to the
alternative hypothesis stated as follows:
H4b: Good producer price index news (unexpected decrease in the producer price
index) increases stock returns, and vice versa for bad producer price index news
For return volatility, Kim (2003, p.625) found evidence of an alternative relationship
in the US, namely that bad (higher than expected) US PPI news announcements are
positively related to US equity market volatility. The alternative hypothesis for
volatility, based on this evidence, is set out below:
H4c: Bad producer price index news (unexpected increase in the producer price
index) increases stock return volatility
10 Tiwari (2012) did, however, find that in Australia consumer price changes precede producer price
changes.
10 August 2016 58
3.5 Consumer Price Index
My null hypothesis for the consumer price index is that it has no effect on Australian
stock market returns. Gultekin (1983) studied the relationship between CPI based
expected inflation and Australian stock market returns over the period January 1947
to December 1979. No significant relationship was found. Kim and In (2002) analysed
the relationship between Australian CPI announcements and returns over a later
period than Gultekin (1983), spanning July 1991 to December 2000. Again, no
significant relationship was found. In Europe, Wasserfallen (1989) found there was
no significant relationship between UK/Swiss consumer price surprises and
UK/Swiss stock market returns. Kim (2003) found no relationship between US CPI
surprises and US stock market returns. Turning to volatility in Australia, Kim and In
(2002) found no relationship between CPI announcements and stock return volatility.
Overseas, Flannery and Protopapadakis (2002) found no relationship between US CPI
announcements and US stock return volatility. Similarly, Kim (2003) found no
relationship between US CPI surprises and stock return volatility. The null hypothesis
is set out below:
H5: No relationship between retail sales surprises and stock returns/return
volatility
One of the most obvious alternative hypotheses is based on the Fisher Hypothesis.
The Fisher Hypothesis characterises interest rates as consisting of a real component,
determined by real factors, such as the time horizons of investors, productivity of
capital, and expected inflation (Fisher 1930). More generally, the hypothesis predicts
nominal expected returns on assets are positively related to expected inflation, as the
nominal component will vary one for one with inflation. An alternative hypothesis,
based on this theory, is set out as follows:
10 August 2016 59
H5a: Good consumer price index news (unexpected decrease in the consumer
price index) decreases stock returns and vice versa for bad consumer price index
news
Alternatively, Fama (1981, p.545) postulates a negative relationship between inflation
and returns may result from a ‘proxy effect’. This assumes expected future real output
is positively related to stock prices and the demand for money. If a decrease in
expected future output is not offset by a decrease in money supply, it results in higher
inflation. Inflation, therefore, becomes a proxy for changes in expected future output
and stock prices. 11 Evidence, in support of a negative relationship, is presented in
Wasserfallen (1989), Flannery and Protopapadakis’s (2002) and Kim (2003).
Wasserfallen (1989, p.622) found consumer price surprises in West Germany had a
negative relationship with West German stock returns over the period 1977 to 1985.
Flannery and Protopapadakis’s (2002, p.766) US study observed a negative
relationship between CPI surprises over January 1980 to December 1996. For the
Japanese stock market, Kim (2003, p.626) found bad (higher than expected) Japanese
CPI announcements were negatively related to Japanese returns. The alternative
hypothesis, based on this theory and evidence, is stated below:
H5b: Good consumer price index news (unexpected decrease in the consumer
price index) increases stock returns, and vice versa for bad consumer price index
news
In Japan, Kim’s (2003, p.627) evidence supported an alternative hypothesis for
volatility. Good (lower than expected) CPI surprises were observed to have a negative
11 For the case of Australia, it is worth noting since mid-1993, the Reserve Bank of Australia has
conducted monetary policy with a particular focus on maintaining inflation within a band of 2 to 3
per cent over the medium term (Reserve Bank of Australia 1999). The implication here is that
unexpectedly high levels of inflation may be associated with a tightening of monetary policy or an
increase in the overnight cash rate. The hypothesised effects of the overnight cash rate on stock
returns are outlined in Section 3.7 below.
10 August 2016 60
effect on Japanese stock return volatility, while bad (higher than expected) CPI
announcements had a positive effect. The alternative hypothesis based on this
evidence is set out below:
H5c: Good consumer price index news (unexpected decrease in the consumer
price index) decreases stock return volatility, and vice versa for bad consumer
price index news
3.6 Real Gross Domestic Product
My null hypothesis for real GDP is that there is no relationship with Australian stock
market returns. Over the period January 1989 to December 1993, Brooks et al (1999)
found no relationship between GDP news announcements (using ARIMA model
based forecasts) and Australian stock returns. Separating negative and positive
surprises had no effect on the results. Kim and In’s (2002) Australian study confirmed
this result using GDP announcements (but without adjustment for the expected
component) over a later period January 1991 to December 2000. Flannery and
Protopapakis (2002) found no relationship between US real GNP announcements and
the level of returns. Kim (2003) also investigated the US market, testing the effect of
real GDP announcements on the US stock market. Again no relationship was found.
The null hypothesis is set out below:
H6: No relationship between real GDP surprises and stock returns/return
volatility
Despite this, there is strong theoretical and empirical support for alternative
hypotheses, although this is mainly based on foreign studies.
A number of theories are consistent with the view that growth in real GDP (output) is
positively related to returns. Increases in output lead to increases in real rates of return
10 August 2016 61
on capital, hence attracting capital investment (Jorgenson 1971). A ‘rational
expectations’ view of the real GDP/stock market price relationship is that
expectations of future real output should set current security prices (Fama 1981).
Campbell and Shiller (1988) outlined the mechanism through which corporate
earnings (which are inextricably linked to output), forecast future dividends, thereby,
creating a link between expected output and future dividends. In turn, their expected
future dividends are discounted to set current security prices through Gordon’s (1962)
dividend growth model. These theories establish a positive relationship between
expected/actual output (measured using indicators such as industrial production, real
GNP, or GDP) and stock returns. This positive relationship is one of the most
regularly found empirical results among foreign studies (Asprem 1989, Fama 1981,
Schwert 1990, Mukherjee & Naka 1995, Cheung and Ng 1998, Ratanapakorn &
Sharma 2007, Humpe & Macmillan 2009). In Australia, Groenewold (2004, p.660)
detected a positive relationship between real GDP shocks and Australian stock
returns.12 The alternative hypothesis, based on these theories and evidence, is outlined
below:
H6a: Good real GDP news (unexpected increase in real GDP growth) increases
stock returns, and vice versa for bad real GDP news
With respect to stock market return volatility, considerable evidence is found in
Australia and overseas to show real GDP announcements affect stock return volatility.
Kim and In (2002, p.578) found that Australian return volatility was positively
influenced by Australian real GDP announcement days (that is, the announcement
itself and not the specifics of the news content). Flannery and Protopapakis’s (2002,
p.766) study on the US also found real GNP announcement days were positively
12 Groenewold (2004) differs from my study in that he had a focus on long run relationships between
output and stock prices as opposed to macroeconomic surprises and daily returns.
10 August 2016 62
related to stock return volatility. This gives rise to an alternative hypothesis that real
GDP surprises, of any sign, are positively related to stock return volatility. This
hypothesis is as follows:
H6b: Real GDP news (unexpected increase or decrease in real GDP growth)
increases stock return volatility
A second alternative, with respect to volatility, is that good (higher than expected)
real GDP surprises reduce stock return volatility. Kim (2003, p.624) observed good
real GDP surprises in the US reduce US return volatility, which supports the
hypothesis set out below:
H6c: Good real GDP news (unexpected increase in real GDP growth) decreases
stock return volatility
3.7 Overnight Cash Rate
My null hypothesis is that the overnight cash rate has no effect on Australian stock
market returns. Wasserfallen (1989) observed Swiss and UK stock market returns
showed no significant relationship with nominal or real interest rates. The null
hypothesis is outlined as follows:
H7: No relationship between overnight cash rate surprises and stock
returns/return volatility
An alternative hypothesis is that interest rates have a positive relationship with stock
returns. Expectations theory, when applied to stock returns, predicts short-term
interest rates should have a one for one relationship with stock returns. This is because
riskier classes of assets, such as stocks, have a constant premium over ‘risk free’
assets, such as sovereign bills (Campbell 1987, Fama & Schwert 1977). Constant
10 August 2016 63
returns imply stock prices will adjust to offset changes in the discount rate, stemming
from changes in risk-free rate to ensure the premium remains constant. The alternative
hypothesis, based on this theory, is set out below:
H7a: Good overnight cash rate news (unexpected decrease in overnight cash rate)
decreases stock returns and vice versa for bad overnight cash rate news
Conversely, Shiller and Beltratti (1992) outlined, in the context of a rational
expectations present value model, a rise in the expected discount rate would cause
bond prices to fall and bond yields to rise, as their traditionally fixed coupons yield
higher returns as a proportion of their price.13 This makes bonds a more attractive
investment vis-à-vis stocks, and so, stock prices need to fall to induce investors to
buy stocks. This theory was outlined in the context of long-term bonds, but would
apply equally for overnight cash rates if cash rate changes were reflected in longer
term bond yields. Empirical support for this alternative is found in Wasserfallen’s
(1989, p.622) study on the West German market. West German stock market returns
showed a significant negative relationship with nominal interest rate surprises.
Flannery and Protopadakis’s (2002, p.766) results also exhibited a negative
relationship between lagged three-month Treasury bill yields and US stock returns.
This relationship, however, was based on continuously reported market yields, as
opposed to an announcement on interest rate policy. Assuming the relationship
between continuously reported interest rates and stock returns holds for interest rate
surprises and stock returns, the alternative hypothesis, based on these theories and
empirical evidence, is set out below:
13 Discount rates are typically driven by interest rates such as the overnight cash rate which reflect
the cost of alternative investment opportunities.
10 August 2016 64
H7b: Good overnight cash rate news (unexpected decrease in overnight cash rate)
increases stock returns and vice versa for bad overnight cash rate news
Turning to volatility, the results of Kearney and Daly’s (1998, p.603) Australian study
provided support for an alternative hypothesis, showing Australian stock market
volatility is positively related to the conditional volatility of the three-month bank
accepted bill rate. I assume volatility in cash rate surprises directly translates into
three-month bank accepted bill volatility. The alternative hypothesis, based on this
evidence and assumption, is set out below:
H7c: A positive relationship exists between the absolute size of overnight cash
rate surprises and stock return volatility
3.8 Consumer Sentiment
While I refer to consumer sentiment as a macroeconomic variable, it differs from the
other variables in that it is a ‘behavioural’ factor, as opposed to a ‘macro’ factor
(Harvey, Liu & Zhu 2014, p.4). My null hypothesis is that consumer sentiment has
no relationship with Australian stock market returns. Although evidence to the
contrary exists in Australia (Akhtar et al 2011), I pose this as the null hypothesis for
the sake of consistency with the null hypotheses posited for the other macroeconomic
variables above. This is also consistent with the formulation of my statistical testing
methods, which are designed to detect evidence of a relationship through rejection of
the null hypothesis. The null hypothesis, based on this rationale, is set out below:
H8: No relationship between consumer sentiment surprises and stock
returns/return volatility
De Long et al (1990) outlined behavioural theories that support an alternative
hypothesis of a positive relationship between consumer sentiment and stock returns.
10 August 2016 65
They argued irrational investors, who trade based on sentiment, induce changes in
returns that are both costly and risky for arbitrageurs to force back to fundamental
levels. This is because risk stems from the unpredictability of investor sentiment, and
arbitrageurs typically have constraints on their investment horizons. For example, an
arbitrageur may go out of business waiting for prices to return to fundamentals.
Baker and Wurgler (2007) found when investor sentiment is low, the subsequent
returns on the stocks of firms that are difficult to value tend to become high (relative
to their long-run average). This suggests that low sentiment leads to the stocks of such
firms being initially undervalued. In the US, Qiu and Welch (2006) found the
consumer confidence index is a proxy for investor sentiment, and that it correlates
with the excess rate of return on small firms, thus linking investor sentiment to
consumer sentiment. Taken together, these studies suggest consumer sentiment is
positively related to returns; however, the studies emphasise this sentiment is linked
to irrational beliefs about future corporate cash flows. Another perspective is that
consumer sentiment is a forecaster of, and may even cause, changes in consumption
expenditure (Carroll, Fuhrer & Wilcox 1994). This could indirectly affect stock
returns through changes in output (or real GDP).
Other research shows changes in consumer sentiment precede changes in output.
Matsusaka and Sbordone (1995) hypothesised that expectations of lower income (low
consumer sentiment) lead to lower orders of goods produced to buyer specifications,
and which cannot easily be resold without significant loss. As a result, an economy’s
build-to-order firms experience lower employment, resulting in reduced
consumption, incomes and output. As per the discussion in Section 3.3, any effects
that consumer sentiment have on output, or real GDP, can result in an indirect effect
on stock returns. Given that real GDP is typically observed to be positively related to
10 August 2016 66
returns (see Section 3.6), this theory would suggest that consumer sentiment is
positively related to stock returns.
Akhtar et al (2011, p.1248) found the Westpac-Melbourne Institute consumer
sentiment index announcements were positively related to returns, although it was
only the decreases in consumer sentiment that were associated with negative
Australian stock market returns; positive announcements had no effect.14 The
alternative hypothesis, based on these theories and evidence, is set out below:
H8a: Bad consumer sentiment news (unexpected decrease in consumer
sentiment) decreases stock returns
With respect to volatility, De Long et al (1990) again provided theoretical support for
an alternative hypothesis; that is, investor sentiment may be positively related to stock
return volatility. They reasoned irrational investors, trading based on sentiment,
induced sustained price movements (in both directions) that are both costly and risky
for arbitrageurs to force back to fundamentals. The alternative hypothesis, based on
this theory, is outlined below:
H8b: A positive relationship exists between consumer sentiment surprises and
stock return volatility
14 Note that this is still a positive relationship, albeit asymmetric, because decreases in consumer
sentiment are related to decreases in returns meaning the variables move in the same direction
hence the correlation is positive.
10 August 2016 67
4 Methodology
4.1 Returns
Stock returns based on market indices are calculated using equation (2).
1
ln 100tt
t
pR x
p
Where:
tp is the closing share market index price on the trading day in question; and
1tp is the closing share market index price on the previous trading day.
4.2 Surprises (Unexpected Components of Announcements)
The surprise or ‘news’ series ,k tSurprise for each macroeconomic variable is
constructed using equation (3).
, 1 , ,( )k t t k t k tSurprise E Announcement Announcement
Where:
1 ,( )t k tE Announcement is the forecast value for period t prior to period t ; and
,k tAnnouncement are the realised values of each announcement at time t .
A positive value of the ,k tSurprise series for each of the k macroeconomic variables
indicates the expected value is high relative to the outcome. The interpretation of
whether this is considered good or bad news is not straightforward and is discussed
with direct reference to the results (Chapter 6).
10 August 2016 68
The surprise series reflects the unexpected components of the forecasts or news. News
by definition is new and unexpected information. Conversely, the expected
component of an announcement is assumed not to be news to the market.
4.3 Control Variables
US Returns
US stock market returns are calculated using equation (4).
,
, 1
ln 100US t
t
US t
pUS x
p
Where ,US tp is the US stock market index level on day t .
Oil Returns
Oil returns are calculated using equation (5).
1
ln 100tt
t
fOil x
f
Where tf is the oil future price index level on day t .
Term Spread
The term spread on government bonds is calculated using equation (6).
, ,t long t short t
TS y y
Where:
,long ty is the yield reported by Bloomberg, based on their 10-year government
bond index, and expressed as a whole number percentage; and
10 August 2016 69
,short ty is the yield reported by Bloomberg, based on their 5-year government
bond index, and expressed as a whole number percentage (details in Section 5.3).
Default Spread
The default spread on Australian corporate bonds is calculated using equation (7).
, ,t corporate t government tDS y y
Where:
,corporate ty is the yield on one of Bloomberg’s Australian corporate bond indices,
expressed as a whole number percentage (details in Section 5.3); and
,government ty is the yield on a Bloomberg government bond index that is of the
same tenor as ,corporate ty , expressed as a whole number percentage.
10 August 2016 70
4.4 Returns Estimation
The form shown in (8) is used to model returns. It is an autoregressive moving average
(ARMA) specification that is fitted to observed returns.
, ,
, , , ,
1 1
Fri
Hol t i Day i t
i Mon
TS CSI
j Control i t k Surprise k t
j US k Unem
p q
i t i i t i t
i i
t c Hol Day
Control Surprise
r
R a a a
a a
a b
Where:
tR are the daily log percentage returns on the stock market indices;
ca is a constant;
Hola is the coefficient on tHol dummy variables assigned to days after holidays;
,
Fri
i Day
i Mon
a
are the coefficients on dummy variables for Monday through to Friday,
but excluding Wednesday;
,
TS
j Control
j US
a
are the coefficients on each of the control variables: tUS , t
Oil , tDS
and tTS ;
,
CSI
k Surprise
k Unem
a
are the coefficients on the macroeconomic surprise variables;
1
p
i t i
i
ra
are the coefficients on the autoregressive lags up to order p;
1
q
i t i
i
b
are the coefficients on the moving average terms up to order q; and
t are the regression residuals.
For the sake of parsimonious presentation 1 1
p q
i t i i t i t
i i
ra b
is abbreviated to ( )M
.
10 August 2016 71
Day-of-the-week and holiday variables are used to capture any return effects that may be
attributed to different days of the week (Gultekin 1983, Fama 1991).
The holiday variable accounts for return effects resulting from information accumulated
when the Australian Stock Exchange is closed. Upon opening after a holiday, it is thought
this information is factored in.
The surprise series coefficient ,k Surprisea
quantifies the sensitivity of daily returns to each
of the k macroeconomic surprises. An additional specification is tested, replacing:
, ,
CSI
k Surprise k t
k Unem
Surprisea
with
, ,
, ,
CSI CSI
k Surprise k Bad News
k Unem k Unem
Surprise Bad News
k t k tD Da a
.
Where:
,
Surprise
k tD is a dummy variable that takes the value of one if ,k tSurprise does not
equal zero or zero otherwise; and
,
Bad News
k tD is a bad news dummy variable that takes the value of one if the
announcement contains bad news, or is otherwise zero.
This additional specification is designed to capture the average effect of announcement
days containing good news surprises ,k Surprisea and the average effect of announcement
days containing bad news surprises. The latter is found by adding the marginal effect of
bad news , k Bad Newsa to good news , , k Surprise k Bad Newsaa . This specification helps to
determine if good news has a different effect on returns than bad news.
This returns equation is estimated using Eviews 7 econometric software, which fits the
specification using the least squares (NLS and ARMA) method. It is estimated alone and
then simultaneously (using the autoregressive conditional heteroscedasticity method in
Eviews) with the volatility specification outlined below.
10 August 2016 72
4.5 Volatility Estimation
This study uses an EGARCH specification to model volatility, and was chosen after
carrying out the analysis conducted in Appendix B. As noted in Section 2.3.2:
Conclusions from the Literature, Kim’s (2003, p.618) EGARCH model was one of the
most successful for finding significant relationships between foreign macroeconomic
surprises and the Australian stock market.
The EGARCH specification is set out in (9).
, ,
, , , ,
1 1 1
2
2
ln( )
ln( )
Fri
Hol t i Day i t
i Mon
TS CSI
j Control i t k surprise k tj US k Unem
p qrt j t j
j j j
j j jt j t j
t
t j
Hol b Day
b Control Surprise
b
b
Where:
2ln( )t is the log of the estimated conditional variance or volatility;
is a constant;
Holb is the coefficient on tHol dummy variables assigned to days after holidays;
,
Fri
i Day
i Tue
b
are the coefficients on dummy variables for Monday through to Friday,
but excluding Wednesday;
,
TS
j Control
j US
b
are the coefficients on each of the control variables: tUS , tOil , tDS
and tTS ;
,
CSI
k
k Unem
surpriseb
are the coefficients on the absolute value of surprise variables;
1
p
j
i
are the coefficients on the lagged GARCH effects up to order p;
10 August 2016 73
1
r
j
j
are the coefficients capturing the sign or leverage effects of the
standardised residuals t j
t j
up to order r = q;
1
q
j
j
are the coefficients on absolute value of the standardised residuals t j
t j
capturing ARCH effects up to order q; and
t j is the estimated conditional standard deviation at time ( t j ) used to
standardise the regression residuals t .15
Again, for the sake of parsimonious presentation,
1 1 1
2ln( )p qr
t j t j
j j j
j j jt j t j
t j
is abbreviated to ( )V .
The estimated conditional variance, and hence standard deviation, is based on an
assumption made regarding the distribution of standardised residuals. This assumption is
investigated in Appendix B.
Again, holiday, day-of-the-week and control variables are included (see Section 4.4).
The coefficient on the absolute value of surprises ,
CSI
k surprisek Unem
b
captures the sensitivity of
return volatility to the absolute size of macroeconomic surprises. As with returns, an
additional specification is tested, replacing:
, ,
CSI
k surprise k tk Unem
Surpriseb
with
, , , ,
CSI CSISurprise Bad News
k Surprise k t k Bad News k t
k Unem k Unem
D Db b
15 The volatility equation used here is limited to controlling for the potentially differing effects of
negative and positive values of the control variables on volatility. Using the absolute magnitude of
movements in control variable returns would be a fruitful addition to the research allowing the
absolute size effects of control variables on volatility to be captured.
10 August 2016 74
Where:
,
Surprise
k tD is a dummy variable that takes the value of one if ,k tSurprise does not
equal zero or zero otherwise; and
,
Bad News
k tD is a bad news dummy variable that takes the value of one if the
announcement contains bad news and zero otherwise.
This additional specification is designed to capture the average effect of announcement
days that contain good news surprises ,k Surpriseb and the average effect of announcement
days containing bad news surprises. The latter is found by adding the marginal effect of
bad news , k Bad Newsb to good news: , , k Surprise k Bad Newsb b . This specification allows us
to determine whether good news has a different effect on return volatility to bad news.
10 August 2016 75
5 Data
This chapter sets out the variables of interest for my study and the data used to
represent those variables. Stock returns are the independent variable explained by
macroeconomic surprises, so here, I detail the data I intend to use, as well as their
statistical characteristics. The macroeconomic surprises (as the explanatory variables)
are then explained outlining the forecasts and the announcement series used to
calculate surprises and statistical characteristics. The chapter finishes with an outline
of the control variables, which are used to remove the effects of other factors known
to affect Australian stock returns.
All variables are observed on a daily basis over the period January 2000 to December
2013.
5.1 Stock Market Indices
The ASX All Ordinaries index is the most commonly used stock market index in
Australian literature. This index was used in the work of Kearns & Pagan (1993),
Brooks et al (1999), Kim & In (2002), Groenewold (2003), Kim (2003), Chaudhuri
& Smiles (2004) and Akhtar et al (2011). However, as of 3 April 2000, the All
Ordinaries index was restructured by the ASX to reflect a greater proportion of the
market and include the 500 largest companies. Prior to this, it reflected only 229–330
stocks (Worthington 2009, p.46).
ASX National Manager of Market Data, John Ying, noted in September 1999:
‘The existing liquidity requirements [on the All Ordinaries index] will be removed
as these are far more appropriate to benchmark indices.’ (Australian Stock
Exchange 1999)
10 August 2016 76
The announcement was made in relation to the re-establishment of the All Ordinaries
as an index to reflect overall market movements, and also the establishment of new
indices, including the ASX 200 as a ‘benchmark’ index for portfolio performance
benchmarking. The All Ordinaries index prior to 2000 is, in fact, more comparable to
the ASX 200 index, which was developed post 2000.
It is important to note the relaxation of liquidity requirements in the All Ordinaries
index is likely to lead to thin trading issues in the index, such as delayed price
reactions. This could possibly result in the All Ordinaries index being relatively slow
to reflect new information because it now includes a large number of small market
capitalisation stocks, which can trade infrequently. I have, therefore, opted to use the
ASX 200 index in this study. For the sake of robustness, however, I will use both the
ASX 200 and All Ordinaries indices to determine if the results are sensitive to the
choice of index.
Also in this study, I use the total returns (also known as cumulative) indices because
this version of the index is conventionally used in stock return studies. However, it is
worth noting Groenewold (2003, p.460) found little difference in his results, between
those estimated on the cumulative index and those estimate on the non-cumulative
index.
Daily observations of the S&P ASX 200 and the All Ordinaries total returns index
were acquired from Datastream and used as the measure of market returns.16 Daily
percentage returns for both indices were calculated as set out in 4.1.
16 The indices were not adjusted for dividend effects. The Datastream code for the S&P ASX 200
total returns index is ‘ASX200I(RI)’. The code for the All Ordinaries total returns index is
‘ASXAORD(RI)’.
10 August 2016 77
The ASX 200 Indices give 3652 daily returns observations to work with, spanning the
period 4 January 2000 to 31 December 2013. The series plotted in Figure 1 appears
to exhibit volatility clustering, particularly around 2008 and 2011, which is suggestive
of time varying volatility.
Figure 1 ASX 200 Index Total Daily Returns
The summary statistics for the series in Table 3 indicate daily returns have a
significant positive bias, as indicated by the mean of 0.0318 per cent. The minimum
daily return of -8.7 per cent occurred on 10 October 2008 with the onset of the GFC.
The maximum of 5.6 per cent is smaller by comparison and occurred just a few days
after the minimum return on 13 October 2008.
-10
-8
-6
-4
-2
0
2
4
6
8
03/0
1/2
00
0
31/0
7/2
00
0
26/0
2/2
00
1
24/0
9/2
00
1
22/0
4/2
00
2
18/1
1/2
00
2
16/0
6/2
00
3
12/0
1/2
00
4
09/0
8/2
00
4
07/0
3/2
00
5
03/1
0/2
00
5
01/0
5/2
00
6
27/1
1/2
00
6
25/0
6/2
00
7
21/0
1/2
00
8
18/0
8/2
00
8
16/0
3/2
00
9
12/1
0/2
00
9
10/0
5/2
01
0
06/1
2/2
01
0
04/0
7/2
01
1
30/0
1/2
01
2
27/0
8/2
01
2
25/0
3/2
01
3
21/1
0/2
01
3
per cent
10 August 2016 78
Table 3 ASX 200 Index Total Daily Returns –Summary Statistics
ASX 200 Daily Total Returns (%)
Mean 0.0318
Standard Error 0.0173
Median 0.0567
Mode NA
Standard Deviation 1.0311
Sample Variance 1.0632
Kurtosis 6
Skewness 0
Range 14.33
Minimum -8.71
Maximum 5.63
Count 3542
Like the ASX 200 returns series, 3542 observations were available for the All
Ordinaries index from 4 January 2000 to 31 December 2013. Visually, the All
Ordinaries daily total returns (Figure 2) indicate a very similar pattern to the ASX 200
returns series. Clustering of volatility in the All Ordinaries returns coincides with
same time periods as the ASX 200, notably 2008 and 2011.
Figure 2 Australian All Ordinaries Index Total Daily Returns
10 August 2016 79
The differences between the All Ordinaries index and the ASX 200 returns only really
become apparent in the summary statistics (shown in Table 4). The range and standard
deviation of the All Ordinaries returns over the period are marginally smaller than
those for the ASX 200, indicating the inclusion of small market capitalised stocks
lowers the level and variability of returns over the period. The distribution also has a
slightly different shape to the ASX 200 returns, with the mean of 0.0314 sitting further
below the median of 0.0658 and a negative skew of one. This suggests a marginally
higher probability of lower returns than the ASX 200 index.
Table 4 All Ordinaries Index Total Daily Returns – Summary Statistics
All Ordinaries Daily Returns (%)
Mean 0.0314
Standard Error 0.0168
Median 0.0658
Mode NA
Standard Deviation 0.9990
Sample Variance 0.9979
Kurtosis 6
Skewness -1
Range 13.92
Minimum -8.55
Maximum 5.36
Count 3542
5.1.1 Stationarity of Stock Returns
The ASX 200 and All Ordinaries return series were tested for stationarity to ensure
they were suitable for time series modelling. An examination of Figure 1 and Figure
2 did not indicate any drift or trend in the daily returns. Accordingly, the augmented
Dicky-Fuller test without drift or trend was carried out to test for the null hypothesis
of a unit root or non-stationarity.
10 August 2016 80
Table 5 Augmented Dickey-Fuller Unit Root Tests - No Drift or Trend
Total Returns Series Index test-statistic Critical Value at 5 per cent
Standard and Poor’s ASX 200 -41.0011 -1.95
All Ordinaries -40.3838 -1.95
The results in Table 5 show the absolute value of the test statistics of both series were
far below the critical value, thus strongly rejecting the hypothesis of a unit root. Based
on the strength of these results, I considered it unnecessary to carry out any additional
stationarity tests.
5.2 Macroeconomic Surprises
As outlined in 4.2, macroeconomic surprises are the difference between the expected
component of the announcement (the forecast) and announcement itself. The equation
is reproduced in (10).
, 1 , ,( )k t t k t k tSurprise E Announcement Announcement
Where:
1 ,( )t k tE Announcement is the forecast value for period t immediately prior to
period t ; and
,k tAnnouncement are the realised values of each announcement at time t .
5.2.1 Forecasts
Consensus forecasts were obtained from Money Market Services (MMS)
International, a former subsidiary of S&P. MMS Asia surveys the forecasts of market-
making participants in Australia (Haver 2013). The medians of these surveyed
forecasts (as first reported) and their corresponding dates are accessed through
Haverselect. MMS surveys are prevalent in the literature review (see Singh 1993 &
1995, and Kim 2003, Becker, Finnerty and Friedman 1995 and Flannery and
10 August 2016 81
Protopapadakis 2002) with Singh providing evidence to suggest the survey data has
superior information content to that of basic ARIMA forecasts. Unemployment
information forecasts are available from 2003, while balance of trade, retail sales, the
PPI and CPI are available from 2003. The MMS Asia forecasts are outlined in Table
6.
Table 6 Money Market Services Consensus Macroeconomic Forecasts
Forecast
Original
Source
Format
Frequency Observation
Period Missing Forecasts
Unemployment per cent level Monthly December 2003 -
December 2013 2
Balance of Trade $ Billion Monthly June 2005 –
December 2013 0
Retail Sales
per cent change
from previous
month
Monthly June 2005 -
December 2013 1
Producer Price Index
per cent change
from previous
quarter
Quarterly September 2005 -
December 2013 0
Consumer Price Index
per cent change
from previous
quarter
Quarterly September 2005 -
September 2013 0
The observation period relates to periods (for example quarter or month) when the
macroeconomic variable was under observation.17
Real GDP, the consumer sentiment index and the overnight cash rate forecasts could
not be adequately sourced from MMS. I used other methods to obtain these forecasts,
which are outlined in their respective sections. The structure of these forecasts is
outlined in Table 7.
17 As distinct from the announcement day date which is when the value for the macroeconomic
variable observed during the observation period is released.
10 August 2016 82
Table 7 Other Macroeconomic Forecasts
Forecast Original Source
Format Frequency Observation Period
Missing
Forecasts
Real GDP per cent change from last
quarter Quarterly
March 2000 –
September 2013 0
Overnight Cash Rate per cent level Monthly September 2003 -
December 2013 0
Consumer Sentiment index level Monthly April 2004 – December
2013 0
5.2.2 Announcements
With respect to macroeconomic announcements, I consider data releases from the
Australian Bureau of Statistics (ABS) and Reserve Bank of Australia (RBA) to be the
most relevant sources. I view these authorities as the most unbiased source of
information available to investors, given their non-commercial objectives.
Announcements made by the ABS included balance of goods and services (trade),
CPI, PPI, real GDP, unemployment and retail sales. Overnight cash rate
announcements were those made by the Reserve Bank of Australia (RBA). The
Westpac-Melbourne Institute consumer sentiment index was the only announcement
sourced from a non-federal government organisation. I believe the value of the index
is sufficiently impartial to commercial interests, on account of the accuracy of the
index value itself giving it its commercial value. The announcements are outlined in
Table 8.
10 August 2016 83
Table 8 Macroeconomic Announcement Values
Announcement Format Frequency Date Range Source
Unemployment Per cent level Monthly January 2004 –
December 2013
Australian Bureau of
Statistics
Balance of Trade $ Billion Monthly August 2005–
December 2013
Australian Bureau of
Statistics
Retail Sales
per cent change
from previous
month
Monthly August 2005–
December 2013
Australian Bureau of
Statistics
Producer Price Index
per cent change
from previous
quarter
Quarterly October 2005–
November 2013
Australian Bureau of
Statistics
Consumer Price Index
per cent change
from previous
quarter
Quarterly October 2005–
October 2013
Australian Bureau of
Statistics
Real GDP per cent change
from last quarter Quarterly
January 2000–
December 2013
Australian Bureau of
Statistics
Overnight Cash Rate per cent level Monthly September 2003 –
December 2013
Reserve Bank of
Australia
Consumer Sentiment index level Monthly April 2004 –
December 2013
Westpac Melbourne
Institute
Accurately pairing the timing of macroeconomic surprises with their associated stock
market returns necessitated matching the specific date of the unrevised value to the
announcement itself. It was important to use the unrevised value because revised
values contained information that was not available on the date when the
announcement was first released and, therefore, not reflected in returns. Use of the
revised values could have masked the impact of the original unrevised values on the
stock market, thus obscuring any underlying relationship that may have existed, and
making it undetectable in regression analysis.
Unrevised macroeconomic announcement values for each of the ABS announcements
were sourced from MMS. The availability of unrevised announcements was the main
constraint on the number of observations available for analysis in my study. This is
because not all series were available over the entire period from January 2000 to
December 2013.
10 August 2016 84
5.2.3 Surprises
Before applying equation (1), data that was not expressed in unitless measures (such
as a percentage level or percentage change) was converted to percentage measures.
This was for consistency with stock market returns, which are expressed in
percentages.
The summary statistics for the resulting surprise series are shown in Table 9.
Table 9 Summary Statistics for Macroeconomic Surprises
(%) Unemployment Balance of
Trade
Retail
Sales
Producer Price
Index
Consumer
Price Index Real GDP Cash Rates
Consumer
Sentiment
Index
Mean 0.05 7.41 0.01 0.02 0 0.26 -0.01 -0.22
Standard
Error 0.02 37.01 0.07 0.1 0.05 0.07 0.01 0.51
Median 0.1 2.61 0 0.1 0 0.29 -0.01 -0.53
Mode 0 0 0.4 -0.2 -0.2 NA 0 NA
Standard
Deviation 0.21 371.96 0.66 0.57 0.28 0.53 0.08 5.54
Range 2.1 5048.02 4.4 2.4 1.1 2.88 0.75 28.98
Minimum -1.4 -2203.57 -2.4 -1.4 -0.6 -0.78 -0.25 -13.18
Maximum 0.7 2844.44 2 1 0.5 2.09 0.5 15.80
Count 118 101 100 33 33 55 114 117
Each series is explained in detail below.
Unemployment
The unemployment rate is the percentage of people in the labour force who are
unemployed as measured by the ABS monthly labour force survey (Australian Bureau
of Statistics 2014a). For example, if the results of the monthly survey show 12 million
people are in the labour force, but of these, 708,000 are classified as being
unemployed, the unemployment rate would be 5.9 per cent. The raw unemployment
10 August 2016 85
announcements and forecasts are expressed as monthly seasonally adjusted
percentage levels.18
This percentage format was desirable for the purposes of my regression, and no
further conversion was required. MMS forecasts were available from December 2003
and paired with unrevised unemployment announcements from this point on. One
unemployment announcement was missing (August 2013) and coincided with one of
two missing MMS forecasts. These missing values resulted in the loss of two of the
120 observations spanning December 2003 to November 2013. Announced values for
months after November 2013 were not used because they were announced after
December 2013 – the limit of my study’s observation period. In total, that left me with
118 pairs of observations.
The announced values were subtracted from the forecasts to create the unemployment
surprise series plotted in Figure 3.
18 The seasonally adjusted series removes the effects of estimated month-to-month seasonal variation
in unemployment.
10 August 2016 86
Figure 3 Unemployment Rate Surprises
The mean value of the forecast error was 0.05 per cent, with the standard error of 0.02
per cent, indicating a statistically significant upward bias in the forecasts.19 The
implication of these results is that the announced unemployment rate is often lower
than expected. This is highlighted in Figure 3 by the large number of values above the
zero axis representing overestimates.
Balance of Trade
The balance of trade measures the net dollar value of goods and services exported
against those imported, on a monthly basis. The data from the ABS is expressed in
seasonally adjusted billions of dollars (Australian Bureau of Statistics 2014b).20
19 This assumes that the series is normally distributed. 20 Seasonally adjusted estimates are derived by estimating the systematic calendar related influences
and removing them from the original estimates see
http://www.abs.gov.au/AUSSTATS/[email protected]/90a12181d877a6a6ca2568b5007b861c/5d5081176d
8bd2cdca256f960075c84a!OpenDocument for more details.
-2
-1.5
-1
-0.5
0
0.5
1
15/0
1/2
00
4
13/0
5/2
00
4
09/0
9/2
00
4
13/0
1/2
00
5
12/0
5/2
00
5
08/0
9/2
00
5
12/0
1/2
00
6
11/0
5/2
00
6
07/0
9/2
00
6
11/0
1/2
00
7
10/0
5/2
00
7
06/0
9/2
00
7
17/0
1/2
00
8
08/0
5/2
00
8
11/0
9/2
00
8
15/0
1/2
00
9
07/0
5/2
00
9
10/0
9/2
00
9
14/0
1/2
01
0
13/0
5/2
01
0
09/0
9/2
01
0
13/0
1/2
01
1
12/0
5/2
01
1
08/0
9/2
01
1
19/0
1/2
01
2
10/0
5/2
01
2
06/0
9/2
01
2
17/0
1/2
01
3
09/0
5/2
01
3
12/0
9/2
01
3
per cent
10 August 2016 87
The series are converted to percentages for consistency with stock market returns in
the regression. The forecast data was converted to percentage changes using equation
(11).
1
1
( )(% ) 100
( )
t t tt t
t
E BOT BOTE BOT x
absolute value BOT
Where:
1( )t tE BOT are the balance of trade forecasts in billions of dollars;
BOT is the actual balance of trade in billions of dollars; and
( )tabsolute value BOT is the absolute value of actual BOT in billions of dollars.
The actual balance of trade data was also converted to a percentage change as shown
in (12).
1
1% 100 ( )
t tt
t
BOT BOTBOT x
absolute value BOT
The absolute values in the denominator were required to preserve the correct sign of
the change. It should be noted that values close to zero in period t might result in very
large percentage changes in period 1t . MMS forecasts were only available from
June 2005, giving me 103 observations to work with. Two observations were lost
because the November and December 2013 observation period announcements
occurred in 2014, which is outside the range of my study.
The resulting 101 observations are plotted in Figure 4.
10 August 2016 88
Figure 4 Balance of Trade Surprises
The large ‘spike’ and ‘dip’ shown in September 2012 and July 2013 result from the
trade balance being unusually close to zero in the month prior. That is, the
denominator in equation (11) and (12) was unusually close to zero and as a result
dramatically scaled up the expected and actual percentage change calculated by those
equations. In order to maintain consistency with the other macroeconomic surprises
and avoid manipulation of data which may be interpreted as arbitrary the outliers were
left in the data set.21
The mean forecast error is positive. However, it is very small compared to the mean’s
standard error, suggesting it is not significantly different from zero.22 The median
forecast error, however, is also greater than zero, which confirms overly optimistic
21 I note that Andersen et al (2007, p.258) implement an alternative data preparation technique in
calculating macroeconomic announcement surprises where the surprise is divided by the standard
deviation of the surprise component. This technique may possibly mitigate the effect of balance of
trade outliers. 22 This assumes a normal distribution.
-3000
-2000
-1000
0
1000
2000
3000
4000
02/0
8/2
00
5
06/1
2/2
00
5
03/0
4/2
00
6
11/0
8/2
00
6
29/1
1/2
00
6
03/0
4/2
00
7
01/0
8/2
00
7
03/1
2/2
00
7
07/0
4/2
00
8
31/0
7/2
00
8
04/1
2/2
00
8
02/0
4/2
00
9
05/0
8/2
00
9
09/1
2/2
00
9
01/0
4/2
01
0
04/0
8/2
01
0
02/1
2/2
01
0
05/0
4/2
01
1
03/0
8/2
01
1
12/1
2/2
01
1
04/0
4/2
01
2
02/0
8/2
01
2
07/1
2/2
01
2
03/0
4/2
01
3
06/0
8/2
01
3
05/1
2/2
01
3
per cent
10 August 2016 89
balance of trade forecasts were common over the period. The two extreme values are
of a similar magnitude to each other at 2844 and -2203 per cent.
Retail Sales
Retail sales are the monthly dollar value turnover of retail trade for Australian
business. The data reported by the ABS, represent month-to-month percentage
changes in dollar values that are seasonally adjusted (Australian Bureau of Statistics
2014c).23 No conversion to percentage was therefore required. Forecasts were only
available for June 2005 onward with one value missing in March 2012, reducing the
number of observations available for my study to 102. Additionally, forecasts for
November and December 2013 were not announced until after 2013, and hence, are
outside the observation period for my study. This further reduced the number of
observations to 100. The series are plotted in Figure 5.
23 Estimating the systematic calendar related influences and removing them from the original
estimates derive seasonally adjusted estimates. See
http://www.abs.gov.au/AUSSTATS/[email protected]/90a12181d877a6a6ca2568b5007b861c/5d5081176d
8bd2cdca256f960075c84a!OpenDocument for more details.
10 August 2016 90
Figure 5 Retail Sales Surprises
The series exhibits a higher level of volatility from 2008 to 2010 than for the earlier
period. This could be associated with the onset of the global financial crisis. Also, the
ABS considered data on retail sales from July to November 2008 as:
‘of limited use for measuring month-to-month estimates because of the increased
volatility in these series due to the smaller sample size and the rotation effect of
having a different third of the sample reporting each month’
Consequently, at the time of this study, the ABS did not make seasonally adjusted
monthly change retail sales data available over this brief period. The data as it was
first announced however, was available through MMS and this is used to construct
the forecast errors reported in Figure 5. Statistical precision of the ABS figures is of
limited relevance – it is the stock market’s reaction to these announced figures that is
the central concern of this study.
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
02/0
8/2
00
5
30/1
1/2
00
5
31/0
3/2
00
6
02/0
8/2
00
6
30/1
1/2
00
6
02/0
4/2
00
7
01/0
8/2
00
7
04/1
2/2
00
7
04/0
4/2
00
8
31/0
7/2
00
8
02/1
2/2
00
8
01/0
4/2
00
9
04/0
8/2
00
9
03/1
2/2
00
9
31/0
3/2
01
0
03/0
8/2
01
0
02/1
2/2
01
0
31/0
3/2
01
1
03/0
8/2
01
1
01/1
2/2
01
1
03/0
4/2
01
2
02/0
8/2
01
2
03/1
2/2
01
2
04/0
4/2
01
3
05/0
8/2
01
3
03/1
2/2
01
3
per cent
10 August 2016 91
The mean absolute deviation in percentage changes was 0.01, which was not
significantly different from zero in light of the mean standard error of 0.07.24 This
suggests that forecasts are not biased. Additionally, the median is zero, which also
tends to indicate unbiasedness. However, the mode is 0.4, suggesting the most
common outcome is an overestimate.
Producer Price Index
The final commodities PPI measures the quarterly change in the price index
established by the ABS for products ready to be sold for immediate consumption,
capital formation, or export.
The quarter-on-quarter seasonally adjusted series is published as a per cent change,
and requires no conversion for consistency with the unit of measurement used for
stock returns (Australian Bureau of Statistics 2014d).25 Forecasts were available from
the 2005 September quarter onward, reducing the number of observations available
to 34. The December 2013 quarter announcement was not released until 2014,
reducing the final sample to 33 observations.26 The series are plotted in Figure 6.
24 This assumes that the series is normally distributed. 25 Estimating the systematic calendar related influences and removing them from the original
estimates derive seasonally adjusted estimates. See
http://www.abs.gov.au/AUSSTATS/[email protected]/90a12181d877a6a6ca2568b5007b861c/5d5081176d
8bd2cdca256f960075c84a!OpenDocument for more details. 26 The year 2014 is beyond my study’s observation period.
10 August 2016 92
Figure 6 Producer Price Index Surprises
The mean of 0.02 per cent is not significant. The median and mode confirm the lack
of clear evidence of bias with the median exhibiting a slightly positive bias of 0.1,
while the mode conversely shows a slightly negative bias of -0.2.
Consumer Price Index
The CPI, reported on a quarterly basis, measures the general level of prices for
consumer goods and services consumed by Australian households. The index is
expressed as a quarter-on-quarter per cent change, or quarterly ‘inflation’, and no
transformation of the data is required to make it unitless (Australian Bureau of
Statistics 2014e).
Only 33 CPI forecasts were available from MMS from September 2005 onwards,
paring back the number of observations available for analysis from 56 to 33. The
surprises based on these observations are plotted in Figure 7.
-2
-1.5
-1
-0.5
0
0.5
1
1.5
24/1
0/2
00
5
15/0
6/2
00
6
23/1
0/2
00
6
23/0
4/2
00
7
22/1
0/2
00
7
21/0
4/2
00
8
20/1
0/2
00
8
20/0
4/2
00
9
26/1
0/2
00
9
27/0
4/2
01
0
25/1
0/2
01
0
21/0
4/2
01
1
24/1
0/2
01
1
23/0
4/2
01
2
02/1
1/2
01
2
03/0
5/2
01
3
01/1
1/2
01
3
per cent
10 August 2016 93
Figure 7 Consumer Price Index Surprises
The surprises or forecast errors appear to be fairly symmetrically distributed around
zero. This is confirmed by the mean and median returning a value of zero. The most
common surprise, however, is negative as shown by the mode of -0.2. This indicates,
if anything, the market tends to underestimate inflation.
Real Gross Domestic Product Growth
Real GDP growth measures the change in total value of goods and services produced
in Australia, holding prices constant from a particular base year. 27
The MMS real GDP consensus forecast series contained 18 forecasts spanning the
December quarter 2004 to the September quarter 2009. A more comprehensive series
of forecasts is available from the RBA spanning March 2000 to August 2011 (Reserve
Bank of Australia 2012). However, their more recent forecasts in the Statements on
27 Estimating the systematic calendar related influences and removing them from the original
estimates derive seasonally adjusted estimates. See
http://www.abs.gov.au/AUSSTATS/[email protected]/90a12181d877a6a6ca2568b5007b861c/5d5081176d
8bd2cdca256f960075c84a!OpenDocument for more details.
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.62
6/1
0/2
00
5
26/0
4/2
00
6
25/1
0/2
00
6
24/0
4/2
00
7
24/1
0/2
00
7
23/0
4/2
00
8
22/1
0/2
00
8
22/0
4/2
00
9
28/1
0/2
00
9
28/0
4/2
01
0
27/1
0/2
01
0
27/0
4/2
01
1
26/1
0/2
01
1
24/0
4/2
01
2
24/1
0/2
01
2
24/0
4/2
01
3
23/1
0/2
01
3
per cent
10 August 2016 94
Monetary Policy are available only for June and December, meaning only 26
observations are available from 2000.
Additionally, in their November 2012 discussion paper, the RBA highlighted their
real GDP forecasts have very little explanatory power (Tulip & Wallace 2012, p.30).
As a result, I concluded forecasts might not be reliable or frequent enough to produce
robust estimates of market expectations of real GDP growth. As an alternative, I
sourced the most up-to-date (revised) real GDP data from the ABS, dating back to
December 1959, and modelled expectations on a naïve model based on an expanding
window of the historical data. This is similar to the approach outlined in Singh 1993
and 1995.
Augmented Dickey-Fuller unit root tests were carried out on the 216 observations of
the seasonally adjusted per cent change series spanning December 1959 to September
2013 (see Table 10).
Using the augmented Dickey-Fuller test, the series tested as stationary (when no drift
or trend was included), indicating that an ARMA model could be meaningfully fitted.
The Akaike Information Criterion (AIC) was used to compare competing models. An
AR (1) model resulted in the lowest AIC where only the intercept was statistically
significant. This suggests the historical mean of the full information set produces the
best forecast, and was used accordingly to produce forecasts.
10 August 2016 95
Table 10 Real GDP Growth – ADF Test and Akaike Information Criterion
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
t-statistic Critical Value
10 per cent 5 per cent 1 per cent
Augmented Dickey-Fuller Test (no
drift or trend) -5.8151 -2.58 -1.95 -1.62
ARMA (p,q)
Akaike
Information
Criterion
Observations
216
(0,0) No Solution
(1,0) 650.13
(0,1) 650.18
(1,1) 652.04
(1,2) 650.76
(2,1) 653.9
(2,2) 653.53
(2,0) 652.56
(0,2) 651.94
parameter Value standard
error t statistic p-value
AR(1) -0.0647 0.06777 -0.954 0.34
Intercept 0.9338 0.09461 9.87 <0.0001***
A forecast for each of the 214 quarters from June 1960 to September 2013 was based
on the mean of all of the ABS observations preceding each quarter.28
Real GDP data was expressed as seasonally adjusted quarter-on-quarter per cent
changes, and so, required no conversion to percentages (Australian Bureau of
Statistics 2014f). All real GDP figures, as first announced by the ABS, were available
from MMS for the entire observation period in the study consisting of 56 quarters. I
used the real GDP estimates (explained above) as forecasts. The only constraint paring
back the observations for this series was the release date for the December 2013
28 No forecast was made for December 1959 and March 1960, as at least two observations are
required to produce an average.
10 August 2016 96
quarter, which was outside the observation period (after 2013); this resulted in the
loss of one observation. The 55 observed surprises are plotted in Figure 8.
Figure 8 Real GDP Surprises
Real GDP growth surprises appear to exhibit a significant upward bias, particularly
after 2007, which is confirmed by summary statistics. The mean of 0.26 is upward
biased and has a standard error of 0.07. The median of 0.26 is similarly upward biased.
This is likely a result of a number of quarters of unusually low actual growth deviating
from the naïve model’s predictions, particularly after 2007.29
Policy Cash Rate
The policy cash rate in my study is the overnight cash rate. That is, the interest rate at
which financial institutions borrow and lend in the overnight money market. The RBA
29 This assumes a normal distribution.
-1
-0.5
0
0.5
1
1.5
2
2.5
14/0
6/2
00
0
13/1
2/2
00
0
06/0
6/2
00
1
05/1
2/2
00
1
05/0
6/2
00
2
04/1
2/2
00
2
04/0
6/2
00
3
03/1
2/2
00
3
02/0
6/2
00
4
01/1
2/2
00
4
01/0
6/2
00
5
07/1
2/2
00
5
07/0
6/2
00
6
06/1
2/2
00
6
06/0
6/2
00
7
05/1
2/2
00
7
04/0
6/2
00
8
03/1
2/2
00
8
03/0
6/2
00
9
16/1
2/2
00
9
02/0
6/2
01
0
01/1
2/2
01
0
01/0
6/2
01
1
07/1
2/2
01
1
06/0
6/2
01
2
05/1
2/2
01
2
05/0
6/2
01
3
04/1
2/2
01
3
per cent
10 August 2016 97
sets targets for this rate in its implementation of monetary policy, which flows through
to other interest rates charged on funds in the Australian economy.
I use the market’s expectations as overnight cash rate forecasts. The expected
overnight cash rate target was derived from the price of 30-day interbank cash rate
futures contracts. The data used was a generic, and sourced from Bloomberg using
the ‘IB CMDTY’ ticker over the period August 2003 to December 2013.
The ‘latest’ price for the futures contract was used, relating to settlement in the month
in which the cash rate announcement was being made. The latest price was the price
observed the day immediately prior to the cash rate announcement, which virtually
always took place on the first Tuesday of every month.30
The formula shown in (13) was used to derive the expected value for the cash rate
announced the next day.
1t b
ta
x r nr
n
Where:
1tr is the expected value for the cash rate announced the next day;
x is 100 minus the contract price, for that month, prevailing on the close of the
day prior to the announcement;
tr is the rate prevailing prior to the announcement;
bn is the proportion of days in the month before and including the day of the
announcement; and
an is the proportion of days in the month after the announcement.
30 Except for January, where no announcement was made. For months where Tuesday was the first
day of the month, Bloomberg data had to be manually retrieved to augment the ‘generic’ 30-day
series. This is because the generic series futures prices are always aligned with the month for
which the data is being retrieved.
10 August 2016 98
Only values for February through to December were reported, as announcements were
only scheduled only for these months. This resulted in 112 one-day-ahead expected
values, which were used as forecasts.
Policy cash rate data was expressed in percentage levels, which is consistent with the
ASX 200 returns in the regression equation (in terms of being unitless), and no
transformation was required (Reserve Bank of Australia 2013). The forecasts implied
on the first Tuesday of every month (except for January), using the methodology
outlined above, were paired accordingly with their date and the announced cash rate.
The 114 resulting forecast surprises are shown in Figure 9.
Figure 9 Interest Rate Surprises
The maximum cash rate surprise is 0.5 per cent. This occurred when the cash rate was
dropped unexpectedly by this amount in October 2008 with the onset of the GFC. The
forecasts do not show any significant evidence of bias with a mean and median of -
0.01.
-0.30
-0.20
-0.10
0.00
0.10
0.20
0.30
0.40
0.50
0.60
02/0
9/2
00
3
06/0
1/2
00
4
04/0
5/2
00
4
07/0
9/2
00
4
01/0
2/2
00
5
07/0
6/2
00
5
04/1
0/2
00
5
07/0
3/2
00
6
04/0
7/2
00
6
05/1
2/2
00
6
01/0
5/2
00
7
06/1
1/2
00
7
04/0
3/2
00
8
01/0
7/2
00
8
04/1
1/2
00
8
03/0
3/2
00
9
07/0
7/2
00
9
03/1
1/2
00
9
02/0
3/2
01
0
03/0
8/2
01
0
07/1
2/2
01
0
03/0
5/2
01
1
06/0
9/2
01
1
07/0
2/2
01
2
05/0
6/2
01
2
02/1
0/2
01
2
05/0
2/2
01
3
02/0
7/2
01
3
05/1
1/2
01
3
per cent
10 August 2016 99
Consumer Sentiment
The Westpac-Melbourne Institute consumer sentiment index reflects consumers’
evaluations of their household finances over the past and coming year, expectations
of economic conditions over the coming years, and buying conditions for major
household items.
MMS forecasts of the index contained an insufficient number of observations to create
an adequate number of forecast errors for analysis. Augmented Dickey-Fuller tests on
the series showed the announcement series was non-stationary in levels, but stationary
in first differences (see Table 11).
This indicates that the consumer sentiment index can be modelled as an ARIMA
process to produce forecasts. The Akaike Information Criteria on a number of
specifications, modelled over the entire data set from October 1974 to June 2013,
suggested that an ARIMA (1,1,2) without an intercept was the most parsimonious fit.
An expanding window historical data set was used to estimate ARIMA (1,1,2)
specifications and produce one-step ahead forecasts. The expanding window used all
historical consumer sentiment index values dating back to October 1974, before the
month in which the forecast was made. This data was used to estimate the ARIMA
(2,1,1) specification, which in turn, was used to produce the one period ahead forecast.
For the subsequent month, the process was repeated, expanding the historical data set
by one more observation. This process was repeated for all months from January 2000
to June 2013 producing 168 forecasts.
10 August 2016 100
Table 11 Consumer Sentiment – ADF Tests and Akaike Information Criteria
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
t-statistic Critical Value
10 per cent 5 per cent 1 per cent
Augmented Dickey-Fuller Test
(no drift or trend) -5.8151 -2.58 -1.95 -1.62
ARMA (p,q) Akaike
Information
Criterion Observations
216
(0,0) No Solution
(1,0) 650.13
(0,1) 650.18
(1,1) 652.04
(1,2) 650.76
(2,1) 653.9
(2,2) 653.53
(2,0) 652.56
(0,2) 651.94
parameter Value standard
error t statistic p-value
AR(1) -0.0647 0.06777 -0.954 0.34
Intercept 0.9338 0.09461 9.87 <0.0001
The data was expressed as a seasonally adjusted index and required conversion to
percentages using the equation (14).
1
1
( )(% ) 100t t t
t t
t
E CSI CSIE CSI x
CSI
Where:
1( )t tE CSI are the modelled forecasts explained above; and
tCSI are the seasonally adjusted consumer sentiment index actual figures.
1(% )t tE CSI can be interpreted as the expected percentage change on the
current level of the CSI.
10 August 2016 101
The actuals data was also converted to percentage changes as shown in (15).
1
1% 100t tt
t
CSI CSICSI x
CSI
Equation (14) was then deducted from equation (13) to create the times series of
consumer sentiment index surprises.
On account of release dates only being available from MMS from April 2004, the
number of useful observations was reduced from 168 to 117. The sample was further
reduced to 108 on account of nine missing release dates within the set of available
dates.31 That is, some months in the MMS data set did not report the day on which the
announcement was made, and so, it is assumed no announcement occurred. The
forecast errors/surprises are plotted in Figure 10.
Figure 10 Consumer Sentiment Surprises
31 The missing release dates were for the months July, August, November and December 2004, April,
May and November 2005, July 2009 and February 2012.
-15.00
-10.00
-5.00
0.00
5.00
10.00
15.00
20.00
07/0
4/2
00
4
08/0
9/2
00
4
08/0
2/2
00
5
13/0
7/2
00
5
13/1
2/2
00
5
17/0
5/2
00
6
11/1
0/2
00
6
13/0
3/2
00
7
15/0
8/2
00
7
15/0
1/2
00
8
11/0
6/2
00
8
11/1
1/2
00
8
08/0
4/2
00
9
09/0
9/2
00
9
09/0
2/2
01
0
14/0
7/2
01
0
14/1
2/2
01
0
18/0
5/2
01
1
11/1
0/2
01
1
13/0
3/2
01
2
15/0
8/2
01
2
17/0
1/2
01
3
12/0
6/2
01
3
12/1
1/2
01
3
per cent
10 August 2016 102
All the data observations were plotted, except for the nine missing dates. Casual
observation of the plot does not reveal any bias. Additionally, the forecast over- and
underestimates do not appear to increase in magnitude with the onset of the GFC. The
mean forecast error is -0.22, which is insignificant.
5.3 Control Variables
Crude Oil
A number of oil price benchmarks are available, with Brent and West Texas
Intermediate (WTI) as the two most notable. Brent accounts for around two thirds of
global physical trade in oil, despite only accounting for one per cent of crude oil
production (Dunn & Holloway 2012, p.68). WTI has a strong US focus and recent
developments in the oil and gas market resulted in a preference for Brent as an
indicator of international oil prices. Thus, Brent was selected as the index for oil prices
in the analysis.
I found that some stock returns studies use spot prices for oil as an explanatory
variable, while others use closest to maturity futures prices. Sardosky (2001), Boyer
and Filion (2007), and Hasan and Ratti (2012) used one-month futures prices due to
spot prices being more affected by temporary, random events that introduce noise into
the analysis. I assume market participants place more weight on futures prices, which
is consistent with the recent literature. Accordingly, one-month Brent future contract
prices were sourced from Bloomberg. Missing values were replaced with the last
known price.32
The Australian Dollar contract price per barrel and returns are shown in Figure 11.
32 The Bloomberg ticker for the Brent futures index used is ‘CO1 Cmdty’.
10 August 2016 103
Figure 11 Brent Crude Oil One-Month Futures Prices and Returns
US Stock Market Index
I included the lagged daily US S&P 500 Index returns to control for international
effects on the Australian stock market. The closing index value was acquired from
Datastream and converted to Australian returns using the closing US-Australian dollar
exchange rate on each day.33 Returns were calculated using equation (7) and plotted
in Figure 12.
33 The S&P 500 index code used in data stream was ‘S&PCOMP(RI)’ divided by ‘AUUSDSP’
observations for each day.
0
20
40
60
80
100
120
140
160
-20
-15
-10
-5
0
5
10
15
AUD priceper cent
10 August 2016 104
Figure 12 Lagged US Standard and Poor’s 500 Index Returns
The returns exhibit volatility clustering in similar periods to those evident in the
Australian return series in Figure 1 and Figure 2. This suggests some commonality
may exist and inclusion of this variable is appropriate for this study.
Day of Week and Holidays
Day-of-the-week dummy variables and holiday effect dummy variables are
commonly used as control variables in return studies (see Jaffe 1984, Singh 1993 and
Kim 2003). Australian Stock Exchange holiday dates were sourced from Bloomberg.
Dates that correspond to days with no price changes were deleted from my study, and
the following day, assigned a dummy variable to control for opening price reactions
to information accruing over the holiday period.
All days of the week (except for Wednesday) were assigned their own series and
coded with a dummy variable taking the value of one if it was that particular day-of-
10 August 2016 105
the-week, or zero otherwise. This established Wednesday as the base case, against
which all other days were compared.
Term Spreads
Flannery and Protopapadakis (2002), and Groenewold (2003) included term and
default spreads in their analysis as control variables. The term spread is high on bonds
(upward sloping yield curve) during economic downturns, when future conditions are
expected to improve, also signalling high-expected returns (Fama 1991, p.1585).
Harvey (1989, p.39) explained the role of the term spread in predicting economic
growth. He reasoned that an investor’s marginal value of a dollar is high during
recessions (due to lower consumption) than in affluent times when consumption is
high. Foreseeing this, rational investors sell short-term bonds and buy long-term
bonds as insurance against an expected down turn. Holding all other factors constant,
this raises the yield on short-term bonds (through reduced price) and depresses the
yield on long-term bonds through long-term bonds prices increasing (Harvey 1989).
Stock returns are linked to a firm’s earnings, and thus, real economic growth (Gordon
1962; Fama 1981; Campbell & Shiller 1988; Schwert 1990). The link between term
spreads and economic growth indirectly suggests term spreads are a gauge of expected
returns from equities.
Flannery and Protopapadakis (2002, p.760) used the Treasury term structure
premium, measured as the difference in yield to maturity between ten-year Treasury
bonds and three-month Treasury bills. Groenewold (2003, p.460) used the term spread
between the rates on ten-year Government Bond and three-month Treasury notes.
Three-month Commonwealth Government Treasury Bond data sourced from
Bloomberg contained many missing observations between 2000 and 2013, so I used
10 August 2016 106
one-year bonds to replicate the term spread used by Groenewold for the Australian
market.
The spreads between ten- and one-year Government bonds are shown in Figure 13.34
The term spread becomes negative around the year 2000 ‘Dot-Com’ bubble and in
the years leading up to the 2008 Global Financial Crisis. This indicates the yields on
shorter-term bonds were becoming large in comparison to longer-term bonds at the
onset of economic turmoil, which is consistent with Harvey’s theory outlined above.
Shortly after each of these crises, the spread rapidly becomes positive, which is also
consistent with the view that economic growth and, thus, future returns are expected
to improve.
34 The Bloomberg tickers used for the one and ten year Government bonds are ‘GACGB1 Index’ and
‘GACGB10 Index’ respectively.
10 August 2016 107
Figure 13 Term Spread - Australian Commonwealth Government Bonds
Default Spreads
Fama (1991, p.1585) outlined that during economic downturns default spreads are
high on bonds, while very low stock prices result in relatively high dividend yields.
This implies high-expected returns on bonds and stocks. That is, during persistent
downturns, an investor requires higher compensation if risking depressed levels of
wealth.
Flannery and Protopapadakis (2002, p.760) calculated a default premium, measured
as the difference in yield between Moody’s BAA and AAA seasoned corporate bond
indices.
Groenewold (2003, p.460) measured the default spread between the rates for five-
year Government bonds and five-year New South Wales Treasury Bonds. He noted
that bonds with a greater ‘quality’ difference would have been preferred, but was
restricted to this pair due to data availability.
-1.00
-0.50
0.00
0.50
1.00
1.50
2.00
2.50
3.000
3/0
1/2
00
0
09/1
0/2
00
0
16/0
7/2
00
1
22/0
4/2
00
2
27/0
1/2
00
3
03/1
1/2
00
3
09/0
8/2
00
4
16/0
5/2
00
5
20/0
2/2
00
6
27/1
1/2
00
6
03/0
9/2
00
7
09/0
6/2
00
8
16/0
3/2
00
9
21/1
2/2
00
9
27/0
9/2
01
0
04/0
7/2
01
1
09/0
4/2
01
2
14/0
1/2
01
3
21/1
0/2
01
3
per cent
10 August 2016 108
For my research, the longest continuous series of Australian corporate bond yields
available at the time was the Bloomberg 5-year AA fair value curve index. While
other Australian corporate bond series are available from Datastream and UBS, they
do not hold a credit rating or term to maturity constant. This means the default spread
calculated on these bonds is contaminated with term and credit rating variations that
do not reflect the pricing of a given default category. Bloomberg had a variety of other
Australian corporate bond indices, including a BBB band, which would have been
preferable due to the greater premium on these bonds. However, the AA 5-year index
was the only series available with continuous observations from January 2000. The
Bloomberg 5-year Australian Commonwealth Government bond index series was
deducted from the AA fair value curve series to derive a default spread.35 Anomalous
data points were replaced with the preceding day’s value. The resulting series is
shown in Figure 14.
35 The Bloomberg tickers used for the five year Government bond index and AA 5 year fair value
curve are ‘GACGB5 Index’ and ‘C3585Y Index’ respectively.
10 August 2016 109
Figure 14 5-Year Australian Corporate Bond Default Spread
The default spread is relatively stable leading up to the GFC in 2008, fluctuating
within a band of 0.5 to one per cent. The peaks in the series appear to roughly align
with the clustering of increased volatility displayed in Figure 1 and Figure 2. This is
suggestive of a shared relationship between stock market risk and bond returns.
0.00
0.50
1.00
1.50
2.00
2.50
3.000
3/0
1/2
00
0
12/0
6/2
00
0
20/1
1/2
00
0
30/0
4/2
00
1
08/1
0/2
00
1
18/0
3/2
00
2
26/0
8/2
00
2
03/0
2/2
00
3
14/0
7/2
00
3
22/1
2/2
00
3
31/0
5/2
00
4
08/1
1/2
00
4
18/0
4/2
00
5
26/0
9/2
00
5
06/0
3/2
00
6
14/0
8/2
00
6
22/0
1/2
00
7
02/0
7/2
00
7
10/1
2/2
00
7
19/0
5/2
00
8
27/1
0/2
00
8
06/0
4/2
00
9
14/0
9/2
00
9
22/0
2/2
01
0
02/0
8/2
01
0
10/0
1/2
01
1
20/0
6/2
01
1
28/1
1/2
01
1
07/0
5/2
01
2
15/1
0/2
01
2
25/0
3/2
01
3
02/0
9/2
01
3
per cent
10 August 2016 110
6 Results
The models outlined in Chapter 4 were estimated using ASX 200 daily returns from
26 October 2005 to 31 December 2013. This period was chosen because the data for
all of the macroeconomic surprises was available between those dates.
To reiterate Chapter 4, two variants of the model were estimated: one using
values/absolute values of macroeconomic surprises (continuous model), and the other
using dummy variables for macroeconomic announcement days separated into good
and bad news days (dummy variable based model). The continuous models’
dependent variables were the forecast errors expressed as a percentage. The
continuous model allowed me to capture the percentage change in stock returns and
stock return volatility per one per cent of error in macroeconomic forecasts. This
provided information on the sensitivity of stock returns and return volatility to
macroeconomic surprises. The details of model fitting are outlined in Appendix B.
6.1 Continuous Model Results
In the mean equation in Table 12, surprises can be negative or positive as they are all
based on the equation (16):
, 1 , ,( )k t t k t k tSurprise E Announcement Announcement
In the variance equation, absolute values of surprises are used, so both good and bad
news is positive and, therefore, summarised into surprises more generally. A positive
coefficient for the macroeconomic variables in the variance equation indicates
surprises in general increase volatility, while a negative coefficient on the variables
indicates surprises, in general, decrease volatility.
10 August 2016 111
The coefficients represent the effect on returns, or return volatility expressed as whole
number percentages. For example, a coefficient of 0.5 would represent a 50 basis
point or a one half a per cent increase in return on a given day.
Table 12 Continuous EGARCH model results based on full period sample
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
tests are two sided based on a null hypothesis of zero
ASX 200 Daily Total Returns: 26 October 2005 - 31 December 2013
Mean Equation
, , , , , ,( ) +
Fri TS CSI
t Hol t i Day i t j Control i t k Surprise k t
i Mon j US k Unem
t Hol Day Control SurpriseR M a a a a
Variable Coefficient p-value
Intercept 0.1152 0.0282**
Hola Holiday 0.3067 0.0029***
Daya Monday 0.0246 0.6710
Daya Tuesday -0.0408 0.4414
Daya Thursday 0.0182 0.7499
Daya Friday -0.0198 0.7097
Controla US Returns (Lagged) 0.3872 0.0000***
Controla Oil Returns (Lagged) 0.0251 0.0353**
Controla Term Spread 0.0323 0.1919
Controla Default Spread -0.0674 0.0118**
Surprisea Unemployment 0.4306 0.2373
Surprisea Balance of Trade 0.0000 0.8262
Surprisea Retail Sales 0.1850 0.0628
Surprisea Producer Price Index -0.1470 0.6137
Surprisea Consumer Price Index 0.7673 0.0425**
Surprisea Real Gross Domestic Product -0.1939 0.2954
Surprisea Overnight Cash Rate 2.1336 0.0838
Surprisea Consumer Sentiment Index 0.0103 0.5302
10 August 2016 112
Variance Equation
, , , ,
2
, ,( ) ln( )Fri TS CSI
Hol t i t i t k surprise k t
i Mon j US k Unem
t i Day j ControlHol Day Control SurpriseV b b b b
Variable Coefficient p-value
Intercept -0.0014 0.9879
ARCH (1) term 0.1084 0.0000***
Asymmetry term -0.1206 0.0000***
GARCH (1) term 0.9662 0.0000***
Holb Holiday 0.0279 0.7677
Dayb Monday 0.1216 0.3596
Dayb Tuesday -0.3193 0.0383**
Dayb Thursday -0.1695 0.2588
Dayb Friday -0.2086 0.0844
Controlb US Returns (Lagged) -0.0775 0.0000***
Controlb Oil Returns (Lagged) -0.0007 0.9359
Controlb Term Spread 0.0007 0.8985
Controlb Default Spread 0.0083 0.2192
surpriseb Unemployment -0.2570 0.3307
surpriseb Balance of Trade -0.0002 0.1675
surpriseb Retail Sales 0.0591 0.5428
surpriseb Producer Price Index 0.4262 0.0332**
surpriseb Consumer Price Index -0.3685 0.3772
surpriseb Real Gross Domestic Product -0.1079 0.6353
surpriseb Overnight Cash Rate 0.7953 0.3954
surpriseb Consumer Sentiment Index 0.0356 0.0241**
Included observations 2070
Adjusted R-squared 0.2603
Log likelihood -2627.7910
Akaike Information criterion 2.5766
Diagnostics
Q(20) [p-value] 14.541 [0.8020]
Q2(20) [p-value] 11.452 [0.9340]
10 August 2016 113
ARCH LM Test F-Statistic [p-value] 0.6051 [0.9119]
Engle-Ng Joint Sign Bias Test F-Statistic [p-value] 1.223 [0.2997]
In Table 12 (and the subsequent tables of results for the models that follow), the p-
values are based on Bollerslev-Wooldridge robust standard errors. The p-values given
are based on a two sided test and a null hypothesis of zero consistent with the null
hypotheses outlined in Chapter 3. Two sided tests have been used because they allow
simultaneous testing for negative and positive effects outlined in the alternative
hypotheses in Chapter 3, yet are still more conservative than one sided tests as they
require greater deviation from zero to detect significance. I mainly discuss variables
significant at the five per cent level for the sake of brevity.
To start with, I briefly discuss the effect of control variables. For returns (shown in
the mean equation), the results indicate holidays, US returns and Brent crude oil
futures returns have a positive relationship with Australian stock returns. The result
for US returns is consistent with Kim and In (2002). The relationship between oil
prices and Australian stock market returns are the opposite of that found by Hasan
and Ratti (2012). The default spread shows a negative relationship with returns, which
suggests stock price changes are related to bond price changes. As corporate bond
prices fall, the yields that reflect the coupon payment, as a proportion of the price,
increase which results in an increased default spread. The negative relationship with
stock prices reported in Table 12, therefore, shows that bond and stock prices fall
together. This is consistent with the findings of Fama (1991) who observed that
increased default spreads are related to decreased stock prices during economic
downturns. ASX 200 return volatility shows evidence of a negative relationship with
Tuesdays and US returns. This is consistent with Jaffe (1984) who found that day of
the week effects were unequal. While these results are interesting and reasonable, they
10 August 2016 114
are not the main focus of my research and so I move on to discuss the effect of
macroeconomic surprises.
For returns, only the CPI reports a significant relationship. Good (bad) CPI surprises
report a positive (negative) relationship with returns. That is, stock returns increase
by 76.73 basis points for every one per cent that the CPI is lower than expected.36.
This rejects my null hypothesis of no relationship between CPI surprises and stock
returns. Instead, it appears to support the alternative ‘proxy effect’ hypothesis (H5b)
where inflation acts as a proxy for changes in expected future output (which is
typically positively related to stock market returns) and stock prices.
For volatility, the PPI relates to increased ASX 200 return volatility, reporting a 42.62
basis points increase in returns for every one per cent surprise (or forecast error) of
any sign. This rejects my null hypothesis of no effect and means greater PPI surprises
are associated with greater stock return volatility. This is consistent with alternative
hypothesis H4c and hence the findings of Kim (2003, p.625) in the US market.37 The
CPI, however, was not found to be significant. These results are in contrast to Tiwari
(2012) who found that CPI changes precede PPI changes. If the CPI is a forecaster of
the PPI, one would expect only information in the CPI to be of importance to the stock
market and, therefore, the CPI to be significant instead of the PPI. This result is
examined further for robustness in Section 6.5. Consumer sentiment index surprises
(of any sign) significantly increase volatility, rejecting my null hypothesis of no
relationship. For every one per cent that consumer sentiment differs from that which
was expected, volatility increases by 3.56 basis points. This supports alternative
36 As per equation (1), lower actuals against expectations result in a positive macroeconomic
surprise. 37 It should be noted that the test conducted here cannot isolate the effect of good from bad news on
volatility. Alternative hypothesis H4c specifies that only bad PPI news increases return volatility.
Despite this, the results presented here are not inconsistent with this alternative hypothesis. The
model specification in section 6.2 allows for a more specific test of alternative hypothesis H4c.
10 August 2016 115
hypothesis H8b based on De Long et al’s (1990) hypothesis that the presence of
irrational investors, trading based on sentiment, increases volatility in excess of that
justified by fundamentals. This assumes consumer sentiment is a reasonable proxy
for investor sentiment (Qiu and Welch 2006, Akhtar et al 2011).
6.2 Dummy Variable Based Model Results
The dummy based model captures the average change in stock returns and stock
return volatility in response to good or bad macroeconomic surprises. The definition
of good and bad surprises is outlined in Chapter 3. The results for the full period are
shown in Table 13.
10 August 2016 116
Table 13 Dummy variable EGARCH model results based on full period
sample
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
tests are two sided based on a null hypothesis of zero
ASX 200 Daily Total Returns: 26 October 2005 - 31 December 2013
Mean Equation
, , , ,
, ,
, ,
( ) +
Fri TS
t Hol t i Day i t j Control i t
i Mon j US
CSI CSI
k Surprise k Bad News
k Unem k Unem
Surprise Bad News
k t k t
t Hol Day Control
D D
R M a a a
a a
Variable Coefficient p-value
AR (1) -0.0449 0.0467**
Hola Holiday 0.3425 0.0003***
Daya Monday 0.0913 0.0698
Daya Tuesday 0.0300 0.5314
Daya Thursday 0.0901 0.0916
Daya Friday 0.0607 0.1528
Controla US Returns (Lagged) 0.3932 0.0000***
Controla Oil Returns (Lagged) 0.0266 0.0268**
Controla Term Spread 0.0364 0.1244
Controla Default Spread -0.0401 0.0556
Surprisea Unemployment 0.1424 0.2043
Surprisea Balance of Trade 0.0764 0.5094
Surprisea Retail Sales 0.0700 0.5653
Surprisea Producer Price Index 0.1372 0.3937
Surprisea Consumer Price Index 0.2886 0.1106
Surprisea Real Gross Domestic Product 0.5838 0.0460**
Surprisea Overnight Cash Rate 0.1598 0.3958
Surprisea Consumer Sentiment Index -0.0719 0.5134
Bad News Announcements
Bad Newsa Unemployment -0.0313 0.8877
Bad Newsa Balance of Trade -0.2069 0.1718
10 August 2016 117
Bad Newsa Retail Sales -0.0060 0.9683
Bad Newsa Producer Price Index -0.2582 0.3939
Bad Newsa Consumer Price Index -0.1748 0.5056
Bad Newsa Real Gross Domestic Product -0.6250 0.0594
Bad Newsa Overnight Cash Rate -0.2282 0.2441
Bad Newsa Consumer Sentiment Index 0.1043 0.4957
Variance Equation
, ,
2
, ,
, , , ,
( ) ln( )
Fri TS
Hol t i t i t
i Mon j US
t i Day j Control
CSI CSISurprise Bad News
k Surprise k t k Bad News k t
k Unem k Unem
Hol Day ControlV b b b
D Db b
Variable Coefficient p-value
Intercept 0.0361 0.7072
ARCH 0.1336 0.0000***
Asymmetry term -0.1253 0.0000***
GARCH 0.9672 0.0000***
Holb Holiday 0.0774 0.4340
Dayb Monday 0.0629 0.6321
Dayb Tuesday -0.3186 0.0470**
Dayb Thursday -0.2381 0.1228
Dayb Friday -0.2796 0.0231**
Controlb US Returns (Lagged) -0.0775 0.0000***
Controlb Oil Returns (Lagged) 0.0016 0.8714
Controlb Term Spread 0.0006 0.9187
Controlb Default Spread 0.0079 0.1843
Surpriseb Unemployment 0.0816 0.4875
Surpriseb Balance of Trade 0.0054 0.9691
Surpriseb Retail Sales 0.0258 0.8645
Surpriseb Producer Price Index 0.2511 0.2214
Surpriseb Consumer Price Index -0.3046 0.1227
Surpriseb Real Gross Domestic Product -0.6175 0.0032***
10 August 2016 118
Surpriseb Overnight Cash Rate -0.0586 0.7453
Surpriseb Consumer Sentiment Index 0.0937 0.4539
Bad News Announcements
Bad Newsb Unemployment 0.1175 0.5433
Bad Newsb Balance of Trade -0.1317 0.3936
Bad Newsb Retail Sales -0.0736 0.6035
Bad Newsb Producer Price Index -0.0116 0.9628
Bad Newsb Consumer Price Index 0.2771 0.2389
Bad Newsb Real Gross Domestic Product 0.6848 0.0079***
Bad Newsb Overnight Cash Rate -0.1853 0.2699
Bad Newsb Consumer Sentiment Index -0.3257 0.0170**
Included observations 2070
Adjusted R-squared 0.2570
Log likelihood -2621.5500
Akaike Information criterion 2.5860
Diagnostics
Q(20) 11.1920 [0.9170]
Q2(20) 11.8900 [0.8900]
ARCH LM Test F-Statistic [p-value] 0.6199 [0.9011]
Engle-Ng Joint Sign Bias Test F-Statistic [p-value] 1.0400 [0.3736]
The results for the mean equation indicate that holidays, US returns and Brent crude
oil futures returns have a positive relationship with ASX 200 returns. This is
consistent with the mean equation results for the continuous model in Table 12.
With respect to the eight macroeconomic variables, returns respond positively to real
GDP surprises, showing an asymmetric response that increases returns by 58.38 basis
points in response to good news, but exhibiting no significant response to bad news.
This rejects my null hypothesis of no relationship between real GDP surprises and
stock returns, indicating good real GDP news is related to increased returns. The
response to good news is consistent with alternative hypothesis H6a and hence the
10 August 2016 119
theories of Jorgenson (1971), Fama (1981) and Campbell and Shiller (1988) who offer
various explanations for a positive relationship between stock returns and expected
future output growth.
The variance equation shows Tuesdays and US returns are related to lower volatility
in ASX 200 returns, which is consistent with the results in Table 12 discussed above.
The dummy based model picks up an additional day-of-the-week effect for Fridays,
which is also related to lower return volatility. Good real GDP surprises appear to
reduce volatility by 61.75 basis points, with an asymmetric bad news response that
increases volatility by 6.73 basis points (-61.75 + 68.48 basis points). This rejects my
null hypothesis of no relationship between real GDP surprises and stock return
volatility. It indicates good real GDP news is associated with decreased return
volatility, while bad real GDP news is associated with increased return volatility and
that the effect of bad real GDP news is slightly stronger than good news. This also
suggests better than expected growth prospects have a ‘calming’ influence on the
stock market, while worse than expected growth has the opposite effect. The good
news effect on volatility is consistent with alternative hypothesis H6c and hence Kim’s
(2003, p.624) observations in the US.
Consumer sentiment index surprises have an asymmetric response to bad news,
decreasing volatility by 32.57 basis points and rejecting my null hypothesis of no
relationship between the consumer sentiment index and stock market volatility. This
asymmetric bad news effect is not intuitively convincing because one would typically
associate bad consumer sentiment with deteriorating business conditions and
increased uncertainty, both of which would be expected to result in increased
volatility. The finding also contradicts Akhtar et al (2011) who found bad consumer
sentiment news decreases returns. Under these circumstances, bad consumer
10 August 2016 120
sentiment news would be more likely to increase (rather than decrease) the volatility
of returns. This result is re-examined in Section 6.5.
6.3 Continuous Model Results: Pre- and Post-Global Financial Crisis
To determine whether the effects differ before and after the onset of the Global
Financial Crisis in 2008, both variants of the model were estimated before and after
(and including) 10 October 2008. Lim, Durand and Yang (2014, p.171) observed the
crises encountered over the period in my study climaxed during October 2008. In
Australian markets, the largest fall in returns was on 10 October, dropping 8.70 per
cent. Two, as opposed to three or more, sub-periods were chosen using this date as
the break point to maximise the number of sub-period observations. The validity of
the results under this structure is tested using alternate dates and three sub-periods
outlined in Section 6.5.
The results for the continuous regression pre- and post-GFC are shown in Table 14.
Table 14 Continuous EGARCH model results: Pre- and Post-GFC
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
tests are two sided based on a null hypothesis of zero
ASX 200 Daily Total Returns
Mean Equation
, , , , , ,( ) +
Fri TS CSI
t Hol t i Day i t j Control i t k Surprise k t
i Mon j US k Unem
t Hol Day Control SurpriseR M a a a a
Variable Coefficient
(pre-GFC) p-value
Coefficient
(post-GFC) p-value
AR(1) -0.1495 0.0001*** - -
Hola Holiday 0.4893 0.0055*** 0.2465 0.0309**
Daya Monday 0.2391 0.0017*** -0.0469 0.4783
Daya Tuesday 0.0443 0.5158 -0.0533 0.3969
Daya Thursday 0.2410 0.0025*** -0.0302 0.6373
Daya Friday 0.1076 0.0911 -0.0362 0.5556
10 August 2016 121
Controla US Returns (Lagged) 0.4235 0.0000*** 0.3711 0.0000***
Controla Oil Returns (Lagged) 0.0185 0.3013 0.0327 0.0263**
Controla Term Spread -0.0092 0.9389 0.0513 0.1167
Controla Default Spread -0.0932 0.0270** 0.0072 0.7593
Surprisea Unemployment -1.0467 0.4404 0.4663 0.2242
Surprisea Balance of Trade -0.0033 0.2583 0.0000 0.9565
Surprisea Retail Sales 0.1771 0.4868 0.1762 0.1420
Surprisea Producer Price Index -0.3933 0.4836 -0.0573 0.8551
Surprisea Consumer Price Index -0.9695 0.1472 1.6623 0.0003***
Surprisea Real Gross Domestic Product -0.0016 0.9969 -0.2786 0.0824
Surprisea Overnight Cash Rate 7.5232 0.0028*** 1.3647 0.2473
Surprisea Consumer Sentiment Index 0.0029 0.8875 0.0279 0.2587
Variance Equation
, , , ,
2
, ,( ) ln( )Fri TS CSI
Hol t i t i t k surprise k t
i Mon j US k Unem
t i Day j ControlHol Day Control SurpriseV b b b b
Variable Coefficient
(pre-GFC) p-value
Coefficient
(post-GFC) p-value
Intercept 0.0551 0.7299 -0.1622 0.1560
ARCH (1) term 0.1242 0.0131** 0.1417 0.0001***
Asymmetry term -0.1576 0.0000*** -0.1101 0.0000***
GARCH (1) term 0.9105 0.0000*** 0.9560 0.0000***
Holb Holiday 0.0362 0.8553 0.0542 0.6751
Dayb Monday -0.0592 0.7824 0.1820 0.2259
Dayb Tuesday -0.4771 0.0892 -0.2139 0.2268
Dayb Thursday -0.4446 0.0551 -0.0213 0.9031
Dayb Friday -0.4023 0.0511 -0.1024 0.5006
Controlb US Returns (Lagged) -0.1015 0.0036*** -0.0630 0.0020***
Controlb Oil Returns (Lagged) -0.0304 0.1668 -0.0024 0.8482
Controlb Term Spread -0.0426 0.4536 0.0294 0.0286**
Controlb Default Spread 0.0653 0.0144** 0.0226 0.1290
surpriseb Unemployment 1.8382 0.2004 -0.2136 0.4943
10 August 2016 122
surpriseb Balance of Trade 0.0041 0.0844 -0.0002 0.3331
surpriseb Retail Sales -0.4835 0.1513 -0.1128 0.3500
surpriseb Producer Price Index 0.5653 0.2626 0.1281 0.5893
surpriseb Consumer Price Index 0.3101 0.6977 -0.2173 0.7039
surpriseb Real Gross Domestic Product -0.3685 0.5634 -0.1690 0.5190
surpriseb Overnight Cash Rate 1.2663 0.4476 -0.1267 0.9121
surpriseb Consumer Sentiment Index 0.0040 0.8898 0.0210 0.3656
pre-GFC post-GFC
Included observations 748 1322
Adjusted R-squared 0.306016 0.2344
Log likelihood -946.1024 -1646.0250
Akaike Information criterion 2.62694 2.5516
Diagnostics
pre-GFC post-GFC
Q(20) [p-value] 12.1450 [0.8790] 6.7375 [0.9920]
Q2(20) [p-value] 12.3620 [0.8700] 20.9900 [0.2800]
ARCH LM Test F-Statistic [p-value] 0.6316 [0.8907] 1.0615 [0.3853]
Engle-Ng Joint Sign Bias Test F-Statistic
[p-value] 0.0779 [0.9719] 0.5121 [0.6740]
Prior to the GFC, holidays, Mondays, Thursdays and US returns were positively
related to returns in the mean equation. The default spread shows a negative
relationship. These results are similar to those for the full period’s continuous
regression.
Post-GFC, the effect of US returns and holidays on the ASX 200 returns remains
significant and of a consistent sign. However, they both have a diminished effect.
Day-of-the-week and default spread effects on ASX 200 returns become insignificant
after the onset of the crisis, while the effect of oil futures returns becomes significant
and positive. With respect to model fitting, the inclusion of an autoregressive lag no
longer results in the most parsimonious fit. These results tend to suggest, after the
10 August 2016 123
stock market euphoria leading up to 2008, there is an increased role for
fundamentals.38
The overnight cash rate is the only macroeconomic announcement to show a
relationship with returns prior to the GFC, strongly increasing returns by 7.5232 per
cent for every one per cent lower the cash rate turned out to be (compared to
expectations). That is, good (bad) cash rate news increases (decreases) returns. This
result rejects my null hypothesis of no relationship and supports alternative
hypothesis H7b which is underpinned by Shiller and Beltratti’s (1992) hypothesis that
investors substitute between dividend paying and interest bearing instruments when
the discount/interest rate changes. The effect becomes insignificant in the post-GFC
period. This result is in contrast to results in the whole period’s regressions in Table
12 and Table 13 above. Both tables did not detect any significant relationship
between cash rates and ASX 200 returns. Reflecting back on Figure 9 in Section
5.2.3, the overnight cash rate fell significantly more than expected on 7 October
2008, which marginally falls within my definition of the pre-crisis period. This could
explain the cash rate’s significance exclusively in the pre-GFC period. The
robustness of this result is tested in Section 6.5.
The coefficient on the CPI surprises becomes significantly positive only after the
GFC. That is, ASX 200 returns increase by 1.6623 per cent for every one per cent the
CPI decreases (compared to expectations). Based on this, it appears good (bad) CPI
news increases (decreases) returns, thus rejecting my hypothesis of no relationship.
The results are consistent with the sign on the CPI in the whole period regression
results in Table 12, but suggest the relationship, detected between CPI and ASX 200
returns over the whole period, stems from the period following the onset of the GFC.
38 The term fundamentals is used here in the same way that Harvey, Liu and Zhu (2014) classify
‘macro’ factors.
10 August 2016 124
This is perhaps due to increased importance placed on fundamentals thereafter.
Fama’s (1981) ‘proxy’ effect hypothesis, where inflation becomes a proxy for
changes in expected future output and stock prices (alternative hypothesis H5b in
Section 3.5), is supported by this result.39
The variance equation shows positive US returns reduce return volatility both before
and after the GFC. Prior to the GFC, increased default spreads are related to increased
stock market volatility, whereas there is no significant default spread effect on returns
reported in the period following the crisis. The term spread coefficient shows no
relationship with ASX 200 return volatility in the pre-GFC period, but reports a
significant positive relationship thereafter. This appears to be counterintuitive because
falling term spreads are associated with an increased risk of recession (Harvey 1989),
and so, one should expect a falling term spread to be associated with increased stock
market risk or volatility. Under these circumstances, a negative relationship between
the term spread and stock market volatility should be observed - not the positive one
reported. The tests in Section 6.5 assess whether this result is robust.
The variance equation reports no significant relationship with any of the
macroeconomic surprises. This is in contrast with the results, for the whole period
regression in Table 12, that report a positive relationship between the PPI and
consumer sentiment index news (of any sign), and return volatility. The finding that
both of these variables are not significant in the sub-periods, pre- and post-GFC, is
possibly a result of the smaller sample sizes within these periods (compared to the
whole period).
39 As noted in the introduction, output is regularly found to be positively related to returns throughout
in the literature.
10 August 2016 125
6.4 Dummy Variable Based Model Results: Pre- and Post-Global Financial
Crisis
The results for the dummy variable variant of the model are shown in Table 15.
Table 15 Dummy variable EGARCH model results: Pre/Post-GFC
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
tests are two sided based on a null hypothesis of zero
ASX 200 Daily Total Returns
Mean Equation
, , , ,
, ,
, ,
( ) +
Fri TS
t Hol t i Day i t j Control i t
i Mon j US
CSI CSI
k Surprise k Bad News
k Unem k Unem
Surprise Bad News
k t k t
t Hol Day Control
D D
R M a a a
a a
Variable Coefficient
(pre-GFC) p-value
Coefficient
(post-GFC) p-value
AR (1) -0.1446 0.0001*** - -
Hola Holiday 0.3717 0.0111** 0.2449 0.0221**
Daya Monday 0.2482 0.0014*** -0.0448 0.5083
Daya Tuesday 0.0732 0.3278 -0.0424 0.5293
Daya Thursday 0.2682 0.0010*** -0.0586 0.3963
Daya Friday 0.1012 0.0904 -0.0437 0.4756
Controla US Returns (Lagged) 0.4372 0.0000*** 0.3710 0.0000***
Controla Oil Returns (Lagged) 0.0115 0.4976 0.0337 0.0208**
Controla Term Spread -0.0236 0.8385 0.0608 0.0686
Controla Default Spread -0.1203 0.0049*** 0.0027 0.9113
Surprisea Unemployment -0.1400 0.4642 0.3222 0.0170**
Surprisea Balance of Trade 0.2526 0.2833 0.0067 0.9581
Surprisea Retail Sales 0.0375 0.8621 -0.0903 0.5312
Surprisea Producer Price Index 0.1403 0.6587 0.2120 0.2545
Surprisea Consumer Price Index 0.0968 0.7008 0.3231 0.1681
Surprisea Real Gross Domestic Product 0.0173 0.8923 1.0057 0.0024***
10 August 2016 126
Surprisea Overnight Cash Rate 0.2263 0.1953 0.0203 0.9351
Surprisea Consumer Sentiment Index -0.0003 0.9981 -0.1754 0.2781
Bad News Announcements
Bad Newsa Unemployment -0.1055 0.8058 0.0238 0.9019
Bad Newsa Balance of Trade -0.2845 0.3183 -0.1838 0.2625
Bad Newsa Retail Sales 0.1070 0.7031 0.1168 0.5146
Bad Newsa Producer Price Index -0.0053 0.9899 -0.4365 0.2555
Bad Newsa Consumer Price Index 0.5976 0.1210 -0.9294 0.0017***
Bad Newsa Real Gross Domestic Product 0.0644 0.8163 -1.1248 0.0024***
Bad Newsa Overnight Cash Rate -0.3822 0.0332** -0.0274 0.9160
Bad Newsa Consumer Sentiment Index -0.1651 0.3794 0.2629 0.1964
Variance Equation
, ,
2
, ,
, , , ,
( ) ln( )
Fri TS
Hol t i t i t
i Mon j US
t i Day j Control
CSI CSISurprise Bad News
k Surprise k t k Bad News k t
k Unem k Unem
Hol Day ControlV b b b
D Db b
Variable Coefficient
(pre-GFC) p-value
Coefficient
(post-GFC) p-value
Intercept 0.1051 0.5306 -0.1318 0.2506
ARCH 0.1831 0.0028*** 0.1200 0.0003***
Asymmetry term -0.2100 0.0000*** -0.1242 0.0000***
GARCH 0.8478 0.0000*** 0.9596 0.0000***
Holb Holiday 0.0285 0.8946 0.0838 0.5320
Dayb Monday -0.2005 0.3130 0.1938 0.1989
Dayb Tuesday -0.6387 0.0113** -0.1901 0.2978
Dayb Thursday -0.6217 0.0064*** -0.0180 0.9183
Dayb Friday -0.7216 0.0002*** -0.0667 0.6608
Controlb US Returns (Lagged) -0.0769 0.0298** -0.0660 0.0007***
Controlb Oil Returns (Lagged) -0.0242 0.2994 0.0050 0.6655
Controlb Term Spread -0.0077 0.9318 0.0192 0.1070
Controlb Default Spread 0.1397 0.0005*** 0.0150 0.2526
Surpriseb Unemployment 0.4057 0.1159 0.0823 0.5626
10 August 2016 127
Surpriseb Balance of Trade 0.2620 0.3211 -0.1230 0.4620
Surpriseb Retail Sales 0.1242 0.6883 -0.1592 0.3602
Surpriseb Producer Price Index 0.5790 0.3242 0.0505 0.7996
Surpriseb Consumer Price Index 0.2927 0.5835 -0.3253 0.1050
Surpriseb Real Gross Domestic Product -1.7855 0.0000*** -0.6478 0.0120**
Surpriseb Overnight Cash Rate -0.0308 0.9298 0.1797 0.3762
Surpriseb Consumer Sentiment Index -0.2569 0.3032 0.1795 0.2581
Bad News Announcements
Bad Newsb Unemployment -0.1381 0.7497 0.1726 0.4633
Bad Newsb Balance of Trade -0.1298 0.7218 -0.1603 0.3808
Bad Newsb Retail Sales -0.7755 0.0333** 0.1298 0.4856
Bad Newsb Producer Price Index -0.5745 0.3777 0.1603 0.5578
Bad Newsb Consumer Price Index -0.3912 0.5035 0.3304 0.2242
Bad Newsb Real Gross Domestic Product 2.1612 0.0000*** 0.4577 0.1166
Bad Newsb Overnight Cash Rate -0.8529 0.0241** -0.0894 0.6750
Bad Newsb Consumer Sentiment Index -0.3083 0.3781 -0.4113 0.0189**
pre-GFC post-GFC
Included observations 748 1322
Adjusted R-squared 0.3033 0.2457
Log likelihood -928.0526 -1639.7270
Akaike Information criterion 2.6285 2.5624
Diagnostics
pre-GFC post-GFC
Q(20) [p-value] 13.0220 [0.8370] 7.4822 [0.9950]
Q2(20) [p-value] 13.2110 [0.8280] 21.1920 [0.3860]
ARCH LM Test F-Statistic [p-value] 0.6767 [0.8514] 1.0542 [0.3937]
Engle-Ng Joint Sign Bias Test F-Statistic
[p-value] 0.1899 [0.9033] 0.7210 [0.5394]
For control variables, the dummy variable specification of the model in Table 15
indicates the same relationship with ASX 200 returns as the continuous model in
Table 14. Returns respond positively to holidays, and positively to US returns in both
10 August 2016 128
the pre- and post-GFC sub-periods. Day-of-the-week effects on returns are positive
for Monday and Thursday during the pre-GFC period, but thereafter, report no
significant effects. The default spread reports a negative relationship with returns in
the period preceding the crisis, but reports no relationship after its onset. Post-GFC,
oil returns exhibit a positive relationship with stock returns, while no effects were
found in the period prior to the GFC. The autoregressive lags no longer have a role in
fitting a parsimonious model. As with the continuous model, it appears that after the
GFC, fundamentals play a greater part in explaining stock returns.
Turning to the eight macroeconomic variables, good unemployment surprises are
positively related to ASX 200 returns, causing them to increase by 32.22 basis points
on average. That is, good unemployment news is associated with an increase in returns
in the post-GFC period. The null hypothesis of no relationship is rejected. The result
supports alternative hypothesis H1a which is explained by Boyd, Hu & Jagannathan
(2005, p.650). They highlighted unemployment news may be a proxy of growth
expectations. This is because unanticipated decreases in the unemployment rate may
signal faster future output growth. Higher output growth typically equates to higher
growth in corporate cash flows, and higher stock prices and returns. My results
support this hypothesis, but only in the post-GFC period.
Good real GDP surprises are also positively related to ASX 200 returns in the post
crisis period, causing them to increase, on average, by 1.0057 per cent. During the
same period, bad real GDP news has a negative effect on returns (-1.1248 per cent on
average), at the margin that more than offsets the good news effects. On average, this
results in a negative; effect of -11.91 basis points (100.57-112.48 basis points) on
returns.
10 August 2016 129
These results indicate that post-GFC, good real GDP news is associated with
increased returns, rejecting my null hypothesis of no relationship and supporting
alternative hypothesis H6a; that stock returns are related to output growth expectations
(Jorgenson 1971, Fama 1981). This is one of the most fundamental and regular
empirical findings in the literature. One of the more interesting things about this result
is that this fundamental relationship is found only post-GFC. The sign of the effects
are still consistent with the dummy variable based full period’s regression, which is
an encouraging sign of robustness. In the post-crisis period, bad CPI news has an
asymmetric negative relationship with returns, causing them to decrease by 92.94
basis points on average, while good CPI news reports no significant effect. This
finding rejects the null hypothesis of no relationship and, again, supports Fama’s
(1981) proxy effect hypothesis (outlined in alternative hypothesis H5b). Unexpected
increases in inflation (bad CPI news) may be a sign of a deteriorating outlook for
future real output and stock returns.
Overnight cash rate news has an asymmetric effect, only evident prior to the crisis
when the cash rate tended to be rising. Bad news is associated with a decrease in
returns of 38.22 basis points on average, rejecting the hypothesis that the overnight
cash rate has no relationship with stock prices. These results are consistent with those
in the continuous model in Table 14; however, this regression provides additional
information that indicates the cash rate relationship in the continuous model is an
asymmetric one. The results support alternative hypothesis H7b which reasons that
investors substitute from dividend-paying to interest-bearing instruments, when the
discount/interest rate increases (Shiller and Beltratti 1992), and indicate that
investors are particularly sensitive to interest rate increases in the lead up to the GFC.
10 August 2016 130
The volatility equation in the dummy based model shows that, Tuesdays, Thursdays
and Fridays were associated with reduced volatility prior to the crisis. The effect is
not persistent and disappears after the onset of the GFC. US returns are negatively
related to volatility over the whole period. This result is remarkably consistent
throughout out all of the modelling. It appears that stock market risk in Australia is
strongly linked to the performance of the US economy, regardless of Australian
economic conditions. The default spread is positively related to volatility but only
prior to the GFC. The same result was found in the continuous model and suggests,
leading up to the GFC, increased default risk in the debt market is an indicator of
increased risk in the equity market.
With respect to the eight macroeconomic variables, prior to the GFC, good real GDP
news is associated with decreased volatility (-1.7855 per cent on average), while the
marginal effect of bad real GDP news (2.1612 per cent on average) more than offsets
this. This means that bad news, overall, is associated with an increase in volatility of
37.58 basis points (-178.55 + 216.12 basis points) on average. Only the relationships
with good news persist after the onset of the GFC, and are associated with return
volatility being reduced by 64.78 basis points on average. Both results reject the null
hypothesis of no effect and highlight that real GDP announcements have a significant
asymmetric relationship across the whole period and are consistent with alternative
hypothesis H6c.40 The effect of bad news on return volatility, however, is limited to
the period prior to the GFC. These results appear sensible, with the good news effect
being supported by Kim’s (2003, p.624) US findings. It can be reasoned that the bad
news (lower than expected real GDP) may be seen as a sign of deteriorating business
40 Hypothesis H6c only specifies an effect based on good news, however the additional bad news
effect observed here is consistent with this hypothesis in terms of an inverse relationship existing
between the sign on real GDP surprises and the size of returns volatility. That is, higher than
expected (good) real GDP reduces return volatility and vice versa for lower than expected (bad)
real GDP.
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conditions and, therefore, increased uncertainty, risk and heightened volatility in
financial markets. The opposite appears to be the case for good news, or higher than
expected GDP.
Prior to the crisis, bad retail sales have an asymmetric relationship with return
volatility, which decreases on average by 77.55 basis points on bad retail sales’ news
days. This finding rejects the null hypothesis of no relationship with return volatility
and is not consistent with any of the alternative hypotheses postulated. It supports the
possibility that bad, or lower than expected, retail sales news is in fact good news for
the economy and calms the market, reducing volatility. This finding is more closely
examined in Section 6.5 where robustness tests are carried out.
Cash rate news also reports an asymmetric relationship with return volatility prior to
the GFC, with volatility decreasing on average by 85.29 basis points on days where
bad cash rate news is released. This rejects the hypothesis of no relationship between
interest rates and return volatility. There is no intuitive reason why an unexpected
increase in cash rates would dampen market volatility and the result is inconsistent
with the alternative hypotheses postulated. As previously discussed, unexpected
increases in interest rates are defined as bad news. The results are, therefore, showing
an unexpected increase in the cash rate is associated with decreased volatility. This,
if anything, is opposite to what one would expect. The result is tested for robustness
in Section 6.5.
In the period following the onset of the GFC, bad consumer sentiment news has an
asymmetric relationship with return volatility, and it is also associated with a 41.13
basis point decrease in the volatility of returns on average. This result is similar to
that found in the whole period dummy variable based regression shown in Table 13.
As discussed, and in relation to those results, the asymmetric bad news effect is not
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intuitively convincing, and it also contradicts the findings of Akhtar et al (2011).
Again, this result is examined more closely for robustness in Section 6.5.
6.5 Robustness Tests
The fourteen results (including the split into pre- and post-GFC regressions) that are
found to be significant are summarised in Table 16.
Table 16 Summary of Results by Macroeconomic Variable
Variable Continuous Model Dummy Variable based Model
Returns
Unemployment not significant post-GFC
Retail Sales not significant not significant
Producer Price Index not significant not significant
Consumer Price Index full period and post- GFC post-GFC
Real GDP not significant full period and post-GFC
Cash Rate pre-GFC pre-GFC
Consumer Sentiment Index not significant not significant
Return Volatility
Unemployment not significant not significant
Retail Sales not significant pre-GFC
Producer Price Index full period not significant
Consumer Price Index not significant not significant
Real GDP not significant full period, pre- and post-GFC
Cash Rate not significant pre-GFC
Consumer Sentiment Index full period full period and post-GFC
To ensure the robustness of these results, three separate robustness tests have been
carried out. Firstly, all of the regression models were re-estimated using the All
Ordinaries index based total returns (instead of the ASX 200). Secondly, all
significant macroeconomic surprises were cross-checked by re-estimating an ASX
200 returns based regression, on each macroeconomic surprise series, in isolation of
the others. Lastly, given that a large number of the findings were significant, as a
result of splitting the sample into pre- and post-October 2008 sub-periods, an
alternative choice of sub-periods was used to check if the results were robust to the
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choice of break point. The details and results of these robustness tests are outlined in
Appendix C.
The seven main results (including the split into pre- and post-GFC regressions) that
survived the robustness tests are summarised in Table 17.41
Table 17 Summary of Results Surviving Robustness Tests
Variable Continuous Model Dummy Variable based Model
Returns
Unemployment not significant post-GFC
Consumer Price Index post- GFC post-GFC
Real GDP not significant post-GFC
Cash Rate not significant pre-GFC
Return Volatility
Real GDP not significant full period, pre- and post-GFC42
Consumer Sentiment Index full period not significant
6.6 Summary and Discussion of Results
The overnight cash rate is special because it is the only variable that has a robust
relationship with stock market returns prior to the GFC. The following theories and
evidence offer an insight into why this may be. Flannery and James (1984)
hypothesise the effect of nominal interest rate changes is related to a firm’s maturity
composition of nominal contracts. They found that interest rates were significantly
related to the stock price of deposit taking institutions, and that the sensitivity of the
relationship was related to the extent of the maturity mismatch between assets and
liabilities. In the Australian context, this hypothesis is particularly relevant because
41 With respect to control variables, the positive term spread coefficient observed in the post-GFC
period using the continuous model in Section 6.3 was not robust to using an alternative choice of
sub-periods. 42 The effects of bad real GDP news on return volatility did not survive the test using an alternative
choice sub-periods.
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47.6 per cent of the S&P ASX 200 is comprised of financial institutions (as shown in
Figure 15).
Figure 15 ASX 200 Index - Sector Composition
Note. From Standard and Poor’s indices S&P/ASX 200 sector breakdown, July 2015 (S&P 2015)
Faff and Howard (1999) studied the relationship between long-term interest rates and
large Australian bank stock returns, and found a negative relationship during the
period of rapidly rising stock prices between 1978 and 1987. During the period of
relatively subdued stock market growth, between November 1987 and December
1992, no significant relationship was found. Over the period January 1992 to January
2007, Jain, Narayan and Thompson (2011, p.971) found short-term interest rates are
negatively related to the largest four banks stock returns.43
These studies taken with the results of my own study, suggest financial institutions’,
(specifically large banks) stock returns may be sensitive to interest rate changes,
mainly during periods of rapidly rising stock market prices. They also suggest returns
on these stocks are negatively related. This is possibly a result of exacerbated maturity
43 ANZ, Commonwealth Bank of Australian, National Australia Bank and Westpac Banking
Corporation.
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mismatches between assets and liabilities. Alternatively, the stock price level of large
banks, rather than operational aspects such as asset maturity mismatches, may be the
determinant of interest rate sensitivity. At lower prices, the greater potential for capital
growth combined with strong dividend yields from bank stocks may require a greater
than usual or continually sustained change in the direction of short-term interest rates
in order to attract funds to interest bearing securities. Further research is required to
test these hypotheses.
Other than the cash rate, no other macroeconomic variables appear to play a part in
explaining returns during the stock market boom leading up to the GFC. Moreover,
the AR(1) or autocorrelation coefficients during this period were highly significant
and in the order of -15 basis points. This may indicate the market was not weak form
efficient during the stock market boom, as the previous days’ returns appear to have
more explanatory power than fundamental factors, such as real GDP, unemployment
and inflation.
It is interesting to note that unemployment, real GDP and the CPI are fundamental
macroeconomic variables, and their relationships with stock market returns only
became significant in the post-GFC period when market volatility (and thus risk) was
at a relatively high level. This is shown in Figure 16 from 2008 onward.
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Figure 16 ASX 200 Index - Total Daily Returns
The inclusion of autocorrelation or ‘AR’ coefficients in the post-GFC regressions
resulted in an inferior fit to those specifications that excluded them (see Appendix B
for more details). This may indicate, during times of heightened volatility and stock
market risk, fundamental factors (such as unemployment and real GDP growth) play
a more important role in determining stock prices than the previous days' returns and
overnight cash rates.
One might expect the breakdown in the relationship, between fundamentals and the
stock market in the lead up to the ‘peak’ of the market boom prior to the GFC, may
be attributed to overly high levels of consumer or investor sentiment, or irrational
exuberance (Keynes 1936, Shiller 2003). A casual inspection of the consumer
sentiment data shown in Figure 17 tends to indicate, if anything, the opposite. That is,
consumer sentiment levels were trending down over the period up to 2008.
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Figure 17 Westpac-Melbourne Institute Consumer Sentiment Index
Another possibility is that the magnitude of volatility in consumer/investor sentiment
(as opposed to the level of sentiment) is related to the breakdown in the relationship
between fundamentals and the stock market returns. A casual inspection of the
consumer sentiment surprise data in Figure 18 indicates volatility in surprises appears
to be greater during the pre-crisis period where the models fail to detect any
relationship between fundamentals and stock market returns.
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Figure 18 Consumer Sentiment Surprises
The AR(1) or autocorrelation coefficients have greater explanatory power during the
pre-crisis period than fundamentals. This is perhaps evidence of a divergence from
fundamental stock values induced by irrational traders, and thus, evidence against
market efficiency. This warrants a closer look at the AR(1) or autocorrelation
coefficients. If the values of these coefficients, (which are around -15 basis points)
are found to be small, when compared to the bid-ask spread of the average stock, the
effect may only be a market microstructure anomaly and, therefore, unlikely to be
evidence against weak form efficiency in the Australian market (Fama 1970 & 1991).
An examination of this is beyond the scope of this thesis, but regardless, consumer
sentiment index surprises exhibit a robust positive relationship with stock market
volatility over the full cycle of stock market activity in the period I examine. The
effect of consumer sentiment surprise volatility on stock market volatility is in the
order of 3 basis points. Although small, one should not totally dismiss the role of
consumer or investor sentiment as a source of stock market risk in Australia.
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In terms of magnitude, fundamentals appear to play a much more important role as a
determinant of stock market risk, albeit only in the post-crisis period. Real GDP
surprises have a strong influence on stock market volatility, decreasing it by around
65 basis points if the news is good. Prior to the GFC, the effect of good news appears
much stronger reducing volatility by around 180 basis points. These results indicate
good real GDP news is linked to decreased uncertainty and risk, and the calming
influence of good real GDP news is particularly strong when pre-existing levels of
volatility and, thus, market risk are already low. The real GDP effects support the
view that variation in returns is based on fundamentals and, therefore, rational. This
is evidence in favour of Australian markets being strong-form efficient.
Some final points to note concern the relationships between Australian stock market
returns and control variables. In the post-crisis period, the effect of holidays on ASX
200 returns is diminished. Additionally, day-of-the-week effects, default spreads and
autocorrelation coefficients no longer have any explanatory power. This supports the
idea that macroeconomic fundamentals succeed financial market anomalies (in terms
of explanatory power) in more subdued periods of stock market growth and/or
heightened volatility. Brent oil futures returns have a significant positive relationship
with stock returns in the post-crisis period. Oil, and thus oil prices, can be viewed as
a fundamental macroeconomic factor for the Australian economy from both a
consumption and production point of view (Narayan & Wong 2009, p.2772). The
post-crisis significance of oil returns is, therefore, additional evidence that, during
times of subdued stock market growth and/or heightened volatility, fundamental
macroeconomic variables become a more important determinant of stock prices.
US returns are highly significant in all regressions, playing a major role as
determinant of Australian stock market returns. Australian stock returns increase in
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the order of 40 basis points, while return volatility decreases around 8 basis points for
every one per cent increase in US returns.
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7 Conclusion
The use of the Sharpe-Lintner-Black capital asset pricing model in Masters of
Business Administration and other managerial finance courses highlights the
importance of understanding macroeconomic risk in economic and financial decision-
making (Jagannathan & Wang 1996, p.4). The more recent work of Chen, Ross and
Roll (1986), Chan, Karceski and Lakonishok (1998), and Flannery and Protopapdakis
(2002) has turned attention to the identification of macroeconomic variables as risk
factors. In an informationally efficient market, the returns on a broad market portfolio
of firms should respond quickly to announcements pertaining to macroeconomic
variables if these variables are risk factors. As equity market indices (such as the S&P
ASX 200) are constituted from individual stocks, any macroeconomic variable that
affects the expected future cash flows, and/or the required or expected future rates of
return to a significant proportion of individual stocks in an economy, should also
affect the broad market index (Gordon 1962, Ross 1976, Fama 1981, Campbell &
Shiller 1988, Schwert 1990).
7.1 Thesis Contribution
While most research, to date, has focused on the relationship between macroeconomic
data values and stock market prices over long time horizons, this study examines the
relationship between macroeconomic news and stock market prices at a daily level,
using an event study within a regression framework. Previous Australian studies that
examine the effects of macroeconomic news have tended to focus on a limited number
of macroeconomic variables (Singh 1993; Singh 1995; Brooks et al 1999; Kim & In
2002) and have found no evidence of a relationship between macroeconomic
variables and stock market returns.
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I use eight macroeconomic variables and examine the effect of macroeconomic
surprises (news) on Australian stock market returns, over the period October 2005 to
December 2013, in order to identify if any of the variables are risk factors. The
surprises are measured as the unexpected component of the variable’s
announcements. The eight macroeconomic variables are unemployment, balance of
trade, retail sales, the producer and consumer price index, real GDP, the overnight
cash rate and consumer sentiment index. The unexpected component of these
announcements is separated from the expected component by using MMS surveys of
expectations, ARIMA forecasts, and forecasts derived from futures contracts, to
measure and deduct the expected component from the announcements. I measure the
sensitivity of returns/return volatility to the change in magnitude of these
macroeconomic surprises. I also decompose surprises into good and bad news, and
measure the average effect of each to test if responses differ or are ‘asymmetric’.
The study period is split into pre- and post-2008 GFC sub-periods to test whether
relationships between macroeconomic news and stock market returns are different
during these contrasting phases of stock market activity. Three different tests of
robustness are carried out on the results. Firstly, the dependent variable is changed
from the S&P ASX 200 index based returns to the All Ordinaries index based returns.
Secondly, regressions are estimated using only one macroeconomic variable at a time.
Thirdly, alternate break points are chosen to analyse the sub-periods (pre- and post-
GFC) within the study period. The results that are robust to these various tests are
summarised below.
7.2 Main Results
Bad (higher than expected) overnight cash rate news is the only variable to exhibit a
robust relationship with stock market returns prior to the GFC, and is associated with
10 August 2016 143
a decrease in returns. This indicates Australian investors are particularly sensitive to
rises in interest rates in the lead up to the GFC. The rational expectations present value
model theoretically supports the sign of the relationship. This model predicts that
investors substitute from dividend paying shares to interest bearing instruments when
the discount rate (typically reflecting the cash rate) and bond yields increase (Shiller
& Beltratti 1992).
Aside from the cash rate, no other macroeconomic variables play a part in explaining
returns during the period of rapidly increasing stock prices leading up to the GFC.
The AR(1) or autocorrelation coefficients during this period, however, were highly
significant and in the order of -15 basis points. This is perhaps evidence against weak
form market efficiency because it indicates, during this period, previous days’ returns
have greater explanatory power than fundamental macroeconomic factors, such as
real GDP, unemployment and the CPI.
Fundamental factors only became significant in the post-crisis period, where growth
was relatively subdued and stock market volatility was at a relatively high level.
Additionally, the inclusion of autoregressive or ‘AR’ coefficients, in the post-GFC
regressions, resulted in an inferior fit of model to those specifications that excluded
them.
Good (lower than expected) unemployment news has a significant positive
relationship with ASX 200 returns in the post-crisis period. This is consistent with
Boyd, Hu & Jagannathan (2005, p.650) who suggest unemployment news is a proxy
for expectations of higher future output growth. Lower than anticipated
unemployment may signal firms are experiencing, or are expecting to experience, an
increase in demand and, so, hire additional staff in order to increase output. Stock
prices, and thus returns, may therefore be increasing in anticipation of higher output,
10 August 2016 144
corporate cash flows and earnings. Bad (higher than expected) unemployment news
has no significant effect on returns.
The S&P ASX 200 based returns significantly increase (decrease) in reaction to good
(bad) CPI news in the post-crisis period. The dummy variable based model suggests
the effect is asymmetric with only bad (higher than expected) CPI news, decreasing
returns and good news having no effect. The CPI surprises have the strongest
relationship with returns out of all of the macroeconomic variables included in this
study - the sensitivity of the effect is in the order of one for one if not greater. This
relationship supports the ‘proxy’ hypothesis (Fama 1981, p.563) that the negative
relationship between inflation and stock returns is a proxy for the positive relationship
between expected future real output and stock returns.44 Put another way, unexpected
increases (bad CPI news) may be a sign of a deteriorating outlook for future real
output, which is detrimental to stock returns. The significance of the CPI, and
insignificance of the PPI found in this study, is consistent with Tiwari’s (2012,
p.1577) finding that, in Australia, CPI changes precede PPI changes. This is because
the result supports the idea that stock market participants use the consumer price in
place of the producer price index to inform their trading on account of the CPI leading
changes in the PPI.
Real GDP surprises have an asymmetric relationship with returns. Only good (higher
than expected) real GDP news is found to effect returns (positively) during the post-
crisis period. This positive relationship with returns has strong intuitive appeal,
empirical and theoretical support. An abundance of foreign literature exists that
explains and finds evidence to support the positive relationship between output and
44 Given that inflation targeting has been one of the objectives of monetary policy in Australia over
the period observed, the CPI surprises could also be a proxy for the relationship between the
overnight cash rate and stock market returns.
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stock returns. The absence of a relationship during the pre-GFC stock market boom
adds support to the findings of Binswanger (2004). In Canada, Japan and an aggregate
economy consisting of four European G-7 countries, Binswanger (2004, p.248) found
the fundamental relationship between stock returns and real GDP disappeared during
the stock market boom of the 1980s. He concluded there is support for the hypothesis
that speculative bubbles in the stock market were an international phenomenon
affecting major economies during the 1980’s and 1990’s. The findings of Binswanger
(2004) are consistent with the Australian based results of Groenewold (2003 & 2004).
In his 2003 study, he detected a weak relationship between real GDP growth and
Australian stock returns, prior to 1983, which deteriorated in the period thereafter. I
note that the period thereafter included the stock market boom leading up to 1987, as
well as the boom leading up to 2001. In his later (2004) Australian study, he found
stock market prices were not too far from fundamental values over the period of
relatively subdued stock market price growth from 1988 to 1993; however, they
departed substantially from fundamentals (based on real GDP) in the period prior
(from around 1970 to 1987) and from around 1994 to 1999 when stock market price
growth was strong.
Consumer sentiment index surprises exhibit a positive relationship with stock market
volatility, but only over the full cycle of stock market activity in the period I examine.
Consumer sentiment surprises, whether good or bad, increase volatility in stock
market returns in the order of 3 basis points. The effect is small but robust, indicating
sentiment is a risk factor in returns. This could be interpreted as evidence to support
behaviour-driven systematic mispricing (Hirshleifer & Jiang 2010), but the evidence
also shows macroeconomic fundamentals (real GDP) play a much more important
role as determinant of stock market volatility in terms of magnitude.
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Real GDP surprises decrease stock return volatility by around 65 basis points if the
news is good. Prior to the GFC, the effect of good news appears much stronger,
reducing volatility by around 180 basis points. This indicates good real GDP news is
linked to decreased stock market uncertainty and risk, and the calming influence of
good real GDP news is stronger when pre-existing levels of market volatility/risk are
already low. The strength and consistency of real GDP news effects, across the sub-
periods vis-à-vis consumer sentiment, supports the view that variation in returns is
based on fundamentals and is, therefore, rational. In turn, this lends support to the
view that the Australian market is efficient.
The control variables tend to corroborate the idea that fundamental macroeconomic
variables become a more important determinant of stock returns than financial market
anomalies during times of subdued stock market growth and/or heightened volatility.
In the post-crisis period, the effect of holidays on ASX 200 returns are diminished,
while day-of-the-week effects, default spreads and autocorrelation coefficients no
longer have any explanatory power.45 On the other hand, Brent oil futures returns,
which can be viewed as gauging a fundamental macroeconomic factor (oil prices),
have a significant robust positive relationship with stock returns in the post-crisis
period. US returns continue to play a major role as determinant of Australian stock
market returns. This is consistent with the strong and long standing correlation
between the business cycles of the US and Australia, confirming it is still the case that
‘when the US sneezes Australia catches a cold’ (Crosby & Bodman 2005, p.226).
The increased importance of fundamentals in Australian stock market returns post-
GFC is mirrored in the findings of Velinov and Chen (2015, p.16) in France,
Germany, Italy, Japan, the UK and the US where stock prices were found to fall back
45 These variables no longer have any explanatory power when using the best fit of model according
to the Akaike Information Criteria.
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into line with their fundamentals (approximated by industrial production) after the
GFC. The failure of macroeconomic fundamentals to explain stock returns during the
pre-GFC stock market boom appears to be a phenomenon also observed in the US,
UK and Japan during the stock market boom of the 1980’s (Binswanger 2004). As
Binswanger (2004, p.249) suggested, this may be a result of stock market bubbles or
irrational exuberance, which in turn, would suggest that the Australian stock market
was not efficient in the lead up to the GFC (Shiller 2003). The autocorrelation in
returns, exhibited in my results (in the order of -15 basis points), adds support to the
view that the market may have been informationally inefficient during this period.
The evidence against the Australian stock market being informationally inefficient in
the lead up to the GFC may be attributed to consumer sentiment. Consumer sentiment
appears to be particularly volatile in the lead up to the GFC, but relatively less so
thereafter.
7.3 Limitations and Possible Extensions
Alternative methods of data preparation and model specifications have been noted in
their respective chapters. Andersen et al (2007, p.258) implement an alternative data
preparation technique in calculating macroeconomic announcement surprises where
the surprise is divided by the standard deviation of the surprise component. The
volatility equations used in this study are limited to controlling for the potentially
differing effects of negative and positive values of the control variables on volatility.
Using the absolute magnitude of movements in the control variable returns would
allow the absolute size effects of control variables on volatility to be captured. An
evaluation of these alternative methods is beyond the scope of this study, but may
prove to be a fruitful extension to the research on modelling the effect of
macroeconomic announcement surprises on asset prices.
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The significance of the interest rate-stock return relationship, during periods of
rapidly rising stock prices, is suspected to stem from the prevalence of financial
institutions in the S&P ASX 200 and the negative relationship observed between
interest rates and financial institution stock returns during these periods (Flannery and
James 1984, Faff and Howard 1999, Jain, Narayan and Thompson 2011). Further
research is required to better establish and explain the relationship between interest
rates and financial institutions’ stock prices over contrasting periods of stock market
activity. The suspected relationship between interest rates, financial institutions’ stock
prices and the Australian stock market itself also requires more rigorous investigation
in order to confirm the proposed rationale behind the interest rate-stock market return
relationship identified in this study.
Hasan and Ratti (2012, p.1) found an increase in oil returns significantly reduced
overall stock market returns, which is directly counter to my findings in the post-GFC
period. This could indicate increased Australian economic dependence on
commodities, energy and materials-based sectors, since the GFC, as Hasan and Ratti’s
(2012, p.7) study was predominantly based on the period prior to the GFC.46 Further
research would be required to ascertain whether this is so.
The results of my study corroborate the existing literature and indicate interest rates
matter most for stock returns during booms, whereas real GDP, the CPI and
unemployment matter more during periods of subdued stock price growth. The reader
must keep in mind this study is focussed on stock market returns over a period of one
day - not stock market price levels over long horizons. The reader must also keep in
mind only the effect of surprises in macroeconomic variable announcements is
examined - not the effect of the levels of macroeconomic variables. The findings of
46 Specifically, March 2000 to December 2010.
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my study could be quite different if stock price movement over the long run and levels
of the announced macroeconomic variable data were used. For example, theory
suggests investor/consumer sentiment may explain divergences in stock prices away
from fundamentals (De Long, Shleifer, Summers, and Waldmann 1990), but the
framework in this study cannot determine whether the stock market index price level
is justified by levels of the macroeconomic fundamentals or explained by other factors
such as sentiment. The lack of a significant relationship between the stock market
index and macro fundamentals prior to the GFC, therefore, remains unexplained. A
more detailed analysis of the relationship, between investor/consumer sentiment and
the deviation of Australian stock market returns from fundamentals, is a topic for
further research. The most obvious extension of this study, to investigate such effects,
would be to employ a GARCH-X framework (as used by Ratanapakorn & Sharma
(2007)), which includes the error correction term from a cointegrating model of long-
run relationships while simultaneously accounting for short-term effects.
From the perspective of Fama (1970 & 1991), the autocorrelation effects observed in
the pre- GFC period could possibly be a market microstructure anomaly, and in that
case, unlikely to be evidence against weak form efficiency in the Australian stock
market. Determining whether the magnitude of these effects are small enough to be
passed off as a market microstructure anomaly is beyond the scope of this study and,
thus, a topic for further research.
7.4 Final Conclusion
In summary, I have tested the reaction of daily returns to the surprise component of
Australian macroeconomic variable announcements (or news) in order to identify
potential macroeconomic risk factors. The results show the relationship between
macroeconomic variable news and stock market returns differs between ‘boom’
10 August 2016 150
periods in the stock market and periods of relatively subdued growth. This finding is
reflected in earlier-dated Australian studies and studies on foreign markets.
In the stock market boom leading up to the GFC, higher than expected overnight cash
rate news was found to have a negative relationship, with stock returns, that
disappears in the subsequent period of subdued stock market price growth. Lower
than expected cash rate news has no effect. The appearance of an interest-rate rate
relationship with stock returns, during stock market booms and disappearance in other
periods, is consistent with earlier Australian studies. Additionally, in the pre-GFC
period, stock market returns exhibit a negative relationship with the previous days'
returns, which appears to be evidence against market efficiency in terms of returns
being predictable. This may, however, be a market microstructure anomaly.
Macroeconomic fundamentals matter after the onset of the GFC. News of low
unemployment rates is associated with increased returns. News of high real GDP
growth is associated with increased returns. The CPI has the strongest relationship
with returns, out of all of the variables observed, with news of high (low) inflation
decreasing (increasing) returns. US returns maintain their historically strong positive
relationship with Australian stock market returns.
Over the whole period, consumer sentiment and real GDP surprises are the only
macroeconomic variables to impact market risk in terms of stock market volatility.
The findings for Australian stock market returns/return volatility and macroeconomic
surprises are summarised in Table 18 and Table 19.
10 August 2016 151
Table 18 Summary of Results - Returns
Variable Results
Continuous Model Dummy Variable Model
Unemployment No relationship
Post-GFC, good unemployment
news associated with increased
returns
Consumer Price Index
Post-GFC, good (bad) CPI news
associated with increased
(decreased) returns
Post-GFC, bad CPI news
associated with decreased returns
Real Gross Domestic Product No relationship Post-GFC, good real GDP news
associated with increased returns
Overnight Cash Rate No robust relationship Pre-GFC, bad cash rate news
associated with decreased returns
Table 19 Summary of Results - Return Volatility
Variable Results
Continuous Model Dummy Variable Model
Real Gross Domestic Product No relationship
Good real GDP news is
associated with decreased
return volatility over the entire
sample
Bad real GDP news is
associated with increased return
volatility over the entire sample
Pre-GFC, good real GDP news
is associated with decreased
return volatility
Pre-GFC, bad real GDP news is
associated with increased return
volatility
Post-GFC, good real GDP news
is associated with decreased
return volatility
Consumer Sentiment Index
Consumer sentiment index
surprises are associated with
an increase in return volatility
over the entire sample
No relationship
10 August 2016 152
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9 Appendices
9.1 Appendix A – Structure of Macroeconomic Announcement Data and
Dates
The announcement day date for a macroeconomic variable is distinct from the
observation date for the same variable. This is because the announcement for the
variable usually occurs sometime after the period in which it is observed, due to the
time it takes for the information underlying the announced value to be collected and
processed. For example, the unemployment figure for the observation period
December 2003 is announced on 15 January 2004. Exceptions to this are the cash rate
and consumer sentiment index announcements. Cash rate announcements are made
the day before the intention to implement. Consumer sentiment index announcements
are made during the month in which the underlying survey was carried out.
Announcement dates are required to match unrevised macroeconomic announcement
data values with the appropriate stock return date. ABS announcement day dates are
manually retrieved from the ABS’s ‘Past and Future Releases’ page and paired with
announced values. The availability of these announcement dates on the ABS website
is one of the main constraints on utilising the full series of announced data values.
Announcement day dates for the cash rate are the first Tuesday of every month, except
for January. Announcement day dates for the consumer sentiment index are sourced
from MMS. The announcement dates, found and used to pair this macroeconomic
data to stock returns, are outlined in Table 20.
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Table 20 Macroeconomic Announcement Date Structure
Announcement Date Range Number of
announcements Missing Dates
Announcement
Delay
Unemployment January 2004 –
December 2013 120 0 1 month
Balance of Trade August 2005–
December 2013 101 0 2 months
Retail Sales August 2005–
December 2013 101 0 2 months
Producer Price Index October 2005–
November 2013 33 0 1 month
Consumer Price Index October 2005–
October 2013 33 0 1 month
Real GDP January 2000–
December 2013 55 0 3 months
Overnight Cash Rate September 2003
– December 2013 109 0 None
Consumer Sentiment April 2004 –
December 2013 117 9 None
The structure of the macroeconomic data series in their raw form is outlined in Table
21.
Table 21 Raw Macroeconomic Announcement Data Series Structure
Announcement Format Frequency Observation Span Missing
Values
Unemployment Per cent level Monthly December 2003 –
November 2013 1
Balance of Trade $ Billion Monthly June 2005–
October 2013 0
Retail Sales Per cent change from
previous month Monthly
June 2005–
October 2013 0
Producer Price Index Per cent change from
previous quarter Quarterly
September 2005–
September 2013 0
Consumer Price Index Per cent change from
previous quarter Quarterly
September 2005–
September 2013 0
Real GDP Per cent change from
last quarter Quarterly
March 2000–
December 2013 0
Overnight Cash Rate Per cent level Monthly September 2003 –
December 2013 0
Consumer Sentiment Index level Monthly April 2004 –
December 2013 0
10 August 2016 165
All macroeconomic announcement data are downloaded from the MMS database with
the exception of the overnight cash rate and consumer sentiment index (Haver 2013).
The data are values as first reported; that is, they are not revised values. Data on cash
rate announcements are sourced from the RBA. Their Monetary Policy Changes Table
A02 contains a history of new target cash rates that (from 2000) take effect on the
Wednesday after the announcement (Reserve Bank of Australia 2013). The
announcement occurs on the first Tuesday of every month, except January. For
months that were absent from table A02, I record the last available cash rate. This is
because a missing date means there was no change in monetary policy. Announced
Westpac-Melbourne Institute consumer sentiment index values are sourced from
Bloomberg using the ticker WMCCCONS Index. The index is never revised, so it is
not necessary to source unrevised announcement data.47 Nine of the announcement
dates for the consumer sentiment index are unavailable through MMS, resulting a in
a paring back of useful observations from 117 to 108.
All announcement dates were checked to ensure that they fell only on weekdays,
which was the case.
47 M.Best (personal communication, 7 May 2013) confirmed this.
10 August 2016 166
9.2 Appendix B – Model Fitting
9.2.1 Fitting ARMA for Stock Market Return Modelling
The EGARCH specification appeared to be a good candidate for modelling returns as
discussed in Section 2.3.2. Before resorting to this more complicated specification,
various ARMA specifications were estimated using Eviews 7 statistical software’s
non-linear least squares and ARMA regression function, and based on what was
outlined in Chapter 4.4, using Eviews 7 statistical software’s non-linear least squares
and ARMA regression function. Each specification was ranked according to the
Akaike Information Criteria (AIC). The AIC is preferred to the residual sum of
squares because it not only takes account of the fit to the data but also penalises the
use of additional variables, mitigating the possibility of selecting an over-fitted
specification.
In Table 22, I focus on the intercept, autoregressive lag order p, and moving average
term order q, for the purposes of determining the best ARMA (p,q) fit.
Table 22 AIC - All Ordinaries Total Returns ARMA Regression
ARMA (p,q) Akaike Information Criterion
(0,0) 2.9231
(0,1) 2.9223
(0,2) 2.9227
(1,0) 2.9222
(1,1) 2.9216
(1,2) 2.9225
(2,0) 2.9226
(2,1) 2.9225
(2,2) 2.9234
(1,1) (No Intercept) 2.9219
Number of Observations 2070
10 August 2016 167
The ARMA (1,1) specification that includes an intercept (in bold) exhibits the lowest
AIC (2.9216), and according to the criteria, it is the most parsimonious fit on the 2070
observations of stock returns.
Diagnostics on the standardised residuals of the ARMA (1,1) fit indicate the model
does not adequately capture serial correlation in the standardised residuals. The Q
statistics on the squared standardised residuals in Table 23 reject the null hypothesis
of no autocorrelation at all lags, indicating the conditional variance is time varying –
that is, autoregressive conditional heteroscedasticity (ARCH) effects are present.
Table 23 Q-Statistics on ARMA Model Squared Standardised Residuals
p-value
Lag 1 2 3 4 5 6 7 8 9 10 11 12
- - <0.001*** <0.001*** <0.001*** <0.001*** <0.001*** <0.001*** <0.001*** <0.001*** <0.001*** <0.001***
Lag 13 14 15 16 17 18 19 20 21 22 23 24
<0.001*** <0.001*** <0.001*** <0.001*** <0.001*** <0.001*** <0.001*** <0.001*** <0.001*** <0.001*** <0.001*** <0.001***
The ARCH LM test of the squared residuals in Table 24 confirms the presence of
ARCH effects.
Table 24 ARCH LM Test on ARMA Squared Residuals
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
Test Lags Chi Squared p-value
ARCH LM 1 216.0102 <0.001***
The ARCH LM test was conducted at one lag. If the test indicates ARCH effects are
present at one lag, and the true lag structure is longer than this, it can be concluded an
ARCH effect exists (Enders 2004, p.146). That is, the variance is time varying and
requires a model that captures the effect of time on the residuals.
10 August 2016 168
9.2.2 Fitting GARCH/EGARCH for Stock Market Return and Time Varying
Volatility Modelling
If the return volatility is time varying, the coefficients and standard errors from the
fitted ARMA model could vary significantly from that of simultaneously estimated
mean return, and also the return volatility model (Enders 2004, p.145). In light of this,
I consider a GARCH specification that models both returns and return volatility
simultaneously to be more appropriate for this study.
There has been a proliferation of GARCH models since Tim Bollerslev formalised
the original specification in 1986. Some of the newer variations, such as TARCH and
EGARCH, account for asymmetric volatility responses to shocks.
As see in the Section 2.2, Kearns and Pagan (1993, p.169) found evidence that
Australian stock returns exhibit asymmetric volatility responses. To test for the
presence of asymmetric volatility responses, I fitted a standard GARCH model (using
the AIC) to select the best fit. A GARCH specification with an AR(1) mean equation
gave the most parsimonious fit. The residual diagnostics still indicated the presence
of serial correlation with results identical to those shown in Table 23. Additionally,
Engle and Ng’s (1993) Sign Bias test strongly rejected the joint hypothesis test of no
sign bias with an F distribution p-value of less than 0.0001.
These results suggest that a model, which allows for asymmetric responses in
volatility to return shocks, is more appropriate than a standard GARCH model.
An EGARCH specification was considered the most appropriate because it ensures
the estimated conditional variance is always positive, while allowing the coefficients
on the explanatory variables in the GARCH model to be negative. Based on this
analysis, the model ultimately settled upon for use in Chapter 6 is an EGARCH model.
10 August 2016 169
What follows is an outline of the fitting process for the continuous model in Section
6.1. The basic process outlined is repeated for the remainder of the models. A
summary of diagnostic results is shown at the bottom of their respective tables. For
example, the diagnostics for the first model are shown at the bottom of Table 12 in
Section 6.1.
The most parsimonious fit was an EGARCH (1,1) model, simultaneously estimated
with a mean model using an intercept. There were, however, no lagged autoregressive
or moving average terms.
Diagnostics on the standardised residuals indicated the model satisfactorily captured
serial correlation and sign bias. The Ljung-Box Q statistics out to 20 lags on the
standardised residuals indicated no serial correlation was present, and reported a p-
value of 0.8020. The ARCH LM test out to 20 lags was 0.9119, indicating no ARCH
effects. The Engle-Ng sign bias test indicated no sign bias, reporting an F-distributed
p-value of 0.2997.
Quantile plots of the standardised residuals were constructed to assess whether the
normal distribution for residuals was an appropriate assumption. Figure 19 shows,
despite a slightly skewed pattern, most of the empirically observed standardised
residuals from the fitted model (above) closely match the theoretical quantiles (down
to the second negative standard deviation). Only below the second standard deviation
(positive and negative) do they begin to fall away from the black line. This suggests
that the assumption of normally distributed standardised residuals is appropriate. I
only present the diagnostic for the first model because plots for the other models were
very similar. Plots for other models can be presented on request.
10 August 2016 170
Figure 19 EGARCH Normal Distribution Quantile Plot
10 August 2016 171
9.3 Appendix C – Robustness Tests
9.3.1 All Ordinaries Index Based Regressions
To test robustness, all regressions in the results section above were re-run using the
All Ordinaries index based total returns (over and above the regression using the ASX
200). Differing results are highlighted in the output tables in the following way:
Results in this section that were significant in the ASX 200 based regressions
but not in the All Ordinaries based regressions are emboldened.
Results in this section that were not significant in the ASX 200 based
regressions but found to be significant in the All Ordinaries based regressions
are in italics.
The All Ordinaries based regressions indicate some additional relationships that were
not evident in the ASX 200 based regressions. These relationships might suggest
firms with lower market capitalisation react differently to certain types of news than
firms with relatively high capitalisation. This is because as the All Ordinaries index
includes more firms with lower capitalisation than the ASX 200. The exploration of
this possibility, however, is outside the scope of this paper and maybe a subject for
further research.
10 August 2016 172
Full Period Continuous Regression
Table 25 Continuous Model using All Ordinaries Index based Returns
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
tests are two sided based on a null hypothesis of zero
All Ordinaries Daily Total Returns
Mean Equation
, , , , , ,( ) +
Fri TS CSI
t Hol t i Day i t j Control i t k Surprise k t
i Mon j US k Unem
t Hol Day Control SurpriseR M a a a a
Variable Coefficient p-value
Intercept 0.1226 0.0152**
Hola Holiday 0.3040 0.0021***
Daya Monday 0.0246 0.6572
Daya Tuesday -0.0461 0.3672
Daya Thursday 0.0202 0.7158
Daya Friday -0.0127 0.8035
Controla US Returns (Lagged) 0.3738 0.0000***
Controla Oil Returns (Lagged) 0.0293 0.0116**
Controla Term Spread 0.0307 0.1936
Controla Default Spread -0.0695 0.0065***
Surprisea Unemployment 0.3875 0.2780
Surprisea Balance of Trade 0.0000 0.9402
Surprisea Retail Sales 0.1792 0.0468**
Surprisea Producer Price Index -0.1227 0.6640
Surprisea Consumer Price Index 0.6889 0.0547
Surprisea Real Gross Domestic Product -0.1872 0.3381
Surprisea Overnight Cash Rate 2.0089 0.0846
Surprisea Consumer Sentiment Index 0.0104 0.5024
10 August 2016 173
Variance Equation
, , , ,
2
, ,( ) ln( )Fri TS CSI
Hol t i t i t k surprise k t
i Mon j US k Unem
t i Day j ControlHol Day Control SurpriseV b b b b
Variable Coefficient p-value
Intercept 0.0000 0.9999
ARCH (1) term 0.1225 0.0000***
Asymmetry term -0.1253 0.0000***
GARCH (1) term 0.9616 0.0000***
Holb Holiday 0.0218 0.8235
Dayb Monday 0.0921 0.4952
Dayb Tuesday -0.3422 0.0316**
Dayb Thursday -0.1973 0.2007
Dayb Friday -0.2279 0.0642
Controlb US Returns (Lagged) -0.0826 0.0000***
Controlb Oil Returns (Lagged) 0.0004 0.9673
Controlb Term Spread 0.0029 0.6298
Controlb Default Spread 0.0096 0.1858
surpriseb Unemployment -0.1892 0.4918
surpriseb Balance of Trade -0.0003 0.1091
surpriseb Retail Sales 0.0220 0.8273
surpriseb Producer Price Index 0.4330 0.0403**
surpriseb Consumer Price Index -0.3904 0.3687
surpriseb Real Gross Domestic Product -0.0821 0.7326
surpriseb Overnight Cash Rate 0.8038 0.4088
surpriseb Consumer Sentiment Index 0.0353 0.0293**
Included observations 2070
Adjusted R-squared 0.2653
Log likelihood -2542.5750
Akaike Information criterion 2.4943
Diagnostics
Q(20) [p-value] 11.8210 [0.9220]
Q2(20) [p-value] 11.2400 [0.9400]
10 August 2016 174
ARCH LM Test F-Statistic [p-value] 0.5953 [0.9187]
Engle-Ng Joint Sign Bias Test F-Statistic [p-value] 0.6000 [0.6150]
Table 25 includes results across the whole study period. The mean equation (in the
continuous regression) reports that retail sales have a significant relationship with All
Ordinaries based returns. This was not evident in ASX 200 returns based regression.
Additionally, the CPI, which was significant in the ASX 200 based regressions, is not
significant in the All Ordinaries based regression. This indicates the CPI relationship
with returns, found across the whole period, is dependent on the type of index used. I
therefore consider this non-robust.
10 August 2016 175
Full Period Dummy Variable Based Regression
Table 26 Dummy Variable Model using All Ordinaries Index
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
tests are two sided based on a null hypothesis of zero
All Ordinaries Daily Total Returns
Mean Equation
, , , ,
, ,
, ,
( ) +
Fri TS
t Hol t i Day i t j Control i t
i Mon j US
CSI CSI
k Surprise k Bad News
k Unem k Unem
Surprise Bad News
k t k t
t Hol Day Control
D D
R M a a a
a a
Variable Coefficient p-value
Intercept 0.1207 0.0257**
Hola Holiday 0.3231 0.0003***
Daya Monday 0.0277 0.6407
Daya Tuesday -0.0370 0.5060
Daya Thursday 0.0179 0.7738
Daya Friday -0.0022 0.9672
Controla US Returns (Lagged) 0.3759 0.0000***
Controla Oil Returns (Lagged) 0.0291 0.0150**
Controla Term Spread 0.0347 0.1356
Controla Default Spread -0.0766 0.0027**
Surprisea Unemployment 0.1303 0.2301
Surprisea Balance of Trade 0.0628 0.5669
Surprisea Retail Sales 0.0375 0.7475
Surprisea Producer Price Index 0.1609 0.2851
Surprisea Consumer Price Index 0.2021 0.2410
Surprisea Real Gross Domestic Product 0.4951 0.0830
Surprisea Overnight Cash Rate 0.1369 0.4442
Surprisea Consumer Sentiment Index -0.0974 0.3594
Bad News Announcements
Bad Newsa Unemployment -0.0022 0.9920
10 August 2016 176
Bad Newsa Balance of Trade -0.1419 0.3258
Bad Newsa Retail Sales 0.0174 0.9032
Bad Newsa Producer Price Index -0.2874 0.3228
Bad Newsa Consumer Price Index -0.1611 0.5239
Bad Newsa Real Gross Domestic Product -0.5879 0.0706
Bad Newsa Overnight Cash Rate -0.2076 0.2686
Bad Newsa Consumer Sentiment Index 0.0880 0.5453
Variance Equation
, ,
2
, ,
, , , ,
( ) ln( )
Fri TS
Hol t i t i t
i Mon j US
t i Day j Control
CSI CSISurprise Bad News
k Surprise k t k Bad News k t
k Unem k Unem
Hol Day ControlV b b b
D Db b
Variable Coefficient p-value
Intercept 0.0271 0.7881
ARCH 0.1392 0.0000***
Asymmetry term -0.1373 0.0000***
GARCH 0.9621 0.0000***
Holb Holiday 0.0710 0.4822
Dayb Monday 0.0454 0.7355
Dayb Tuesday -0.3228 0.0486**
Dayb Thursday -0.2639 0.0978
Dayb Friday -0.2950 0.0181**
Controlb US Returns (Lagged) -0.0809 0.0000***
Controlb Oil Returns (Lagged) 0.0038 0.7117
Controlb Term Spread 0.0026 0.6770
Controlb Default Spread 0.0146 0.0507
Surpriseb Unemployment 0.0917 0.4439
Surpriseb Balance of Trade -0.0181 0.8987
Surpriseb Retail Sales 0.0515 0.7353
Surpriseb Producer Price Index 0.2663 0.2128
Surpriseb Consumer Price Index -0.3222 0.1089
10 August 2016 177
Surpriseb Real Gross Domestic Product -0.5930 0.0049***
Surpriseb Overnight Cash Rate -0.0409 0.8281
Surpriseb Consumer Sentiment Index 0.0862 0.4989
Bad News Announcements
Bad Newsb Unemployment 0.1519 0.4467
Bad Newsb Balance of Trade -0.1197 0.4475
Bad Newsb Retail Sales -0.0997 0.4977
Bad Newsb Producer Price Index -0.0028 0.9912
Bad Newsb Consumer Price Index 0.2726 0.2498
Bad Newsb Real Gross Domestic Product 0.6610 0.0123**
Bad Newsb Overnight Cash Rate -0.1980 0.2568
Bad Newsb Consumer Sentiment Index -0.3397 0.0159**
Included observations 2070
Adjusted R-squared 0.2614
Log likelihood -2536.4600
Akaike Information criterion 2.5038
Diagnostics
Q(20) 12.9900 [0.8780]
Q2(20) 12.9890 [0.8780]
ARCH LM Test F-Statistic [p-value] 0.6903 [0.8395]
Engle-Ng Joint Sign Bias Test F-Statistic
[p-value] 0.6883 [0.5591]
Table 26 reports the All Ordinaries based dummy variable regressions for the whole
period. The mean equation results indicate the real GDP figures are not significant in
the All Ordinaries based mean equation. This, again, indicates the real GDP results,
found across the whole sample, are sensitive to the chosen returns index. I therefore
consider this result non-robust.
10 August 2016 178
Pre- and Post-GFC Period Continuous Regression
Table 27 Continuous Model using All Ordinaries Index: Pre- and Post-GFC
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
tests are two sided based on a null hypothesis of zero
All Ordinaries Index Daily Total Returns
Mean Equation
, , , , , ,( ) +
Fri TS CSI
t Hol t i Day i t j Control i t k Surprise k t
i Mon j US k Unem
t Hol Day Control SurpriseR M a a a a
Variable Coefficient
(pre-GFC) p-value
Coefficient
(post-GFC) p-value
AR(1) -0.1193 0.0019*** - -
Hola Holiday 0.4234 0.0093*** 0.2250 0.0408**
Daya Monday 0.2284 0.0013*** -0.0465 0.4608
Daya Tuesday 0.0411 0.5195 -0.0669 0.2716
Daya Thursday 0.2263 0.0031*** -0.0496 0.4247
Daya Friday 0.1087 0.0810 -0.0389 0.5109
Controla US Returns (Lagged) 0.4012 0.0000*** 0.3599 0.0000***
Controla Oil Returns (Lagged) 0.0220 0.2077 0.0365 0.0118**
Controla Term Spread 0.0098 0.9323 0.0603 0.0539
Controla Default Spread -0.0667 0.1030 0.0098 0.6665
Surprisea Unemployment -0.7767 0.5475 0.3890 0.2844
Surprisea Balance of Trade -0.0026 0.3677 0.0000 0.5423
Surprisea Retail Sales 0.2506 0.2811 0.1832 0.1006
Surprisea Producer Price Index -0.9275 0.0167** -0.0993 0.7278
Surprisea Consumer Price Index -0.6791 0.3226 1.4573 0.0026***
Surprisea Real Gross Domestic Product -0.1529 0.6666 -0.2451 0.0961
Surprisea Overnight Cash Rate 5.4656 0.0508 1.3678 0.2148
Surprisea Consumer Sentiment Index -0.0007 0.9707 0.0309 0.1774
10 August 2016 179
Variance Equation
, , , ,
2
, ,( ) ln( )Fri TS CSI
Hol t i t i t k surprise k t
i Mon j US k Unem
t i Day j ControlHol Day Control SurpriseV b b b b
Variable Coefficient
(pre-GFC) p-value
Coefficient
(post-GFC) p-value
Intercept 0.0858 0.6094 -0.1678 0.1465
ARCH (1) term 0.0982 0.0494** 0.1311 0.0001***
Asymmetry term -0.1719 0.0000*** -0.1230 0.0000***
GARCH (1) term 0.8999 0.0000*** 0.9545 0.0000***
Holb Holiday 0.0124 0.9471 0.0888 0.4852
Dayb Monday -0.0915 0.6728 0.2067 0.1736
Dayb Tuesday -0.5223 0.0637 -0.2187 0.2393
Dayb Thursday -0.4602 0.0678 0.0077 0.9643
Dayb Friday -0.4498 0.0287** -0.0860 0.5793
Controlb US Returns (Lagged) -0.1037 0.0032*** -0.0759 0.0001***
Controlb Oil Returns (Lagged) -0.0278 0.1825 0.0036 0.7614
Controlb Term Spread -0.0464 0.4068 0.0249 0.0429**
Controlb Default Spread 0.0740 0.0016*** 0.0206 0.1545
surpriseb Unemployment -0.3566 0.7701 0.0671 0.8058
surpriseb Balance of Trade 0.0022 0.3624 -0.0003 0.0429**
surpriseb Retail Sales -0.8912 0.0002*** 0.0674 0.4913
surpriseb Producer Price Index 0.7779 0.1954 0.0208 0.9197
surpriseb Consumer Price Index -0.2253 0.7580 -0.0356 0.951
surpriseb Real Gross Domestic Product 0.6263 0.2290 -0.1753 0.4708
surpriseb Overnight Cash Rate 2.1466 0.0742 0.1102 0.9164
surpriseb Consumer Sentiment Index 0.0191 0.4153 -0.0402 0.0356**
pre-GFC post-GFC
Included observations 748 1322
Adjusted R-squared 0.2791 0.2545
Log likelihood -900.6437 -1602.7340
Akaike Information criterion 2.5124 2.4822
10 August 2016 180
Diagnostics
pre-GFC post-GFC
Q(20) [p-value] 13.3870 [0.8180] 7.7163 [0.9940]
Q2(20) [p-value] 9.4784 [0.9650] 22.2730 [0.3260]
ARCH LM Test F-Statistic [p-value] 0.4711 [0.9767] 1.1260 [0.3150]
Engle-Ng Joint Sign Bias Test F-Statistic [p-
value] 0.8964 [0.4426] 0.3227 [0.8090]
Table 27 shows the continuous All Ordinaries regression, splitting the whole period
sample into pre- and post-GFC sub-periods.
Prior to the GFC, the All Ordinaries based mean equation indicates the overnight cash
rate relationship with returns (which were significant when using the ASX 200 based
returns) is not significant. I therefore consider this result to be non-robust.
Prior to the GFC, the All Ordinaries based regression results report the PPI has a
significant effect on returns, while the ASX 200 based regression shows no
relationship. In the same sub-period, the variance equation also reports retail sales
surprises have a significant effect. In the period following the onset of the GFC, the
balance of trade surprises and consumer sentiment index surprises show a significant
relationship not detected using the ASX 200 index.
10 August 2016 181
Pre- and Post-GFC Period Dummy Variable Based Regression
Table 28 Dummy Variable Model using All Ordinaries Index: Pre- and Post-
GFC
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
tests are two sided based on a null hypothesis of zero
All Ordinaries Index Daily Total Returns
Mean Equation
, , , ,
, ,
, ,
( ) +
Fri TS
t Hol t i Day i t j Control i t
i Mon j US
CSI CSI
k Surprise k Bad News
k Unem k Unem
Surprise Bad News
k t k t
t Hol Day Control
D D
R M a a a
a a
Variable Coefficient
(pre-GFC) p-value
Coefficient
(post-GFC) p-value
AR (1) -0.1212 0.0015*** - -
Hola Holiday 0.3633 0.0094*** 0.2520 0.0164**
Daya Monday 0.2462 0.0009*** -0.0433 0.5052
Daya Tuesday 0.0714 0.3085 -0.0558 0.3902
Daya Thursday 0.2638 0.0007*** -0.0680 0.3065
Daya Friday 0.1028 0.0803 -0.0419 0.4834
Controla US Returns (Lagged) 0.4142 0.0000*** 0.3603 0.0000***
Controla Oil Returns (Lagged) 0.0168 0.3051 0.0366 0.0100***
Controla Term Spread -0.0434 0.7022 0.0660 0.0394**
Controla Default Spread -0.1138 0.0060*** 0.0048 0.8348
Surprisea Unemployment -0.1467 0.4099 0.3094 0.0170**
Surprisea Balance of Trade 0.2171 0.3266 0.0128 0.9139
Surprisea Retail Sales 0.0339 0.8671 -0.0669 0.6284
Surprisea Producer Price Index 0.1663 0.6025 0.1934 0.2733
Surprisea Consumer Price Index 0.0718 0.7558 0.2613 0.2507
Surprisea Real Gross Domestic Product 0.0181 0.8806 0.9701 0.0023***
Surprisea Overnight Cash Rate 0.2043 0.2354 -0.0016 0.9944
Surprisea Consumer Sentiment Index 0.0030 0.9785 -0.1767 0.2565
Bad News Announcements
10 August 2016 182
Bad Newsa Unemployment -0.0720 0.8653 0.0350 0.8494
Bad Newsa Balance of Trade -0.2270 0.4013 -0.1698 0.2712
Bad Newsa Retail Sales 0.0815 0.7587 0.1082 0.5225
Bad Newsa Producer Price Index -0.1469 0.7244 -0.4075 0.2775
Bad Newsa Consumer Price Index 0.5862 0.1080 -0.8464 0.0029
Bad Newsa Real Gross Domestic Product 0.0306 0.9114 -1.0913 0.0023
Bad Newsa Overnight Cash Rate -0.3637 0.0438 -0.0169 0.9454
Bad Newsa Consumer Sentiment Index -0.2051 0.2654 0.2562 0.1892
Variance Equation
, ,
2
, ,
, , , ,
( ) ln( )
Fri TS
Hol t i t i t
i Mon j US
t i Day j Control
CSI CSISurprise Bad News
k Surprise k t k Bad News k t
k Unem k Unem
Hol Day ControlV b b b
D Db b
Variable Coefficient
(pre-GFC) p-value
Coefficient
(post-GFC) p-value
Intercept 0.0800 0.6364 -0.1254 0.2716
ARCH 0.1912 0.0017*** 0.1238 0.0002***
Asymmetry term -0.2170 0.0000*** -0.1251 0.0000***
GARCH 0.8497 0.0000*** 0.9569 0.0000***
Holb Holiday 0.0226 0.9161 0.0979 0.4674
Dayb Monday -0.1994 0.3158 0.1733 0.2457
Dayb Tuesday -0.6496 0.0109** -0.2211 0.2270
Dayb Thursday -0.6200 0.0076*** -0.0239 0.8908
Dayb Friday -0.6684 0.0006*** -0.0901 0.5495
Controlb US Returns (Lagged) -0.0839 0.0179** -0.0688 0.0004***
Controlb Oil Returns (Lagged) -0.0206 0.3891 0.0052 0.6538
Controlb Term Spread -0.0005 0.9952 0.0210 0.0832
Controlb Default Spread 0.1346 0.0005*** 0.0158 0.2394
Surpriseb Unemployment 0.3969 0.1204 0.0650 0.6469
Surpriseb Balance of Trade 0.2567 0.3286 -0.1671 0.3160
Surpriseb Retail Sales 0.1213 0.6933 -0.1555 0.3780
Surpriseb Producer Price Index 0.5856 0.3300 0.0724 0.7137
10 August 2016 183
Surpriseb Consumer Price Index 0.1963 0.7154 -0.3409 0.0881
Surpriseb Real Gross Domestic Product -1.7874 0.0000*** -0.6195 0.0153**
Surpriseb Overnight Cash Rate -0.0348 0.9227 0.1879 0.3546
Surpriseb Consumer Sentiment Index -0.2501 0.3162 0.1606 0.3137
Bad News Announcements
Bad Newsb Unemployment -0.0964 0.8251 0.1901 0.4235
Bad Newsb Balance of Trade -0.1055 0.7715 -0.1137 0.5323
Bad Newsb Retail Sales -0.7448 0.0365** 0.1365 0.4616
Bad Newsb Producer Price Index -0.5301 0.4270 0.1641 0.5555
Bad Newsb Consumer Price Index -0.3429 0.5613 0.3212 0.2480
Bad Newsb Real Gross Domestic Product 2.1619 0.0000*** 0.4287 0.1416
Bad Newsb Overnight Cash Rate -0.7765 0.0441** -0.0871 0.6859
Bad Newsb Consumer Sentiment Index -0.2927 0.4087 -0.4036 0.0208**
pre-GFC post-GFC
Included observations 748 1322
Adjusted R-squared 0.2754 0.2528
Log likelihood -892.3771 -1593.5880
Akaike Information criterion 2.5331 2.4926
Diagnostics
pre-GFC post-GFC
Q(20) [p-value] 13.5420 [0.8100] 8.3430 [0.9890]
Q2(20) [p-value] 10.7090 [0.9330] 21.4840 [0.3690]
ARCH LM Test F-Statistic [p-value] 0.5442 [0.9478] 1.0765 [0.3682]
Engle-Ng Joint Sign Bias Test F-Statistic
[p-value] 0.1424 [0.9345] 0.3926 [0.7583]
The All Ordinaries returns based dummy variable specification of the model, split into
pre- and post-GFC periods in Table 28, shows very similar results to the specification
regressed on the ASX 200 returns.
10 August 2016 184
9.3.2 Single Macroeconomic Variable Regressions
As an additional test of robustness, all the significant relationships between
macroeconomic variables and returns/return volatility (found in Chapter 6) were
cross-checked. This was achieved by re-running each ASX 200 returns based
regression on one macroeconomic variable announcement at a time. The results are
classified by macroeconomic variable below.
10 August 2016 185
Unemployment
Table 29 Unemployment Dummy Variable based Regression: Post-GFC
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
tests are two sided based on a null hypothesis of zero
ASX 200 Daily Total Returns
Mean Equation
, , , ,
, ,
, ,
( ) +
Fri TS
t Hol t i Day i t j Control i t
i Mon j US
CSI CSI
k Surprise k Bad News
k Unem k Unem
Surprise Bad News
k t k t
t Hol Day Control
D D
R M a a a
a a
Variable Coefficient p-value
Hola Holiday 0.2422 0.0372**
Daya Monday -0.0423 0.5218
Daya Tuesday -0.0491 0.4356
Daya Thursday -0.0479 0.4861
Daya Friday -0.0368 0.5459
Controla US Returns (Lagged) 0.3710 0.0000***
Controla Oil Returns (Lagged) 0.0338 0.0205**
Controla Term Spread 0.0557 0.0876
Controla Default Spread 0.0023 0.9207
Surprisea Unemployment 0.2883 0.0272**
Bad News Announcements
Bad Newsa Unemployment 0.0405 0.8242
Variance Equation
, ,
2
, ,
, , , ,
( ) ln( )
Fri TS
Hol t i t i t
i Mon j US
t i Day j Control
CSI CSISurprise Bad News
k Surprise k t k Bad News k t
k Unem k Unem
Hol Day ControlV b b b
D Db b
Variable Coefficient p-value
Intercept -0.1452 0.1979
ARCH 0.1406 0.0002***
Asymmetry term -0.1155 0.0000***
GARCH 0.9546 0.0000***
10 August 2016 186
Holb Holiday 0.0749 0.5454
Dayb Monday 0.1843 0.2145
Dayb Tuesday -0.2357 0.1972
Dayb Thursday -0.0748 0.6700
Dayb Friday -0.1175 0.4468
Controlb US Returns (Lagged) -0.0650 0.0008
Controlb Oil Returns (Lagged) 0.0005 0.9666
Controlb Term Spread 0.0255 0.0487**
Controlb Default Spread 0.0213 0.1340
Surpriseb Unemployment 0.0810 0.5482
Bad News Announcements
Bad Newsb Unemployment -0.0295 0.8965
Included observations
1322
Q(20) [p-value]
-0.0280 [0.9950]
Q2(20) [p-value]
0.0150 [0.4620]
The mean equation in Table 29 gives a statistically significant coefficient for good
unemployment news, which has a sign consistent with the results shown in Table 15
in Chapter 6. I therefore consider this finding to be robust.
10 August 2016 187
Retail Sales
Table 30 Retail Sales Dummy Variable based Regression: Pre-GFC
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
tests are two sided based on a null hypothesis of zero
ASX 200 Daily Total Returns
Mean Equation
, , , ,
, ,
, ,
( ) +
Fri TS
t Hol t i Day i t j Control i t
i Mon j US
CSI CSI
k Surprise k Bad News
k Unem k Unem
Surprise Bad News
k t k t
t Hol Day Control
D D
R M a a a
a a
Variable Coefficient p-value
AR(1) -0.1359 0.0005***
Hola Holiday 0.4764 0.0061***
Daya Monday 0.2440 0.0014***
Daya Tuesday 0.0420 0.5250
Daya Thursday 0.2114 0.0077***
Daya Friday 0.0998 0.1148
Controla US Returns (Lagged) 0.4193 0.0000***
Controla Oil Returns (Lagged) 0.0102 0.5805
Controla Term Spread -0.0061 0.9595
Controla Default Spread -0.1016 0.0211**
Surprisea
Retail Sales 0.1502 0.4651
Bad News Announcements
Bad Newsa Retail Sales -0.0036 0.9892
Variance Equation
, ,
2
, ,
, , , ,
( ) ln( )
Fri TS
Hol t i t i t
i Mon j US
t i Day j Control
CSI CSISurprise Bad News
k Surprise k t k Bad News k t
k Unem k Unem
Hol Day ControlV b b b
D Db b
Variable Coefficient p-value
Intercept 0.0356 0.8165
ARCH 0.1176 0.0213**
Asymmetry term -0.1963 0.0000***
GARCH 0.8948 0.0000***
10 August 2016 188
Holb Holiday 0.0196 0.9225
Dayb Monday -0.0487 0.8177
Dayb Tuesday -0.4213 0.1221
Dayb Thursday -0.3791 0.1080
Dayb Friday -0.4319 0.0347**
Controlb US Returns (Lagged) -0.0969 0.0075***
Controlb Oil Returns (Lagged) -0.0246 0.2730
Controlb Term Spread -0.0312 0.6273
Controlb Default Spread 0.0892 0.0034***
Surpriseb Retail Sales 0.1878 0.4370
Bad News Announcements
Bad Newsb Retail Sales -0.8430 0.0038***
Included observations 748
Q(20) [p-value]
0.0190 [0.8000]
Q2(20) [p-value]
-0.0300 [0.8460]
10 August 2016 189
Producer Price Index
Table 31 Producer Price Index Continuous Regression
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
tests are two sided based on a null hypothesis of zero
ASX 200 Daily Total Returns
Mean Equation48
, , , , , ,( ) +
Fri TS CSI
t Hol t i Day i t j Control i t k Surprise k t
i Mon j US k Unem
t Hol Day Control SurpriseR M a a a a
Variable Coefficient p-value
Hola Holiday 0.3416 0.0009***
Daya Monday 0.0850 0.0899
Daya Tuesday 0.0127 0.7738
Daya Thursday 0.0985 0.0446**
Daya Friday 0.0418 0.3317
Controla US Returns (Lagged) 0.3930 0.0000***
Controla Oil Returns (Lagged) 0.0246 0.0433**
Controla Term Spread 0.0329 0.1823
Controla Default Spread -0.0322 0.1248
Surprisea Producer Price Index -0.1318 0.6691
Variance Equation
, , , ,
2
, ,( ) ln( )Fri TS CSI
Hol t i t i t k surprise k t
i Mon j US k Unem
t i Day j ControlHol Day Control SurpriseV b b b b
Variable Coefficient p-value
Intercept -0.0015 0.9868
ARCH 0.1414 0.0000***
Asymmetry term -0.1236 0.0000***
GARCH 0.9650 0.0000***
48 The intercept was removed as the original specification displayed serial correlation in the first lag.
The model excluding the intercept was the second most parsimonious fit after a model including
both an intercept and an autoregressive lag; however, the autoregressive lag was not statistically
significant at the 5 per cent level in this specification.
10 August 2016 190
Holb Holiday 0.0557 0.5533
Dayb Monday 0.1014 0.4301
Dayb Tuesday -0.3113 0.0456**
Dayb Thursday -0.1821 0.2262
Dayb Friday -0.2475 0.0453**
Controlb US Returns (Lagged) -0.0782 0.0000***
Controlb Oil Returns (Lagged) -0.0038 0.6988
Controlb Term Spread 0.0007 0.9098
Controlb Default Spread 0.0038 0.5370
surpriseb Producer Price Index 0.3563 0.0644
Included observations 2070
Q(20) [p-value]
-0.0140 [0.7060]
Q2(20) [p-value]
0.0020 [0.8850]
Table 31 does not report any significant relationships between the PPI and return
volatility. I therefore consider the finding that PPI surprises are associated with
increased volatility (reported in Table 12 in Chapter 6) non-robust.
10 August 2016 191
Consumer Price Index
Table 32 Consumer Price Index Continuous Regression
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
tests are two sided based on a null hypothesis of zero
ASX 200 Daily Total Returns
Mean Equation49
, , , , , ,( ) +
Fri TS CSI
t Hol t i Day i t j Control i t k Surprise k t
i Mon j US k Unem
t Hol Day Control SurpriseR M a a a a
Variable Coefficient p-value
Hola Holiday 0.3406 0.0008***
Daya Monday 0.0871 0.0827
Daya Tuesday 0.0133 0.7630
Daya Thursday 0.1014 0.0392**
Daya Friday 0.0392 0.3630
Controla US Returns (Lagged) 0.3905 0.0000***
Controla Oil Returns (Lagged) 0.0243 0.0468**
Controla Term Spread 0.0338 0.1738
Controla Default Spread -0.0321 0.1288
Surprisea Consumer Price Index
0.6184 0.1131
Variance Equation
, , , ,
2
, ,( ) ln( )Fri TS CSI
Hol t i t i t k surprise k t
i Mon j US k Unem
t i Day j ControlHol Day Control SurpriseV b b b b
Variable Coefficient p-value
Intercept -0.0156 0.8672
ARCH 0.1481 0.0000***
Asymmetry term -0.1254 0.0000***
GARCH 0.9650 0.0000***
49 The intercept was removed as the original specification displayed serial correlation in the first lag.
The model excluding the intercept was the second most parsimonious fit after a model including
both an intercept and an autoregressive lag; however, the autoregressive lag was not statistically
significant at the 5 per cent level in this specification.
10 August 2016 192
Holb Holiday 0.0655 0.4874
Dayb Monday 0.1250 0.3361
Dayb Tuesday -0.3050 0.0525
Dayb Thursday -0.1632 0.2824
Dayb Friday -0.2522 0.0425**
Controlb US Returns (Lagged) -0.0792 0.0000***
Controlb Oil Returns (Lagged) -0.0039 0.6956
Controlb Term Spread 0.0003 0.9577
Controlb Default Spread 0.0053 0.4064
surpriseb Consumer Price Index 0.0069 0.9863
Included observations 2070
Q(20) [p-value]
-0.0140 [0.6940]
Q2(20) [p-value]
0.0040 [0.9090]
The mean equation in Table 32 shows the CPI relationship on returns in the
continuous regression over the full period was insignificant. I therefore consider this
finding in the results section to be non-robust.
10 August 2016 193
Table 33 Consumer Price Index Continuous Regression: Post-GFC
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
tests are two sided based on a null hypothesis of zero
ASX 200 Daily Total Returns
Mean Equation
, , , , , ,( ) +
Fri TS CSI
t Hol t i Day i t j Control i t k Surprise k t
i Mon j US k Unem
t Hol Day Control SurpriseR M a a a a
Variable Coefficient p-value
Hola Holiday 0.2637 0.0227**
Daya Monday -0.0439 0.5021
Daya Tuesday -0.0584 0.3498
Daya Thursday -0.0176 0.7839
Daya Friday -0.0333 0.5823
Controla US Returns (Lagged) 0.3718 0.0000***
Controla Oil Returns (Lagged) 0.0320 0.0289**
Controla Term Spread 0.0573 0.0774
Controla Default Spread 0.0027 0.9085
Surprisea Consumer Price Index 1.6729 0.0003***
Variance Equation
, , , ,
2
, ,( ) ln( )Fri TS CSI
Hol t i t i t k surprise k t
i Mon j US k Unem
t i Day j ControlHol Day Control SurpriseV b b b b
Variable Coefficient p-value
Intercept -0.1787 0.1079
ARCH 0.1469 0.0002***
Asymmetry term -0.1146 0.0000***
GARCH 0.9535 0.0000***
10 August 2016 194
Holb Holiday 0.0775 0.5366
Dayb Monday 0.2066 0.1644
Dayb Tuesday -0.2077 0.2516
Dayb Thursday 0.0004 0.9982
Dayb Friday -0.1004 0.5099
Controlb US Returns (Lagged) -0.0658 0.0010***
Controlb Oil Returns (Lagged) 0.0006 0.9597
Controlb Term Spread 0.0272 0.0437**
Controlb Default Spread 0.0216 0.1363
surpriseb Consumer Price Index 0.0489 0.9336
Included observations 1322
Q(20) [p-value]
-0.0290 [0.9940]
Q2(20) [p-value]
0.0120 [0.4890]
Table 33 indicates good CPI news is positively related to returns (and vice versa),
based on the significance of the coefficients. The sign is also the same as that shown
in the original results (Table 12 in Chapter 6). I therefore consider this result to be
robust.
10 August 2016 195
Table 34 Consumer Price Index Dummy Variable based Regression: Post-
GFC
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
tests are two sided based on a null hypothesis of zero
ASX 200 Daily Total Returns
Mean Equation
, , , ,
, ,
, ,
( ) +
Fri TS
t Hol t i Day i t j Control i t
i Mon j US
CSI CSI
k Surprise k Bad News
k Unem k Unem
Surprise Bad News
k t k t
t Hol Day Control
D D
R M a a a
a a
Variable Coefficient p-value
Hola Holiday 0.2600 0.0216**
Daya Monday -0.0466 0.4802
Daya Tuesday -0.0551 0.3768
Daya Thursday -0.0186 0.7733
Daya Friday -0.0383 0.5313
Controla US Returns (Lagged) 0.3718 0.0000***
Controla Oil Returns (Lagged) 0.0309 0.0346**
Controla Term Spread 0.0571 0.0796
Controla Default Spread 0.0048 0.8376
Surprisea
Consumer Price Index 0.2948 0.2305
Bad News Announcements
Bad Newsa Consumer Price Index -0.9384 0.0031***
Variance Equation
, ,
2
, ,
, , , ,
( ) ln( )
Fri TS
Hol t i t i t
i Mon j US
t i Day j Control
CSI CSISurprise Bad News
k Surprise k t k Bad News k t
k Unem k Unem
Hol Day ControlV b b b
D Db b
Variable Coefficient p-value
Intercept -0.1840 0.1034
ARCH 0.1476 0.0002***
Asymmetry term -0.1124 0.0000***
GARCH 0.9533 0.0000***
10 August 2016 196
Holb Holiday 0.0849 0.4953
Dayb Monday 0.2133 0.1567
Dayb Tuesday -0.1981 0.2779
Dayb Thursday -0.0041 0.9813
Dayb Friday -0.0949 0.5370
Controlb US Returns (Lagged) -0.0643 0.0011
Controlb Oil Returns (Lagged) 0.0014 0.9078
Controlb Term Spread 0.0265 0.0460**
Controlb Default Spread 0.0228 0.1268
Surpriseb Consumer Price Index -0.1338 0.4903
Bad News Announcements
Bad Newsb Consumer Price Index 0.2509 0.3221
Included observations 1322
Q(20) [p-value] -0.0280 [0.9950]
Q2(20) [p-value] 0.0120 [0.5800]
The mean equation in Table 34 indicates bad CPI news decreases returns (and vice
versa) based on the significance of the coefficients. The sign is also the same as that
shown in the original results (Table 15 in Chapter 6). I therefore consider this result
to be robust.
10 August 2016 197
Real GDP
Table 35 Real GDP Dummy Variable based Regression
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
tests are two sided based on a null hypothesis of zero
ASX 200 Daily Total Returns
Mean Equation
, , , ,
, ,
, ,
( ) +
Fri TS
t Hol t i Day i t j Control i t
i Mon j US
CSI CSI
k Surprise k Bad News
k Unem k Unem
Surprise Bad News
k t k t
t Hol Day Control
D D
R M a a a
a a
Variable Coefficient p-value
AR(1) -0.0419 0.0634
Hola Holiday 0.3464 0.0004***
Daya Monday 0.0903 0.0690
Daya Tuesday 0.0208 0.6388
Daya Thursday 0.1102 0.0239**
Daya Friday 0.0458 0.2781
Controla US Returns (Lagged) 0.3946 0.0000***
Controla Oil Returns (Lagged) 0.0249 0.0414**
Controla Term Spread 0.0353 0.1414
Controla Default Spread -0.0331 0.1111
Surprisea Real Gross Domestic Product 0.6341 0.0366**
Bad News Announcements
Bad Newsa Real Gross Domestic Product -0.6932 0.0413**
Variance Equation
, ,
2
, ,
, , , ,
( ) ln( )
Fri TS
Hol t i t i t
i Mon j US
t i Day j Control
CSI CSISurprise Bad News
k Surprise k t k Bad News k t
k Unem k Unem
Hol Day ControlV b b b
D Db b
Variable Coefficient p-value
Intercept 0.0060 0.9513
ARCH 0.1454 0.0000***
Asymmetry term -0.1184 0.0000***
GARCH 0.9659 0.0000***
10 August 2016 198
Holb Holiday 0.0561 0.5725
Dayb Monday 0.1037 0.4403
Dayb Tuesday -0.3393 0.0369**
Dayb Thursday -0.1870 0.2259
Dayb Friday -0.2564 0.0428**
Controlb US Returns (Lagged) -0.0798 0.0000***
Controlb Oil Returns (Lagged) -0.0048 0.6273
Controlb Term Spread -0.0009 0.8804
Controlb Default Spread 0.0060 0.3373
Surpriseb Real Gross Domestic Product -0.6807 0.0011***
Bad News Announcements
Bad Newsb Real Gross Domestic Product 0.6113 0.0164**
Included observations 2070
Q(20) [p-value] -0.1500 [0.8660]
Q2(20) [p-value] 0.0040 [0.8850]
The mean equation in Table 35 reports a statistically significant coefficient for good
real GDP news, which has a sign consistent with the result in shown in Table 13 in
Chapter 6. However, when the All Ordinaries based returns are used as a dependent
variable, there is no significant relationship (see Table 26). I therefore consider this
result to be non-robust.
The variance equation gives statistically significant coefficients for good and bad real
GDP news, which have signs consistent with the result in shown in Table 13 in
Chapter 6. I therefore consider these findings to be robust.
10 August 2016 199
Table 36 Real GDP Dummy based Regression: Pre- and Post-GFC
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
tests are two sided based on a null hypothesis of zero
ASX 200 Daily Total Returns
Mean Equation
, , , ,
, ,
, ,
( ) +
Fri TS
t Hol t i Day i t j Control i t
i Mon j US
CSI CSI
k Surprise k Bad News
k Unem k Unem
Surprise Bad News
k t k t
t Hol Day Control
D D
R M a a a
a a
Variable Coefficient
(pre-GFC) p-value
Coefficient
(post-GFC) p-value
AR(1) -0.1413 0.0003*** 0.2399 0.0382***
Hola Holiday 0.4620 0.0022*** -0.0432 0.5148
Daya Monday 0.2416 0.0017*** -0.0422 0.5043
Daya Tuesday 0.0187 0.7786 -0.0112 0.8631
Daya Thursday 0.2030 0.0079*** -0.0297 0.6291
Daya Friday 0.0750 0.2438 0.3737 0.0000***
Controla US Returns (Lagged) 0.4253 0.0000*** 0.0293 0.0465**
Controla Oil Returns (Lagged) 0.0097 0.5871 0.0547 0.0973
Controla Term Spread -0.0488 0.6988 0.0023 0.9223
Controla Default Spread -0.0873 0.0575 0.2399 0.0382**
Surprisea Real Gross Domestic Product 0.0170 0.9101 1.0164 0.0019***
Bad News Announcements
Bad Newsa Real Gross Domestic Product -0.0516 0.8757 -1.1367 0.0020***
Variance Equation
, ,
2
, ,
, , , ,
( ) ln( )
Fri TS
Hol t i t i t
i Mon j US
t i Day j Control
CSI CSISurprise Bad News
k Surprise k t k Bad News k t
k Unem k Unem
Hol Day ControlV b b b
D Db b
Variable Coefficient
(pre-GFC) p-value
Coefficient
(post-GFC) p-value
Intercept -0.0447 0.7876 -0.1249 0.2700
ARCH 0.1890 0.0031*** 0.1392 0.0001***
Asymmetry term -0.2103 0.0000*** -0.1048 0.0000***
GARCH 0.8597 0.0000*** 0.9592 0.0000***
10 August 2016 200
Holb Holiday 0.0543 0.7999 0.0596 0.6434
Dayb Monday -0.0560 0.7925 0.1765 0.2522
Dayb Tuesday -0.5105 0.0623 -0.2525 0.1788
Dayb Thursday -0.3832 0.0953 -0.0317 0.8568
Dayb Friday -0.4983 0.0116** -0.1265 0.4130
Controlb US Returns (Lagged) -0.1126 0.0026*** -0.0631 0.0009***
Controlb Oil Returns (Lagged) -0.0240 0.3205 0.0007 0.9544
Controlb Term Spread -0.0639 0.4750 0.0219 0.0583
Controlb Default Spread 0.1162 0.0020*** 0.0173 0.1760
Surpriseb Real Gross Domestic Product -1.7141 0.0000*** -0.7410 0.0050***
Bad News Announcements
Bad Newsb Real Gross Domestic Product 2.1116 0.0000*** 0.4484 0.1194
pre-GFC post-GFC
Included observations 748 1322
Q(20) [p-value] 0.0250 [0.7590] -0.0320 [0.9870]
Q2(20) [p-value] -0.0250 [0.8630] 0.0150 [0.3980]
The mean equation in Table 36 yields statistically significant coefficients for good
and bad real GDP news in the post-GFC sub-period, and have signs consistent with
the result in shown in Table 15 in Chapter 6. I therefore consider these findings to be
robust.
The variance equation results report statistically significant coefficients for good and
bad real GDP news in the pre-GFC sub-period and good real GDP news in the post-
GFC sub-period. The signs are consistent with the results in shown in Table 15 in
Chapter 6. I therefore consider these findings to be robust.
10 August 2016 201
Cash Rate
Table 37 Overnight Cash Rate Continuous Regression: Pre-GFC
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
tests are two sided based on a null hypothesis of zero
ASX 200 Daily Total Returns
Mean Equation
, , , , , ,( ) +
Fri TS CSI
t Hol t i Day i t j Control i t k Surprise k t
i Mon j US k Unem
t Hol Day Control SurpriseR M a a a a
Variable Coefficient p-value
AR(1) -0.1334 0.0007***
Hola Holiday 0.5121 0.0034***
Daya Monday 0.2170 0.0050***
Daya Tuesday 0.0366 0.5919
Daya Thursday 0.1868 0.0199**
Daya Friday 0.0846 0.1918
Controla US Returns (Lagged) 0.4226 0.0000***
Controla Oil Returns (Lagged) 0.0147 0.4184
Controla Term Spread 0.0354 0.7679
Controla Default Spread -0.0661 0.1399
Surprisea Overnight Cash Rate 6.7545 0.0103**
Variance Equation
, , , ,
2
, ,( ) ln( )Fri TS CSI
Hol t i t i t k surprise k t
i Mon j US k Unem
t i Day j ControlHol Day Control SurpriseV b b b b
Variable Coefficient p-value
Intercept 0.0303 0.8461
ARCH 0.1095 0.0291**
Asymmetry term -0.1762 0.0000***
GARCH 0.9091 0.0000***
10 August 2016 202
Holb Holiday -0.0079 0.9679
Dayb Monday 0.0203 0.9258
Dayb Tuesday -0.4473 0.1136
Dayb Thursday -0.3071 0.1971
Dayb Friday -0.4174 0.0424**
Controlb US Returns (Lagged) -0.1005 0.0051***
Controlb Oil Returns (Lagged) -0.0293 0.1871
Controlb Term Spread -0.0458 0.4157
Controlb Default Spread 0.0694 0.0115**
surpriseb Overnight Cash Rate 1.5265 0.4168
Included observations 748
Q(20) [p-value] 0.0160 [0.9090]
Q2(20) [p-value] -0.0300 [0.7930]
The result for the mean equation in Table 37, reports a statistically significant
coefficient for good cash rate news in the pre-GFC sub-period and has a sign
consistent with the result in shown in Table 14 in Chapter 6. When the All Ordinaries
based returns are used as the dependent variable, no significant relationship is reported
(see Table 27). I therefore consider this result to be non-robust.
10 August 2016 203
Table 38 Overnight Cash Rate Dummy Variable based Regression: Pre-GFC
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
tests are two sided based on a null hypothesis of zero
ASX 200 Daily Total Returns
Mean Equation
, , , ,
, ,
, ,
( ) +
Fri TS
t Hol t i Day i t j Control i t
i Mon j US
CSI CSI
k Surprise k Bad News
k Unem k Unem
Surprise Bad News
k t k t
t Hol Day Control
D D
R M a a a
a a
Variable Coefficient p-value
AR(1) -0.1387 0.0003***
Hola Holiday 0.5138 0.0029***
Daya Monday 0.2093 0.0066***
Daya Tuesday 0.0251 0.7364
Daya Thursday 0.1838 0.0277**
Daya Friday 0.0909 0.1515
Controla US Returns (Lagged) 0.4267 0.0000***
Controla Oil Returns (Lagged) 0.0153 0.3926
Controla Term Spread 0.0116 0.9245
Controla Default Spread -0.0804 0.0707
Surprisea
Overnight Cash Rate 0.4341 0.0325**
Bad News Announcements
Bad Newsa Overnight Cash Rate -0.4977 0.0209**
Variance Equation
, ,
2
, ,
, , , ,
( ) ln( )
Fri TS
Hol t i t i t
i Mon j US
t i Day j Control
CSI CSISurprise Bad News
k Surprise k t k Bad News k t
k Unem k Unem
Hol Day ControlV b b b
D Db b
Variable Coefficient p-value
Intercept 0.0552 0.7209
ARCH 0.1230 0.0107**
Asymmetry term -0.1821 0.0000***
GARCH 0.9093 0.0000***
10 August 2016 204
Holb Holiday 0.0693 0.7025
Dayb Monday 0.0159 0.9399
Dayb Tuesday -0.4459 0.1050
Dayb Thursday -0.3099 0.2027
Dayb Friday -0.5188 0.0093***
Controlb US Returns (Lagged) -0.0980 0.0040***
Controlb Oil Returns (Lagged) -0.0274 0.2193
Controlb Term Spread -0.0135 0.8214
Controlb Default Spread 0.0750 0.0060***
Surpriseb Overnight Cash Rate -0.0463 0.8796
Bad News Announcements
Bad Newsb Overnight Cash Rate -0.4500 0.1808
Included observations 748
Q(20) [p-value] 0.0130 [0.9390]
Q2(20) [p-value] -0.0360 [0.7250]
The variance equation in Table 38 finds no significant relationship between the cash
rate and return volatility. I therefore consider the finding that bad cash rate
announcements are associated with decreased volatility (reported in Table 15 in
Chapter 6) to be non-robust.
10 August 2016 205
Consumer Sentiment Index
Table 39 Consumer Sentiment Index Continuous Regression
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
tests are two sided based on a null hypothesis of zero
ASX 200 Daily Total Returns
Mean Equation50
, , , , , ,( ) +
Fri TS CSI
t Hol t i Day i t j Control i t k Surprise k t
i Mon j US k Unem
t Hol Day Control SurpriseR M a a a a
Variable Coefficient p-value
Hola Holiday 0.3300 0.0011***
Daya Monday 0.0862 0.0839
Daya Tuesday 0.0180 0.6815
Daya Thursday 0.0995 0.0393**
Daya Friday 0.0428 0.3160
Controla US Returns (Lagged) 0.3882 0.0000***
Controla Oil Returns (Lagged) 0.0254 0.0339**
Controla Term Spread 0.0351 0.1553
Controla Default Spread -0.0311 0.1373
Surprisea Consumer Sentiment Index 0.0125 0.4447
Variance Equation
, , , ,
2
, ,( ) ln( )Fri TS CSI
Hol t i t i t k surprise k t
i Mon j US k Unem
t i Day j ControlHol Day Control SurpriseV b b b b
Variable Coefficient p-value
Intercept -0.0192 0.8362
ARCH 0.1340 0.0000***
Asymmetry term -0.1186 0.0000***
GARCH 0.9666 0.0000***
50 The intercept was removed as the original specification displayed serial correlation in the first lag.
The model excluding the intercept was the second most parsimonious fit after a model including
both an intercept and an autoregressive lag, however, the autoregressive lag was not statistically
significant at the 5 per cent level in this specification.
10 August 2016 206
Holb Holiday 0.0864 0.3391
Dayb Monday 0.1465 0.2546
Dayb Tuesday -0.3156 0.0435**
Dayb Thursday -0.1549 0.2999
Dayb Friday -0.2331 0.0577
Controlb US Returns (Lagged) -0.0802 0.0000***
Controlb Oil Returns (Lagged) -0.0014 0.8769
Controlb Term Spread 0.0009 0.8799
Controlb Default Spread 0.0049 0.4144
surpriseb Consumer Sentiment Index 0.0346 0.0286**
Included observations 2070
Q(20) [p-value] -0.0130 [0.6980]
Q2(20) [p-value] 0.0020 [0.9430]
The variance equation in Table 39 reports that consumer sentiment index surprises
are positively related to return volatility. This is consistent with the finding reported
in Table 12 in Chapter 6 and therefore it appears to be robust.
10 August 2016 207
Table 40 Consumer Sentiment Index Dummy Variable based Regression
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
tests are two sided based on a null hypothesis of zero
ASX 200 Daily Total Returns
Mean Equation
, , , ,
, ,
, ,
( ) +
Fri TS
t Hol t i Day i t j Control i t
i Mon j US
CSI CSI
k Surprise k Bad News
k Unem k Unem
Surprise Bad News
k t k t
t Hol Day Control
D D
R M a a a
a a
Variable Coefficient p-value
Hola Holiday 0.3320 0.0007***
Daya Monday 0.0834 0.1004
Daya Tuesday 0.0168 0.7033
Daya Thursday 0.0967 0.0506
Daya Friday 0.0391 0.3626
Controla US Returns (Lagged) 0.3889 0.0000***
Controla Oil Returns (Lagged) 0.0250 0.0388**
Controla Term Spread 0.0357 0.1472
Controla Default Spread -0.0304 0.1534
Surprisea Consumer Sentiment Index -0.0846 0.4436
Bad News Announcements
Bad Newsa Consumer Sentiment Index 0.1251 0.4021
Variance Equation
, ,
2
, ,
, , , ,
( ) ln( )
Fri TS
Hol t i t i t
i Mon j US
t i Day j Control
CSI CSISurprise Bad News
k Surprise k t k Bad News k t
k Unem k Unem
Hol Day ControlV b b b
D Db b
Variable Coefficient p-value
Intercept -0.0049 0.9586
ARCH 0.1399 0.0000***
Asymmetry term -0.1268 0.0000***
GARCH 0.9691 0.0000***
10 August 2016 208
Holb Holiday 0.0741 0.4105
Dayb Monday 0.1255 0.3416
Dayb Tuesday -0.3139 0.0457**
Dayb Thursday -0.1586 0.2954
Dayb Friday -0.2558 0.0372**
Controlb US Returns (Lagged) -0.0807 0.0000***
Controlb Oil Returns (Lagged) -0.0011 0.9054
Controlb Term Spread 0.0001 0.9812
Controlb Default Spread 0.0039 0.5195
Surpriseb Consumer Sentiment Index 0.1197 0.3031
Bad News Announcements
Bad Newsb Consumer Sentiment Index -0.2489 0.0575
Included observations
2070
Q(20) [p-value] -0.0140 [0.6640]
Q2(20) [p-value] 0.0020 [0.9230]
The variance equation in Table 40 shows no statistically significant relationship
between consumer sentiment index surprises and return volatility. This is not
consistent with the original finding (reported in Table 13 in Chapter 6), that indicates
bad consumer sentiment index surprises decrease return volatility. I therefore consider
the original finding to be non-robust.
10 August 2016 209
Table 41 Consumer Sentiment Index Dummy Variable based Regression:
Post-GFC
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
tests are two sided based on a null hypothesis of zero
ASX 200 Daily Total Returns
Mean Equation
, , , ,
, ,
, ,
( ) +
Fri TS
t Hol t i Day i t j Control i t
i Mon j US
CSI CSI
k Surprise k Bad News
k Unem k Unem
Surprise Bad News
k t k t
t Hol Day Control
D D
R M a a a
a a
Variable Coefficient p-value
Hola Holiday 0.2350 0.0371**
Daya Monday -0.0474 0.4778
Daya Tuesday -0.0455 0.4665
Daya Thursday -0.0217 0.7416
Daya Friday -0.0399 0.5188
Controla US Returns (Lagged) 0.3707 0.0000***
Controla Oil Returns (Lagged) 0.0293 0.0478**
Controla Term Spread 0.0571 0.0822
Controla Default Spread 0.0067 0.7756
Surprisea Consumer Sentiment Index -0.1938 0.2337
Bad News Announcement Days
Bad Newsa Consumer Sentiment Index 0.2741 0.1716
Variance Equation
, ,
2
, ,
, , , ,
( ) ln( )
Fri TS
Hol t i t i t
i Mon j US
t i Day j Control
CSI CSISurprise Bad News
k Surprise k t k Bad News k t
k Unem k Unem
Hol Day ControlV b b b
D Db b
Variable Coefficient p-value
Intercept -0.1269 0.2626
ARCH 0.1335 0.0002***
Asymmetry term -0.1120 0.0000***
GARCH 0.9620 0.0000***
10 August 2016 210
Holb Holiday 0.0930 0.4312
Dayb Monday 0.1889 0.2063
Dayb Tuesday -0.2334 0.1987
Dayb Thursday -0.0081 0.9626
Dayb Friday -0.1294 0.3906
Controlb US Returns (Lagged) -0.0674 0.0003***
Controlb Oil Returns (Lagged) 0.0059 0.5843
Controlb Term Spread 0.0205 0.0680
Controlb Default Spread 0.0126 0.3153
Surpriseb Consumer Sentiment Index 0.1404 0.3498
Bad News Announcements
Bad Newsb Consumer Sentiment Index -0.3200 0.0648
Included observations
1322
Q(20) [p-value] -0.2700 [0.9770]
Q2(20) [p-value] 0.0170 [0.5630]
The variance equation in Table 41 shows no significant relationship exists between
the consumer sentiment index and return volatility. I therefore consider the finding
that bad consumer sentiment index announcements decrease return volatility
(reported in Table 15 in Chapter 6) to be non-robust.
9.3.3 Alternate Break-Point Regressions
To test whether the sub-period regression results were robust to a change in the choice
of break point (10 October 2008), the sample was split into sub-periods using an
alternative dating method. Fry-McKibbin, Hsiao and Tang (2014, p.525) observed
there is seldom consensus on the dating of a particular crisis, and they reviewed a
number of studies that had attempted to date the Global Financial Crisis (GFC). Their
study indicated the GFC was often viewed as two periods: the sub-prime crisis and
10 August 2016 211
the great recession. Based on their analysis, they dated the start of the sub-prime crisis
to 26 July 2007 and the end of the great recession as 31 December 2009. I have used
these two dates to define the GFC as the combination of both the sub-prime crisis and
great recession, resulting in three sub-periods: the pre-GFC period from 26 October
2005 to 25 July 2007, the GFC from 26 July 2007 to 31 December 2009, and the post-
GFC period from 1 January 2010 to 31 December 2013. For the sake of brevity, the
tables are not presented here, but can be produced upon request. All results withstood
the robustness test, with the exception of the pre-GFC retail sales result.
Differing results are highlighted in the output tables in the following way:
Results that were significant in the original regression, but are not significant
here, are emboldened. These results are not considered robust to choice of
break point.
Results that become significant here, but were not significant in the original
regression, are italicised.
Table 42 Continuous Model Results using Alternate Breakpoints: Pre- and
Post-GFC
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
tests are two sided based on a null hypothesis of zero
ASX 200 Daily Total Returns
Mean Equation51
, , , , , ,( ) +
Fri TS CSI
t Hol t i Day i t j Control i t k Surprise k t
i Mon j US k Unem
t Hol Day Control SurpriseR M a a a a
Variable Coefficient
(pre-GFC) p-value
Coefficient
(post-GFC) p-value
AR(1) -0.2666 0.0000*** - -
MA(3) 0.1488 0.0001*** - -
Hola Holiday 0.5337 0.0000*** 0.2164 0.0402**
51 The original specification that only had an AR(1) coefficient exhibited serial correlation in the
standardised residuals at various lags. The specification including an MA(3) term was the most
parsimonious fit controlling for this serial correlation.
10 August 2016 212
Daya Monday 0.2035 0.0083*** -0.0149 0.8308
Daya Tuesday -0.0351 0.6991 -0.0520 0.4432
Daya Thursday 0.2429 0.0091*** -0.0274 0.6881
Daya Friday 0.0720 0.3305 -0.0085 0.8981
Controla US Returns (Lagged) 0.4154 0.0000*** 0.3902 0.0000***
Controla Oil Returns (Lagged) 0.0218 0.1401 0.0324 0.0686
Controla Term Spread 0.0260 0.8056 0.0045 0.9183
Controla Default Spread -0.0384 0.7226 0.0114 0.6614
Surprisea Unemployment -2.3885 0.0210** 0.3331 0.3112
Surprisea Balance of Trade 0.0007 0.5020 0.0000 0.8119
Surprisea Retail Sales 0.0256 0.9125 0.1059 0.4404
Surprisea Producer Price Index 0.1888 0.6800 0.0201 0.9668
Surprisea Consumer Price Index -0.5781 0.3821 1.8411 0.0059***
Surprisea Real Gross Domestic Product 0.1904 0.4293 -0.3012 0.4315
Surprisea Overnight Cash Rate 5.9780 0.0374** 1.5050 0.2841
Surprisea Consumer Sentiment Index -0.0075 0.6770 0.0255 0.2744
Variance Equation
, , , ,
2
, ,( ) ln( )Fri TS CSI
Hol t i t i t k surprise k t
i Mon j US k Unem
t i Day j ControlHol Day Control SurpriseV b b b b
Variable Coefficient
(pre-GFC) p-value
Coefficient
(post-GFC) p-value
Intercept 0.6243 0.0003*** -0.0086 0.9573
ARCH (1) term -0.2252 0.0029*** 0.1185 0.0024***
Asymmetry term -0.1913 0.0000*** -0.1263 0.0000***
GARCH (1) term 0.9106 0.0000*** 0.9515 0.0000***
Holb Holiday -0.0861 0.5948 -0.0261 0.8726
Dayb Monday 0.1557 0.5711 0.1215 0.5519
Dayb Tuesday -0.2258 0.4021 -0.2933 0.2256
Dayb Thursday -0.3397 0.1877 -0.0844 0.7392
Dayb Friday -0.5642 0.0188** -0.1502 0.4685
Controlb US Returns (Lagged) -0.2123 0.0000*** -0.0829 0.0078***
10 August 2016 213
Controlb Oil Returns (Lagged) -0.0551 0.0049** 0.0031 0.8771
Controlb Term Spread -0.0790 0.1104 -0.0060 0.7378
Controlb Default Spread -0.5509 0.0029*** -0.0073 0.6820
surpriseb Unemployment 4.8613 0.0000*** -0.4844 0.3235
surpriseb Balance of Trade -0.0082 0.0034*** -0.0002 0.4704
surpriseb Retail Sales 0.3039 0.4211 -0.1797 0.5292
surpriseb Producer Price Index 0.0301 0.9634 0.0972 0.8687
surpriseb Consumer Price Index -0.0939 0.8780 -0.8259 0.3871
surpriseb Real Gross Domestic Product -2.0001 0.0025*** -0.5387 0.1284
surpriseb Overnight Cash Rate 2.2260 0.2945 0.0736 0.9669
surpriseb Consumer Sentiment Index 0.0324 0.2620 0.0226 0.4615
pre-GFC post-GFC
Included observations 440 1011
Adjusted R-squared 0.2748 0.2514
Log likelihood -405.8986 -1120.4040
Akaike Information criterion 2.0268 2.2916
Diagnostics
pre-GFC post-GFC
Q(20) [p-value] 23.1380 [0.1850] 15.2000 [0.7650]
Q2(20) [p-value] 19.4070 [0.3670] 17.0780 [0.6480]
ARCH LM Test F-Statistic [p-value] 1.1652 [0.2812] 0.7823 [0.7372]
10 August 2016 214
Table 43 Dummy Variable Model Results using Alternate Breakpoints: Pre-
and Post-GFC
* 5 per cent level of significance
** 1 per cent level of significance
*** 0.1 per cent level of significance
tests are two sided based on a null hypothesis of zero
ASX 200 Daily Total Returns
Mean Equation
, , , ,
, ,
, ,
( ) +
Fri TS
t Hol t i Day i t j Control i t
i Mon j US
CSI CSI
k Surprise k Bad News
k Unem k Unem
Surprise Bad News
k t k t
t Hol Day Control
D D
R M a a a
a a
Variable Coefficient
(pre-GFC) p-value
Coefficient
(post-GFC) p-value
AR (1) -0.3076 0.0000*** - -
MA(3) 0.1322 0.0002*** - -
Hola Holiday 0.5317 0.0000*** 0.2154 0.0530
Daya Monday 0.2032 0.0088*** -0.0067 0.9256
Daya Tuesday -0.0050 0.9559 -0.0275 0.6919
Daya Thursday 0.3569 0.0003*** -0.0492 0.4928
Daya Friday 0.0560 0.4705 -0.0079 0.9042
Controla US Returns (Lagged) 0.4079 0.0000*** 0.3925 0.0000***
Controla Oil Returns (Lagged) 0.0099 0.4760 0.0359 0.0316**
Controla Term Spread 0.0152 0.8796 0.0131 0.7333
Controla Default Spread -0.1026 0.3630 0.0021 0.9335
Surprisea Unemployment -0.3700 0.0244** 0.2868 0.0364**
Surprisea Balance of Trade 0.1401 0.5532 0.0708 0.5937
Surprisea Retail Sales -0.0996 0.5499 -0.0558 0.6967
Surprisea Producer Price Index 0.5483 0.1314 0.1401 0.4955
Surprisea Consumer Price Index 0.0223 0.9379 0.4882 0.0146**
Surprisea Real Gross Domestic Product -0.1094 0.5598 1.0904 0.0009***
Surprisea Overnight Cash Rate 0.3299 0.0109** 0.0043 0.9880
Surprisea Consumer Sentiment Index 0.0277 0.7795 -0.1276 0.4551
Bad News Announcements
10 August 2016 215
Bad Newsa Unemployment 0.3653 0.1002 0.0548 0.7974
Bad Newsa Balance of Trade -0.0588 0.8208 -0.2368 0.1797
Bad Newsa Retail Sales 0.1015 0.6496 0.0579 0.7587
Bad Newsa Producer Price Index -0.4733 0.2104 -0.5432 0.1414
Bad Newsa Consumer Price Index 0.0343 0.9216 -1.0381 0.0000***
Bad Newsa Real Gross Domestic Product -0.2001 0.4722 -1.2222 0.0009***
Bad Newsa Overnight Cash Rate -0.3498 0.0140** 0.0255 0.9316
Bad Newsa Consumer Sentiment Index -0.1597 0.3145 0.2562 0.2177
Variance Equation
, ,
2
, ,
, , , ,
( ) ln( )
Fri TS
Hol t i t i t
i Mon j US
t i Day j Control
CSI CSISurprise Bad News
k Surprise k t k Bad News k t
k Unem k Unem
Hol Day ControlV b b b
D Db b
Variable Coefficient
(pre-GFC) p-value
Coefficient
(post-GFC) p-value
Intercept 0.8000 0.0004*** 0.0062 0.9594
ARCH -0.2634 0.0038*** 0.1105 0.0046***
Asymmetry term -0.2643 0.0000*** -0.1370 0.0000***
GARCH 0.8961 0.0000*** 0.9582 0.0000***
Holb Holiday 0.0182 0.9318 -0.0149 0.9219
Dayb Monday -0.0788 0.7598 0.1200 0.4354
Dayb Tuesday -0.1087 0.7288 -0.3456 0.0793
Dayb Thursday -0.1930 0.5242 -0.0828 0.6793
Dayb Friday -0.7236 0.0043*** -0.1280 0.4455
Controlb US Returns (Lagged) -0.2525 0.0000*** -0.0757 0.0069***
Controlb Oil Returns (Lagged) -0.0390 0.0269** 0.0036 0.8258
Controlb Term Spread -0.1009 0.0814 -0.0064 0.7152
Controlb Default Spread -0.7152 0.0113** -0.0078 0.6229
Surpriseb Unemployment 0.8113 0.0020*** 0.1638 0.3010
Surpriseb Balance of Trade 0.5747 0.0442** -0.1787 0.3247
Surpriseb Retail Sales 0.1989 0.4441 -0.1808 0.3711
Surpriseb Producer Price Index 0.6882 0.0635 0.0526 0.8295
10 August 2016 216
Surpriseb Consumer Price Index -0.8624 0.0757 -0.3846 0.0866
Surpriseb Real Gross Domestic Product -1.2677 0.0016*** -0.6218 0.0166**
Surpriseb Overnight Cash Rate -0.7870 0.0036*** 0.2648 0.2402
Surpriseb Consumer Sentiment Index -0.7413 0.0037*** 0.1637 0.3828
Bad News Announcements
Bad Newsb Unemployment -0.3392 0.4098 -0.3884 0.0952
Bad Newsb Balance of Trade -0.7726 0.0065*** -0.0400 0.8389
Bad Newsb Retail Sales -0.2935 0.2477 0.1276 0.5613
Bad Newsb Producer Price Index -2.0116 0.0003*** 0.1357 0.6658
Bad Newsb Consumer Price Index 1.1594 0.0314** 0.5271 0.1402
Bad Newsb Real Gross Domestic Product 0.9040 0.0614 0.0783 0.7961
Bad Newsb Overnight Cash Rate -0.0953 0.7960 0.0437 0.8462
Bad Newsb Consumer Sentiment Index 0.2785 0.3303 -0.3656 0.0532
pre-GFC post-GFC
Included observations 440 1011
Adjusted R-squared 0.2583 0.2589
Log likelihood 0.2583 -1110.2750
Akaike Information criterion 2.0587 2.3032
Diagnostics
pre-GFC post-GFC
Q(20) [p-value] 20.9600 [0.2810] 14.6550 [0.7960]
Q2(20) [p-value] 21.2790 [0.2660] 25.5900 [0.1800]
ARCH LM Test F-Statistic [p-value] 1.5415 [0.0642] 1.0825 [0.3621]
