Download stock prices and macroeconomic variables in vietnam: an empirical

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UNIVERSITY OF ECONOMICS
INSTITUTE OF SOCIAL STUDIES
HO CHI MINH CITY
THE HAGUE
VIETNAM
NETHERLANDS
======o0o======
VIETNAM – NETHERLANDS
PROJECT FOR M.A IN DEVELOPING ECONOMICS
STOCK PRICES AND
MACROECONOMIC VARIABLES IN
VIETNAM: AN EMPIRICAL ANALYSIS
BY
NGUYEN THI BAO KHUYEN
MASTER OF ARTS IN DEVELOPMENT ECONOMICS
HO CHI MINH CITY, 2010
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UNIVERSITY OF ECONOMICS
INSTITUTE OF SOCIAL STUDIES
HO CHI MINH CITY
THE HAGUE
VIETNAM
NETHERLANDS
======o0o======
VIETNAM – NETHERLANDS
PROJECT FOR M.A IN DEVELOPING ECONOMICS
STOCK PRICES AND
MACROECONOMIC VARIABLES IN
VIETNAM: AN EMPIRICAL ANALYSIS
A THESIS PRESENTED BY
NGUYEN THI BAO KHUYEN
IN PARTIAL FULFILMENT OF THE REQUIREMENT FOR THE
DEGREE OF MASTER OF ARTS IN DEVELOPMENT ECONOMICS
SUPERVISOR
Prof. Dr. NGUYEN TRONG HOAI
HO CHI MINH CITY, 2010
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DECLARATION
I certify that this thesis has been written by me, that it is the record of work
carried out by me and that it has not been submitted elsewhere.
HCMC, January 10, 2010
NGUYEN THI BAO KHUYEN
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ACKNOWLEDGEMENTS
IT IS A TREAT to have an opportunity to formally express my appreciation to
people who have really created the concepts and methodology expressed in this
research.
I own the greatest debt to professors of MDE Programme, executive programme
administrations in Vietnam and colleagues. Their enthusiasm about the
experience and what they were teaching, was an important motivator for me.
I would like to express my deep and sincere gratitude to my supervisor,
Professor, Doctor Nguyen Trong Hoai, Dean of the Faculty of Development
Economics, University of Economics, Ho Chi Minh City. His wide knowledge
and his logical way of thinking have been of great value for me. His
understanding, encouraging and personal guidance have provided a good basis
for the present thesis.
I especially appreciate the VietFund Management colleagues, who have been
eager supporters of the research.
NGUYEN THI BAO KHUYEN
March 2010
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ABSTRACT
The article employs the cointegration and error correction version of Granger causality
tests to investigate whether the Vietnamese stock market exhibits the publicly
informational efficiency. The test results strongly suggest informational inefficiency in
the Vietnamese stock market. Specifically, the results from bivariate analysis suggest
that the Vietnamese stock market is not informationally efficient in both short- and
long-run. In addition, the stock market seems to even divorce from the most part of the
economy. Therefore, it is still possible for a “professional” trader to make abnormal
returns by analyzing good or bad news contained in some macroeconomic variables.
The findings re-assure that the Vietnamese stock market is not well functioning in
scarce resource allocation and not attractive enough to encourage foreign investors.
Since the market is not informationally efficient, especially with respect to monetary
variables, it may be dangerous for policy makers to realize the role of monetary
policies, the so-called demand stimulus packages. In terms of the investors‟ point of
view, fundamental analysis is still significant for their investment decisions. Thus,
companies with strong equity analysts would have higher comparative advantages in
this inefficient market. Furthermore, instead of becoming more efficient over time, as
one might expect, the Vietnamese stock market appears to have become increasingly
divorced from reality. This also reveals that the last financial crisis has serious impact
on the Vietnamese stock market.
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TABLE OF CONTENTS
CHAPTER 1..................................................................................................................... 1
INTRODUCTION ........................................................................................................... 1
1.1 RESEARCH CONTEXT ......................................................................................... 1
1.2 THE PROBLEM STATEMENT ............................................................................. 2
1.3 THE RESEARCH OBJECTIVES ........................................................................... 3
1.4 RESEARCH QUESTIONS AND HYPOTHESES ................................................. 4
1.5 RESEARCH METHODOLOGY ............................................................................ 5
1.6 STRUCTURE OF THE THESIS ............................................................................. 5
CHAPTER 2..................................................................................................................... 6
LITERATURE REVIEW ............................................................................................... 6
2.1 THE CONCEPT OF EFFICIENT MARKET .......................................................... 6
2.2 FORMS OF MARKET EFFICIENCY .................................................................... 7
2.3 MARKET EFFICIENCY AND VALUATION .................................................... 12
2.4 THE DETERMINANTS OF STOCK PRICES ..................................................... 13
2.4.1 The Determinants of True Value .................................................................... 13
2.4.2 The Determinants of Stock Price ................................................................... 17
2.5 EMPIRICAL STUDIES ........................................................................................ 20
CHAPTER 3................................................................................................................... 24
RESEARCH METHODOLOGY ................................................................................. 24
3.1 STATIONARITY AND UNIT-ROOT TESTS ..................................................... 24
3.2 VECTOR AUTOGRESSIVE MODELS AND CAUSALITY TESTS ................. 25
3.2.1 VAR Models ................................................................................................... 25
3.2.2 Granger Causality Tests ................................................................................ 26
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3.3 SEMI-STRONG FORM EFFICIENCY TESTS ................................................... 28
3.4 ERROR CORRECTION MODELS ...................................................................... 29
3.4.1 Cointegration ................................................................................................. 30
3.4.2 Error Correction Mechanism ........................................................................ 30
3.4.3 Testing for Cointegration and ECM .............................................................. 32
3.4.4 ECM and Long-Run Efficiency ...................................................................... 33
3.5 DATA COLLECTION AND ANALYSIS ............................................................ 35
CHAPTER 4................................................................................................................... 38
RESEARCH RESULTS ................................................................................................ 38
4.1 DESCRIPTIVE STATISTICS .............................................................................. 38
4.2 BIVARIATE CAUSALITY TESTS ..................................................................... 42
4.3 MULTIVARIATE GRANGER TESTS ................................................................ 48
CHAPTER 5................................................................................................................... 54
CONCLUSION AND POLICY RECOMMENDATIONS ........................................ 54
5.1 MAIN FINDINGS ................................................................................................. 54
5.2 POLICY IMPLICATIONS.................................................................................... 55
5.3 FURTHER STUDIES............................................................................................ 57
REFERENCES ............................................................................................................ 58
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CHAPTER 1
INTRODUCTION
This chapter will explain why the efficient market hypothesis is worth
investigating in the case of the Vietnamese stock market. In particular, this
chapter is divided into six sections. The first section will provide evidence that
tells us why information becomes an issue of concern for the most part of market
participants and policy makers as well. From this background information, the
second section will raise the problem necessary to make clear for the case of
Vietnam. The third section will set four main objectives the thesis expects to
obtain in order to solve the research problem. To obtain the proposed objectives,
the fourth section will raise questions and corresponding hypotheses which direct
the whole thesis to a systematic way. The fifth section will briefly tell us how the
research will be done in terms of analytical models, data collection, and data
analysis. The final section will describe structure of the thesis.
1.1 RESEARCH CONTEXT
Efficient market hypothesis (EMH) has been at the center of debates in financial
literature for several years. The term efficiency is used to describe a market in
which all relevant information is impounded into the price of financial assets. If
the capital market is sufficiently efficient, investors cannot expect to achieve
superior profits from their investment strategies. As a result, Capital Asset
Pricing models can be useful for various investment decisions. In the economic
perspective, the efficient market is even more important because it implies that
the stock market is well functioning in scarce resource allocation. However, this
is not always the case, especially in the emerging stock markets.
The last decades have witnessed spectacular growth in both size and relative
importance of the stock markets in developing countries. High economic growth,
the pursuit of liberalization policies, and trends towards financial market
globalization provided the environment in which stock markets could thrive. In
addition, foreign equity managers were attracted to these markets by the
potentially high rates of return offered and the desire to pursue international
diversification. According to Antoniou and Ergul (1997), as these capital markets
have developed, considerable attention has been given to the question of whether
they function efficiently. But why the efficiently functioning stock market
becomes so important that every developing country does its best to direct
toward.
Islam and Khaled (2005) calls developing countries as „capital starved
economies‟, so efficient allocation of scarce resources and encouragement of
private foreign investment are both of vital importance. They also stated that the
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success of an increasing privatization of these economies will depend crucially
on the presence of an active and efficient stock market. Indeed, rational investors
expectedly drive their investments into the most profitable projects, given
acceptable risks. The efficient market can address the „mixed feelings‟ problem,
which investors are always skeptical about the intrinsic value of any stock under
consideration. This may lead their decisions based on others. In other words, this
phenomenon is commonly considered as herding behavior. For foreign investors,
inefficient markets are usually equivalent to high risky markets when making
their investments abroad. Hence, they tend to apply higher hurdle rates, which in
turn underestimate investment opportunities in developing countries. Eventually,
it‟s hard for any developing country with inefficient/weak stock market to attract
foreign portfolio investment flows. In recent years, the intensity of foreign direct
investment competition among developing countries becomes fiercer, so foreign
indirect investment may become a feasible alternative for economic development.
For above reasons, the questions of whether the markets price securities
efficiently and what makes markets informationally efficient or inefficient turn
out to be ultimately empirical issues. An understanding of these issues will help
to determine the appropriate regulatory framework for the establishment of the
efficiently functioning stock market. This appears to be the case of Vietnam.
1.2 THE PROBLEM STATEMENT
Since its foundation in July 2000, the Vietnamese stock market has dramatically
expanded and become one of the most important sources of capital mobilization.
Up to June 2009, the Vietnamese stock market has 352 listed companies and a
market capitalization of about US$17.5 Billion, approximately 21.3 percent of
Vietnam GDP, which even reached above 45 percent before the financial crisis
(Thomson Reuters).
Despite its impressive growth, the Vietnamese stock market is really
struggling with various typical weaknesses of an emerging market (Truong,
2006). First, it is not fully characterized by the depth and maturity of a stock
exchange observed in a developed country. The legal framework is weak and few
alternatives are available for investors. Interest rates are strictly controlled by the
State Bank. The government deeply intervenes into stock trading transactions.
Accordingly, investors tend to speculate, and thus cause high market volatility.
Second, it is widely known that one of the biggest problems facing traders is lack
of transparency. Reporting requirements for listed companies are not well
defined, and significantly less comprehensive than those in the developed stock
markets. Third, publicly information disclosure is not only unclear but also
unreliable. As a result, trading behavior in the Vietnamese stock market may be
much different from that in developed/newly emerging stock markets. Investors
may base their actions on the decisions of others who are well informed about
market developments, by following the market consensus. In other words, the
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herding behavior may exist in the Vietnamese stock market 1. Thus, the question
is whether the Vietnamese stock market is informationally inefficient?
Interestingly, the mixed evidence from the study of Truong (2006) in the
2002-2004 period conclude that the Vietnamese stock market is, to some extent,
characterized by the weak-form efficiency in which current prices fully reflect all
information contained in past prices. This implies that, for some stocks, it is not
possible for a trader to make abnormal returns by using only the past history of
prices. However, this lowest form of efficiency cannot assure the Vietnamese
stock market is well functioning in scarce resource allocation and attractive
enough to encourage foreign investors. Both investors and policy makers mostly
concern if the current market prices reflect all publicly available information,
such as information on inflation, economic growth, money supply, exchange
rates, interest rates, dividend payments, annual earnings, stock splits, etc.
Therefore, this thesis tries to investigate whether or not it is possible for market
participants to make consistently superior returns just by analyzing good or bad
news contained in annual reports or other published information. In other words,
the focus of this thesis is to find out the relationship between stock prices and
macroeconomic variables in Vietnam.
1.3 THE RESEARCH OBJECTIVES
This thesis attempts to apply the most widely accepted analytical approach in the
studying of stock markets, namely, the efficient market hypothesis, to investigate
the behavior of the Vietnamese stock prices. Particularly, the goal of this thesis is
to test whether the possibility that the Vietnamese stock market exhibits the semistrong form efficiency such that no relationship exists between lagged values of
changes in macroeconomic variables and changes in stock prices over the
December 2000 to June 2009 period. In order to meet this above goal, this thesis
aims to obtain the following specific objectives:
(1) Investigate whether the Vietnamese stock market exhibits the semi-strong
form efficiency;
(2) Examine the speed of adjustment to long-run equilibrium takes place in
the Vietnamese stock market;
(3) Evaluate the possible feedback from the stock prices to the
macroeconomic variables;
(4) Investigate if the financial crisis has any impact on the pattern of
efficiency in the Vietnamese stock market;
(5) Suggest some policy implications for improving the stock market
efficiency.
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This was already tested by Nguyen (2009).
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1.4 RESEARCH QUESTIONS AND HYPOTHESES
In order to obtain the above objectives, this thesis will attempt to answer the
following questions:
(1) Do “past” changes in macroeconomic variables explain “current” changes
in stock prices?
(2) Is it correct to say that the Vietnamese stock market takes times to fully
adjust previous shocks in the economy?
(3) Could the stock market movements be used as a leading indicator in
helping formulating current economic stabilization policies in Vietnam?
(4) Does the financial crisis make the inefficiency of the Vietnamese stock
market worse?
Some hypotheses that can be stated regarding the literature review in Chapter 2
and analytical framework in Chapter 3 are:
(1) This thesis hypothesizes that the semi-strong efficiency would not exist in
the Vietnamese stock market. According to Ross et al. (2006), to be semistrongly efficient, an investor must be skilled at economics and statistics,
and steeped in the idiosyncrasies of individual industries and companies.
However, to acquire and use such skills requires talent, ability, and time.
And in Vietnam, it is still lack of professional institutions so this is rarely
realistic. In addition, based on previous studies (Ibrahim, 1999; Hanousek
and Filer, 2000; Rousseau and Wachtel, 2000; Wongbangpo and Sharma,
2002; Islam and Khaled, 2005; Atmadja, 2005), most emerging stock
markets, to which extent, remain Granger causality relationships from the
macroeconomic variables to stock prices, and thus they are widely
considered to be informationally inefficient.
(2) This thesis hypothesizes that the Vietnamese stock market could take a
long time to adjust itself toward good or bad news in the economy. This
expectation is expectedly suitable for three reasons. First, the publicly
information disclosure is unclear and unreliable. Second, the ability of
investors in analyzing and understanding macroeconomic and industrial
news is still limited. Third, macroeconomic/market and industrial analyses
are mainly considered by equity analysts of professional investment
companies. Therefore, these concepts are still far away from individual
investors. In addition, based on previous studies (Habibullah and
Baharumshah, 1996; Ibrahim, 1999), the adjustment toward the long-run
relationship in developing markets is extremely slow.
(3) This thesis hypothesizes that the Vietnamese stock market, to which
extent, could play as a leading indicator of the economy as a whole. The
Vietnamese stock prices may provide predictive power for economic
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variables because we believe that stock prices contain the market
participants‟ expectations of future real activities (Ibrahim, 1999).
(4) This thesis hypothesizes that the financial crisis does really have
significant impact on the Vietnamese stock market. This hypothesis is
based on the evidence from ASEAN countries after 1997 Asian financial
crisis (Atmadja, 2005) and Central Europe countries after the second wave
of voucher privatization (Hanousek and Filer, 2000).
[[
1.5 RESEARCH METHODOLOGY
The Granger causality and error correction mechanism tests are employed to test
the proposed hypotheses. Theoretically, these tests are only appropriate when the
variables being analyzed, including stock index and macroeconomic factors, are
stationary and co-integrated. Therefore, it becomes necessary to conduct various
priori tests of integration and cointegration. In so doing, this thesis will apply the
unit root test (specifically the augmented Dickey-Fuller tests and the PhillipsPerron tests). Following previous studies, this thesis will employ the Akaike
Information Criterion (AIC) and Schwarz Information Criterion (SIC) to
determine the optimal lag lengths.
Data used in Granger causality and error correction mechanism models are
collected from three official sources, namely, Thomson Reuters, Bloomberg and
International Monetary Fund during the December 2000 to June 2009 period.
Similar to most previous studies in emerging markets, this thesis will use the
monthly data because the Vietnamese stock market is not long enough to apply
quarterly data as other developed markets. For this reason, some fundamental
variables presented the whole economy performance such as GDP will be chosen
in a different way. The choice of proxy variables will be discussed in Chapter 3.
For time series variables are usually non-stationary, most previous studies
used the first-order difference form. And this is also the case in this thesis. As
taking differences, the transformed data might take both positive and negative
values, so it sometimes limits the possibility of applying the logarithmic
functional forms in regression analysis.
1.6 STRUCTURE OF THE THESIS
After this chapter the rest of this thesis will be presented four other chapters.
Chapter 2 reviews the literature about the market efficient hypothesis and stock
price determination, including empirical studies. Chapter 3 presents the research
methodology, where details about variables of interest, econometric models,
hypothesis formulating, and data collection. Chapter 4 presents the research
results, indicating whether the Vietnamese stock market exhibits semi-strong
form efficiency. Chapter 5 ends the thesis with a conclusion, policy implications
and limitations for further studies.
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CHAPTER 2
LITERATURE REVIEW
This chapter will provide a conceptual framework of market efficient hypothesis,
of which we will examine why market efficiency is significant and what are key
determinants of current stock prices. An overview of literature about
informational inefficiency testing helps make clear the research questions and
provide basis for an analytical framework development (research methodology)
discussed in Chapter 3. To achieve these aims, this chapter will be divided into
four sections. The first section will briefly define the concept of efficient market.
To its end, we will understand the essence of market efficiency in stock
valuation, investment, and policy-making process. The second section will
summarize three basic forms of market efficiency so as to help us narrow down
the topic of interest. The third section will justify the determinants of stock
prices, which provide us the underlying foundation for developing the conceptual
model of this thesis. The final section will review a set of previous studies about
market efficiency, especially those in emerging markets in order to logically
drive this thesis into a correct direction.
2.1 THE CONCEPT OF EFFICIENT MARKET
Investors determine stock prices on the basis of the expected cash flows to be
received from a stock and the risk involved. Rational investors should use all the
information they have available or can reasonably obtain. The information set
consists of both known information and beliefs about the future (Jones, 1998).
Regardless of its form, information is the key to the determination of stock prices
and therefore is the central issue of the efficient market concepts.
According to Clarke et al. (2001), the term "efficient market" was first used in
1965 by Fama. In an efficient market, competition forces will cause the full
effects of new information on intrinsic values to be reflected "instantaneously" in
actual prices. The efficient market hypothesis implies that investors are unlikely
to earn abnormal profits by simply predicting the price movements. Indeed, the
underlying engine of price changes only depends on new information. A market
is said to be efficient if prices are quickly adjusted to new information. In this
case, investors will compete to each others in making use of any new information
for profitable opportunities. Interestingly, the higher competition among investors
exists in effort to searching for over- or under-valued securities, the lower
probability of finding and exploiting such mis-priced securities becomes. For the
majority of investors, the gain derived from information analysis may not be in
excess of the transaction costs. Consequently, there is no reason to expect that
market prices are too high or too low. Security prices adjust before an investor
has time to trade on and profit from a new piece of information.
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Generally, an efficient market is one in which the prices of all securities
quickly and fully reflect all available information about the assets. This concept
postulates that investors will assimilate all relevant information into prices in
making their buy and sell decisions (Jones, 1998). Therefore, the current price of
a stock reflects:
1. All known information, including:
 Past information (e.g., last year‟s or last quarters‟ earnings)
 Current information as well as events that have been announced but are
still forthcoming (such as a stock split)
2. Information that can reasonably be inferred; for example, if many
investors believe that interest rates will decline soon, prices will reflect
this belief before the actual decline occurs.
Jones (1998) also states that an efficient market can exist if the following events
occur:
1. A large number of rational, profit-maximizing investors exist who actively
participate in the market by analyzing, valuing, and trading stocks. These
investors are price takers2; that is, one participant alone cannot affect the
price of a security.
2. Information is costless and widely available to market participants at
approximately the same time.
3. Information is generated in a random fashion such that announcements are
basically independent of one another. This is still a question under the
Vietnamese stock market circumstances because information disclosure is
unclear and unreliable.
4. Investors react quickly and fully to the new information, causing stock
prices to adjust accordingly. This is not always a case in the Vietnamese
stock market, because investors are not equally informed.
2.2 FORMS OF MARKET EFFICIENCY
We have defined an efficient market as one in which all information is reflected
in stock prices quickly and fully. The key to assessing market efficiency is
information. According to Jones (1998), in a perfectly efficient market, security
prices always reflect immediately all available information, and investors are not
2
This is similar to the concept of perfect competitive market in microeconomics.
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able to use available information to earn abnormal returns because it already
impounded in prices. In such a market, every security‟s price is equal to its
intrinsic value, which reflects all information about that security‟s prospects. If
some types of information are not fully reflected in prices or lags exist in the
impoundment of information into prices, the market is less than perfectly
efficient. In fact, the market is not perfectly efficient, and it is certainly not
perfectly inefficient, so it is a question of degree.
In financial theory, the efficient market hypothesis is viewed in three common
forms, depending on the kind of available information embodied (Figure 2.1).
These are commonly classified into weak-form, semi strong-form, and strongform efficiency.
Weak Form Efficiency
The weak form is the lowest form of efficiency that defines a market as being
efficient if current prices fully reflect all information contained in past prices
only. That is, nobody can detect mis-priced securities and beat the market by
analyzing the historical prices. This form implies that one should not be able to
profit from using something that “everybody else knows”. For this reason, any
attempt to generating profits by studying the past history of price information
(using technical analysis) is in vain.
Based on the above definition, Fama (1970) suggests three models for testing
stock market efficiency, namely, Fair Game Model, Submartingale Model, and
Random Walk Model. According to Ross et al. (2006), among these models, the
Random Walk is the most powerful model widely used. This model assumes
price changes only depend on new information. Since information itself arrives
randomly, so prices will fluctuate unpredictably.
Pt = Pt-1 + Expected return + Random errort
(2.1)
where
Pt: Stock price at time t
Pt-1: Stock price at time t-1
The expected return is a function of a security‟s risk and would be based on the
model of risk and return (Capital Asset Pricing Model, Arbitrage Pricing Model).
And the random component is due to new information on the stock. It is not
predictable from the past prices.
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Equation (2.1) can be rewritten as follow:
Pt = Expected return + Random errort
(2.2)
Equation (2.2) implies that if the weak form of the EMH is true, past price
changes with a k lag period ( Pt-k) should be unrelated to the future price changes
( Pt). In other words, a market can be said to be weakly efficient if the current
price reflect all past market data. The correct implication of a weak-form efficient
market is that the past history of price information is of no value in assessing
future changes in price. Thus, most previous studies use autocorrelation test or
unit root test to test whether the stock market exhibits the weak-form efficiency.
And they all conclude that the emerging stock markets are weakly efficient3.
 Figure 2.1: Relationship among three different information sets
All information
Publicly available
information
Past
Information
Source: Ross et al. (2006)
Semi-Strong Form Efficiency
The semi-strong form efficiency suggests that the current price fully incorporates
all publicly available information. Public information includes not only past
prices, but also data reported in a company‟s financial statements (annual reports,
income statements, filings for the State Security Commission, etc.), dividend
payments, stock split announcements, announced merger plans, new product
3
These empirical studies, including those on the Vietnamese stock markets are not explicitly presented in
this thesis, because we mostly concern the next level of market efficiency.
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developments, financing difficulties, the financial situation of company‟s
competitors, expectations regarding macroeconomic factors (such as inflation,
money supply, exchange rate, interest rate, economic growth), etc. Semi-strong
efficiency requires the existence of market analysts who are not only financial
economists able to comprehend implications of vast financial information, but
also macroeconomists, experts adept in understanding processes in product and
input markets. And according to Ross et al. (2006), acquisition of such skills,
however, must take a lot of time and effort. In addition, the “public” information
may be relatively difficult to gather and costly to process. It may not be sufficient
to gain the information from, say, major newspapers and company-produced
publications. One may have to follow wire reports, professional publications and
databases, local papers, research journals, etc., in order to gather all information
necessary to effectively analyze securities.
Fama (1970) stated that, in the stock market, the intrinsic value of a share is
equivalently measured by the future discounted value of cash flows that will
accrue to investors. If the stock market is efficient, the market price of share must
be equal to its intrinsic value. And according to dividend discount model, the
price of the security reflects the present value of its expected future cash flows
which is really affected by the changes in the macroeconomic environment such
as money supply, trade activities, foreign capital flow, interest rate, exchange
rate, industrial production, inflation, etc. From this foundation, Hanousek and
Filer (2000) suggest that a market is considered as semi-strongly efficient if it
simultaneously meets the following conditions. First, a contemporaneous
relationship must exist between real variables and market returns. Second, lagged
values of real variables must not enable a potential investor to predict current
returns in the market. Hence, various empirical studies have employed the
Granger causality approach to test semi-strong form efficiency. We will develop
this idea in the research methodology Chapter.
Strong Form Efficiency
The strong form efficiency states that the current price fully incorporates all
existing information, both public and private (also called inside information). The
main difference between the semi-strong and strong efficiency hypotheses is that,
in the latter, nobody should be able to systematically generate profits even if
trading on information not publicly known at the time. In other words, company‟s
managements (insiders) would not be able to systematically gain from inside
information by buying company‟s stocks ten minutes after they decided (but did
not publicly announce) to pursue what they perceive to be a very profitable
acquisition. The rationale for strong-form market efficiency is that the stock
market anticipates, in an unbiased manner, future developments and therefore the
stock prices may have incorporated all relevant information and evaluated in a
much more objective and informative way than the insiders. According to Ross et
al. (2006), a very strong assumption of this form is that inside information cost is
always zero. However, this assumption hardly exists in reality, so the strong form
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efficiency is not very likely to hold. This form of market efficiency is, therefore,
beyond the scope of this thesis.
Ross et al. (2006) summaries that semi-strong form efficiency implies weak
form efficiency, and strong form efficiency implies semi-strong form efficiency.
Therefore, it‟s impossible to expect semi-strong efficiency, if the market is
currently weakly inefficient. The efficient market hypothesis has various practical
meanings. However, within this thesis, two fundamental implications are of
special concern.
First, if the weak form efficiency exists, technical trading systems (technical
analysis) that rely on knowledge and use of past trading data cannot be of value.
According to Keilly and Brown (1997), a basic premise of technical analysis is
that stock prices move in trends that persist. Technicians believe that when new
information comes to the market, it is not immediately available to everyone but
typically is gradually disseminated from the informed professional to the
aggressive investing public and then to the great bulk of investors. Also,
technicians contend that investors do not analyze information and not act
immediately. This process usually takes time. Accordingly, they hypothesize that
stock prices move to a new equilibrium after the release of new information in a
gradual manner, which causes trends in stock price movements that persist for
certain periods. This indicates that if the stock market is „weakly‟ efficient and
prices fully reflect all „past‟ relevant information, technical analysis becomes
meaningless.
Second, if the semi-strong form efficiency is true, no form of “standard”
security analysis4 based on publicly available information will be useful. In this
situation, since stock prices reflect all relevant publicly available information,
gaining access to information others already have is of no value. Traditionally,
fundamental analysts believe that, at any time, there is a basic intrinsic value for
the aggregate stock market, various industries, or individual stocks and that these
values depend on underlying economic factors. As a result, a fundamental analyst
would determine the intrinsic value of an investment asset (common stock, bond)
at a point in time by examining the variables that determine value such as current
and future earnings, interest rates, and risk variables. According to Keilly and
Brown (1997), if the prevailing market price differs from the intrinsic value by
enough to cover transaction costs, an investor should immediately take
appropriate action. However, if all investors can access the same publicly
information, the market price tends to reflect the correctly intrinsic value of an
investment asset. This second implication of EMH raises an important question
4
This implies the conventional fundamental analysis, which seeks to estimate the intrinsic value of a
security and provide buy or sell decisions depending on whether the current market price is less than or
greater than the intrinsic value (Jones, 1998).
12
about the underlying relationships between general economic environment and
stock prices, which is shortly explained in the next section.
2.3 MARKET EFFICIENCY AND VALUATION
According to Damodaran (2002), the question of whether markets are efficient,
and, if not, where the inefficiencies lie, is central to investment valuation. If
markets are in fact efficient, the market price provides the best estimate of value,
and the process of valuation becomes one of justifying the market price. If
markets are not efficient, the market price may deviate from the true value, and
the process of valuation is directed toward obtaining a reasonable estimate of this
value. Those who do valuation well, then, will be able to make higher returns
than other investors because of their capacity to spot under- and over-valued
firms. To make these higher returns, though, markets have to correct their
mistakes over time. There is also much that can be learned from studies of market
efficiency, which highlight segments where the market seems to be inefficient.
These inefficiencies can provide the basis for screening the universe of stocks to
come up with a subsample that is more likely to contain undervalued stocks.
Given the size of the universe of stocks, this not only saves time for the analyst,
but it increases the odds significantly of finding under- and overvalued stocks.
Damodaran (2002) also said that an efficient market is one where the market
price is an unbiased estimate of the true value of the investment. The true value is
also known as the intrinsic value, which becomes the central of concern in
fundamental analysis. Markets do not become efficient automatically. It is the
actions of investors, sensing bargains and putting into effect schemes to beat the
market, that make markets efficient. For this fact, Damodaran (2002) pointed out
three propositions about market efficiency.
First, the probability of finding inefficiencies in an asset market decreases as
the ease of trading on the asset increases. To the extent that investors have
difficulty trading on an asset, either because open markets do not exist or there
are significant barriers to trading, inefficiencies in pricing of the asset can
continue for long periods.
Second, the probability of finding inefficiency in an asset market increase as
the transactions and information cost of exploiting the inefficiency increases. The
cost of collecting information and trading varies widely across markets and even
across investments in the same markets. As these costs increase, it pays less and
less to try to exploit these inefficiencies.
Third, the speed with which inefficiency is resolved will be directly related to
how easily the scheme to exploit the inefficiency can be replicated by other
investors. The ease with which a scheme can be replicated is related to the time,
resources, and information needed to execute it. Since every few investors singlehandedly possess the resources to eliminate inefficiency through trading, it is
13
much more likely that inefficiency will disappear quickly if the scheme used to
exploit the inefficiency is transparent and can be copied by other investors.
Above analysis provides three important points. First, some segments of the
Vietnamese stock market can be weakly efficient. Second, the Vietnamese stock
market as a whole can be semi-strongly inefficient because it seems to be costly
to obtain appropriate public information, especially macroeconomic variables.
Third, fundamental analysis can provide a good conceptual framework for
identifying key determinants of stock prices. As we will discuss in the
consecutive section of valuation models that the intrinsic value of any asset is the
present value of expected future cash flows on it which in turn determined by
expected growth and discount rate. These fundamentals are only determined by
expected macroeconomic conditions. In an efficient market, the current market
prices (Pt) randomly deviate around its true value (Vt), so it must also depend on
the expected macroeconomic conditions. During this thesis, the term “expected”
implies the period t+1. In other words, in an efficient market, all past
macroeconomic conditions (i.e. t-1 or t-p) do not explain the intrinsic value, and
thus the current market prices as well. Therefore, if we find any relationship
between past macroeconomic conditions and current stock prices, we can
conclude that the market is semi-strongly inefficient.
2.4 THE DETERMINANTS OF STOCK PRICES
2.4.1 The Determinants of True Value
According to Damodaran (2002), there are three fundamental approaches to
estimating the true value of investments5, namely, discounted cash flow valuation
(DCF), relative valuation, and contingent claim valuation. However, the DCF is
the foundation on which all other valuation approaches are built. To do relative
valuation correctly, we need to understand the fundamentals of DCF valuation.
To apply option pricing models to value assets, we often have to begin with a
DCF valuation. Although there are various variants of DCF models6, we will only
discuss the easiest one, called Dividend Discount Model (DDM), because our
objective is to identify key determinants of the true value, rather than
fundamental analysis.
5
During this thesis, we just call the term “investment” as the common stock investment.
6
See Damodaran, 2002, Investment Valuation: Tools and Techniques for Determining the Value of Any
Asset, 2nd Edition, Wiley & Sons.
14
The dividend discount model (DDM) assumes that the true value of a
common stock is the present value of all future dividends7. This model is
specified as follows:
Vj
D1
(1 k e )
n
t 1
D2
(1 k e ) 2
D3
(1 k e ) 3
...
D
(1 k e )
Dt
(1 k e ) t
(2.3)
where
Vj = value of common stock j
Dt = dividend during period t
ke = cost of equity
For infinite period model, with assumption that the future dividend stream will
grow at a constant rate, g%/year, the equation (2.3) is then rewritten as follows:
Vj
D0 (1 g)
(1 k e )
D0 (1 g) 2
(1 k e ) 2
...
D1
ke
g
D0 (1 g) n
(1 k e ) n
(2.4)
where
D0 = dividend in period 0 (current period)
D1 = dividend in period 1
ke = cost of equity
g = constant growth rate
7
This model was initially set forth in J.B.Williams (1938), and was subsequently reintroduced and
expanded by Myron J. Gordon (1962).
15
 Figure 2.2: Breakdown of Risk
Competition may be
Exchange rates and
stronger or weaker
Political risk affect
than anticipated
many stocks
Projects may do
Entire industry
better or worse
may be affected
than expected
by action
Interest rates,
inflation, and
news
Firm-specific
Marketwide
Actions/Risk
Actions/Risk
that affect
that affect all
only one firm
Affects few firms
Affects many firms
investments
Source: Damodaran (2002)
The equation (2.4) tells that the intrinsic value of a stock is determined by two
fundamentals, including cost of equity and expected growth rate. According to
Keilly and Brown (1997), two variables ke and g are independent to each other
because ke depends heavily on risk, whereas g is a function of the retention rate
(or reinvestment rate) and the expected return on equity investments (ROE).
Risk is commonly classified into two components. The first component
usually relates to one or a few firms. In financial economics, this category is
known as firm-specific or unsystematic risk. Another risk component has broader
effects on every stock such as interest rate and inflation. This category is known
as market or systematic risk. The breakdown of risk is illustrated in Figure 2.2.
Figure 2.2 shows us that thanks to diversification strategies, rational investors
eventually face market risk, which in turn depends on the macroeconomic and
political conditions (Damodaran, 2002). Given this justification, we can
demonstrate determinants of ke as follows:
ke = f(R, I, E, FX, P)
(2.5)
16
where
R = Interest Rate
I = Inflation
E = News about Economy
FX = Exchange Rate
P = Political Stability
Turning back the second variable of the intrinsic value, g, we realize that the
growth rate in net income (for DDM models only) is a function of two
fundamental variables, including retention ratio (b) and return on equity
investment (ROE).
g = b * ROE
(2.7)
Because retention ratio depends mainly on specific firm‟s dividend policy, so the
rest of our concern is about the return on equity investment. In financial
economics, we have the famous equation as follows:
ROE = ROC + D/E[ROC – r(1-t)]
(2.8)
where
ROC = Return on capital investment
D/E = Capital structure
r
= Interest expense on debt/book value of debt
t
= Tax rate on ordinary income
Although the return on equity is affected by the leverage decisions of the firm,
it‟s also affected by macroeconomic factors. Therefore, g will eventually depend
on both monetary and fiscal policies. From equation (2.4) to equation (2.8), given
political stability, we can conclude that the intrinsic value of a certain stock is a
function of the following factors:
Vj = f( R, I, Es, FX, TB, GB, M)
(2.9)
Where denotes changes in macroeconomic variables, R is interest rate, I is
inflation, Es are economy health variables, FX is foreign exchange, TB is trade
balance, GB is government budget, and M is money supply.
If we assume the market is efficient, the stock price randomly deviates around
its true value, the stock price is then demonstrated as follows:
17
P j = Vj +
(2.10)
Thus, the stock price can be written as the following function:
Pj = f( R, I, Es, FX, TB, GB, M, )
(2.11)
For these reasons, general economic environment influences stock prices. It is
obvious that monetary and fiscal policy measures enacted by various agencies of
national government influence the aggregate economy. The resulting economic
conditions influence all industries and companies within the economy. Fiscal
policy initiatives such as tax cut credits or tax cuts can encourage spending,
whereas additional taxes on income or goods can discourage spending. Increases
or decreases in government spending also influence the general economy. All
such policies influence the business environment for firms that rely directly on
those expenditures. In addition, we know that government spending has a strong
multiplier effect. Monetary policy produces similar economic changes. A
tightening monetary policy that reduces the growth rate of the money supply
reduces the supply of funds for working capital and expansion for all businesses.
Alternatively, a restrictive monetary policy that targets interest rates would raise
market interest rates and therefore firms‟ costs, and make it more expensive for
individuals and firms. Monetary policy also affects all segments of an economy
and that economy‟s relationship with other economies.
Keilly and Brown (1997) states that any economic analysis requires the
consideration of inflation, which causes differences between real and nominal
interest rates and changes the spending and savings behavior of consumers and
corporations. In addition, unexpected changes in the rate of inflation make it
difficult for firms to plan, which inhibits growth and innovation. Beyond the
impact on the domestic economy, differential inflation and interest rates
influence the trade balance between countries and the exchange rate for
currencies. In addition to monetary and fiscal policy actions, events such as war,
political upheavals in foreign countries, or international monetary devaluations
produce changes in business environment that add the uncertainty of sales and
earnings expectations and therefore risk premium required by investors.
2.4.2 The Determinants of Stock Price
Jones (1998) says that although the dividend discount models, using expected
dividends and cost of equity, can help identify the ultimate determinants of stock
prices, a more complete model of economic variables is desirable. This model
was first introduced by Keran in 1971. Jones (1998) emphasizes that although
presented many years ago, this classic model remains an accurate description of
the conceptual nature of stock price determination then, now, and for the future.
The two primary exogenous policy variables, G and M, affect stock prices
through two channels. First, they affect total spending (Y), which, together with
18
the tax rate (tx), affects corporate earnings. Jones (1998) states that both current
levels and lagged changes in Y affect corporate earnings. Expected changes in
(real) corporate earnings (∆E *) are positively related to changes in stock prices
(∆P). Second, they affect total spending, which, together with the economy‟s
potential output (Y*) and past changes in prices, determine current changes in
prices (∆I). ∆Y and ∆I determine current changes in real output (∆X). Changes in
X and I generate expectations about inflation and real growth, which in turn
influences the current interest rate (R). Interest rates, a proxy for discount rate in
a valuation model, have a negative influence on stock prices ( P). Therefore, the
Keran model remains a classic description of stock price determination because it
indicates the major factors that determine stock prices.
19
 Figure 2.3: Determinants of stock price
Exogenous
Variables
Endogenous Variables
Corporate
tax rate (t)
Changes in
government
spending ( G)
Changes in
total spending
( Y)
Changes in
nominal
money ( M)
Changes in
price level
( I)
Nominal
corporate
earnings ( E)
Real corporate
earnings
( E*)
Changes in
real output
( X)
Expected real
corporate
earnings ( E*e)
Stock
price (P)
Interest rates
(R)
Potential
output (Y*)
Changes in
real money
( M)
Source: Jones (1998)
From the Keran‟s perspective, along with the above dividend discount
models, the stock prices can be expressed in the following function:
P = f( R, I, X, FX, TB, G, GB, M, )
(2.12)
In short, fluctuations in stock markets ( P) are related to changes in the aggregate
economy. The price of a certain stock reflect investor expectations about an
issuing firm‟s performance in terms of earnings or cash flow, growth rate and the
investor‟s required rate of return (cost of equity). This performance is likewise
affected by the overall performance of the economy.
Besides the above theoretical models, we will only develop analytical models
for testing semi-strong efficiency in the Vietnamese stock market after reviewing
some empirical studies in order to answer the research questions were mentioned
in Chapter 1.
20
2.5 EMPIRICAL STUDIES
Since its introduction into the financial economics literature over almost 50 years
ago, the efficient markets hypothesis has been examined extensively in numerous
studies. The vast majority of these researches indicate that stock markets are
indeed weakly efficient. For this reason, the rest of concern is often placed on the
semi-strong efficiency. In this section, we briefly discuss the evidence regarding
the semi-strong form of the efficient markets hypothesis.
In his influential paper, Fama (1981) states that common stock returns are
correlated with some macroeconomic variables of a country, such as money
supply, inflation, interest rate, and capital expenditure. The most important
implication of his findings is that changes in macroeconomic variables can be
used to predict changes in stock prices.
The types of relationships between stock market returns and macroeconomic
variables can be varied. As Mahdavi and Sohrabian (1991) report, there was an
asymmetric causal relationship between those variables when they explored the
relationship between changes in stock prices and GDP growth in the U.S using
Granger causality tests. The stock market growth rate caused GDP growth rate,
yet no reverse causation was found. Chen (1991), however, finds that the current
and future economic growth could be revealed by several domestic variables,
such as the market dividend-price ratio, short run interest rates, the lagged
production growth rate, the term premium, and the default premium.
Furthermore, Rousseau and Wachtel (2000) reveal that equity markets have been
key institutions in promoting economic activity in 47 countries. However, it is
worth noting that this finding may be different in countries with less financially
developed or smaller market capitalization (Minier, 2003).
Mauro (2003) suggests that developments in stock prices should be taken into
account in forecasting output. However, it should also be considered that the
relationship between stock returns and economic growth has not been stable over
time (Stock and Watson 1990). For example, Cheng (1995) argues that a number
of systematic economic factors significantly influenced the U.K stock returns.
Meanwhile, this result contradicts with that of Poon and Taylor (1991) who also
observe the interrelationship between macroeconomic factors and stock prices in
the U.K.
The relationship between stock prices and economic activity is not only
limited to the relationship between stock prices and economic growth, but may
also be extended to other economic factors, as Fama (1981) mentioned. Abdullah
and Hayworth (1993) argue that stock returns are positively related to inflation
and growth of the domestic money supply in the U.S, but negatively related to
domestic interest rates. Beenstock and Chan (1988) also report that interest rates,
input (fuel and raw material) costs, money supply, and inflation are the
significant risk factors of the London stock market.
21
For the Pacific region, in Australia, there was a unidirectional relationship (in
negative fashion) between inflation and the nominal stock returns during the
1965-1979 period, with price levels leading the equity index (Saunders and Tress
1981). Other researchers (Leonard and Solt, 1987; Giovanini and Jorion, 1987;
Kaul and Seyhun 1990; Randal and Suk, 1999) also support a significant
relationship between inflation or expected inflation and stock market prices.
In terms of the relationship between stock market returns and exchange rate,
Johnson and Soenen (1998) state depreciation may cause the cost of imports to
increase, leading to domestic price level increases, which would expectedly have
a negative impact on stock prices. Morley and Pentecost (2000) also confirm that
stock markets and exchange rates are linked, and note that this connection is
through a common cyclical pattern rather than a common trend.
For the Asia-Pacific region, Hamao (1988) found a significant relationship
between the Japanese stock returns and several factors, such as the changes in
expected inflation and the term structure of interest rates. Ibrahim (1999) also
observed the Malaysian exchange rate by using bivariate and multivariate
cointegration as well as Granger causality tests and found cointegration when the
M2 measure of money supply and reserves are included, but no long-run
relationship between the exchange rate and stock prices was found using
bivariate models. These suggest that in the short run the exchange rate might play
a significant role in the domestic economy, and that the Malaysian stock
exchange is informationally inefficient.
However, in some cases, macroeconomic factors cannot be reliable indicators
for stock market prices movement in the Asian markets because of the inability
of stock markets to fully capture information about the change in macroeconomic
fundamentals (as is cited in Wongbangpo and Sharma, 2002).
In conclusion, macroeconomic variables (i.e. economic growth, inflation,
interest rate, and exchange rate) of a country are related to each other. Above
studies provide a comprehensive explanation for the relationship among the real
sector, the monetary sector, and in the exchange rate within an economy. Thus,
changes in one of those factors may have an influence on the others. Stock price
movements are, either symmetrically or asymmetrically, related to
macroeconomic variables. In some cases, short run causal linkages between stock
price movement and macroeconomic variables are also appear. It is worth noting
that the relationship between the stock market movement and macroeconomic
variables may not be direct, consistent, and stable over time.
For the focus of this thesis, the test results from emerging stock markets such
as Malaysia, Thailand, Indonesia, Philippines and Central European countries
seem to be most significant. Ibrahim (1999) investigates the dynamic interactions
between seven macroeconomic variables (the industrial production index,
consumer prices, M1, M2, credit aggregates, foreign reserves and exchange rates)
and the stock prices for an emerging market, Malaysia, using Granger causality
tests and error correction mechanism tests. This analysis is conducted using
22
monthly data series for the period from January 1977 to June 1996. To smooth
possible volatility, all data series are expressed in logarithmic forms. Generally,
the results suggest informational inefficiency in the Malaysian market.
Using the bivariate causality tests, Ibrahim (1999) suggests three important
points. First, the results largely indicate that the lagged changes in
macroeconomic variables have no significant predictive ability for the
movements in stock prices. Second, the stock market movements could help
anticipate variations in the industrial production, the M1 money supply, and the
exchange rate. From this finding, he says that the causal link from stock prices to
the M1 money supply may reflect the importance of the stock market on the M1
money demand. Third, there exists the cointegration between the stock prices and
three macroeconomic variables – consumer prices, credit aggregates and official
reserves. The results suggest that deviations from the equilibrium path are
adjusted by about 5%–8% the next month through the movements in stock prices.
Thus, the adjustment toward the long-run relationship is extremely low in
Malaysian stock market.
Hanousek and Filer (2000) examine the possibility that newly-emerging
equity markets in Central Europe exhibit semi-strong form efficiency such that
no relationship exists between lagged values of changes in macroeconomic
variables (M1, M2, exports, imports, trade balance, foreign capital inflow, budget
deficit, government debt, CPI, PPI, exchange rate, and industrial production) and
changes in equity prices using Granger causality tests. They find that while there
are connections between real economy and equity market returns in Poland and
Hungary, these links occur with lags, suggesting the possibility of profitable
trading strategies based on public information and rejecting semi-strong
efficiency hypothesis. For Czech Republic and Slovakia, the situation is more
complex. In the early years of their existence, these markets may have possessed
elements of semi-strong efficiency, with both lagged and contemporaneous
relationships between real variables and equity markets. However, these links
have disappeared over time. In other words, these stock markets appear to have
become increasingly divorced from reality. In the same manner, Azad (2009)
conducts a cross-market test including China, Japan, and South Korea and
concludes that while Chinese stock market is inefficient, Japanese and South
Korean stock markets are semi-strongly efficient. These empirical studies, along
with other studies about the U.S markets, suggest that developed equity and
newly-emerging stock markets exhibit semi-strong form efficiency, while this is
not a case in developing stock markets.
Atmadja (2005) examines the existence of Granger causality among stock
prices indices and macroeconomic variables in five ASEAN countries, Indonesia;
Malaysia; the Philippines; Singapore; and Thailand with particular attention to
the 1997 Asian financial crisis and period onwards. Using monthly time series
data of the countries, a Granger-causality test based on the vector autoregressive
(VAR) analytical framework was employed to empirically reveal the causality
23
among the variables. This research finds that there were few Granger causalities
found between the country‟s stock price index and macroeconomic variables.
This indicates that the linkages between domestic stock price movements and
macroeconomic factors were very weak. Due to that, the ASEAN stock markets
were relatively unable to efficiently capture changes in economic fundamentals
during the observation period in most of the countries in accordance to the
literature in emerging stock markets, and that the influence of specific
macroeconomic factors on the domestic economies differ across countries. This
also implies that the stock markets do not seem to have played a significant role
in most countries‟ economies, and macroeconomic variables are unlikely to be
appropriate indicators to predict not only the future behavior of other
macroeconomic variables, but also that of the stock market price indices.
In summary, in a perfectly efficient market, security prices always reflect
immediately all available information, and investors are not able to use available
information to earn abnormal returns because it already impounded in prices.
However, only information about changes in past prices and macroeconomic
variables is mostly concerned by investors, academics, and policy makers.
Regarding the resource allocation function, the stock market should be oriented
toward semi strong form efficiency. This is also the key concept of this thesis. In
order to test the existence of semi strong – form efficiency, most previous studies
investigated the relationships between stock market returns and changes in
macroeconomic variables in the form of cointegration approach to Granger
causality tests. These will be further discussed in Chapter 3.
24
CHAPTER 3
RESEARCH METHODOLOGY
The conceptual framework discussed in Chapter 2 indicates that if the future
changes in stock prices are dependent on past changes in macroeconomic
variables, the market is characterized by semi-strong inefficiency. To test
whether the market is semi-strongly efficient, most previous studies employed
the Granger causality model (short-run efficiency) and the error correction model
(long-run efficiency). And these are also what this thesis intends to discover in
the Vietnamese context.
This chapter will first explain the fundamental concept in time series
econometrics: “stationarity”, which provides an underlying guidance for the
remaining parts of this thesis. The widely accepted augmented Dickey-Fuller
(ADF) tests and Phillips-Perron (PP) tests are also introduced in this section. It
seems that the most important analytical framework of this thesis places on the
Granger causality and error correction mechanism analysis in the next sections.
Finally, we will discuss how the thesis deals with proxy variables, data
collection, and data analysis.
3.1 STATIONARITY AND UNIT-ROOT TESTS
This research design is completely based on the time series data. And nonstationarity is the central issue of concern when working with this kind of data,
including regression analysis and forecasting. According to Asteriou and Hall
(2007), most macroeconomic time series are trended and therefore in most cases
are non-stationary. The problem with non-stationary data is that the standard OLS
regression procedures can easily lead to incorrect conclusions, especially the
phenomenon of spurious regressions. For this reason, testing whether a certain set
of time series are stationary appears to be the first task in any time series based
research. This is also a case in this thesis.
In stationary time series, shocks will be temporary and over time their effects
will be eliminated as the series revert to their long-run mean values. On the other
hand, non-stationary time series will necessarily contain permanent components.
Therefore, the mean and/or the variance of a non-stationary time series will
depend on time, which leads to cases where a series (a) has no long-run mean to
which the series returns, and (b) the variance will depend on time and will
approach infinity as time goes to infinity.
There are various ways of identifying non-stationary series, such as line
graph, correlogram, t statistic test, or Ljung-Box Q statistic test. However, these
25
methods are usually used in practical applications. Asteriou and Hall (2007)
states that these methods are bound to be imprecise because a near unit-root
process will have the same shape of autocorrelation function with that of a real
unit-root process. Most academic studies apply the widely approved unit-root test
methods, introduced by Dickey-Fuller (1979). In statistics language, if a time
series have a unit root, it is called „non-stationary‟.
There are various versions of unit-root tests8, but for this thesis application
purpose, I just concentrate on the augmented Dickey-Fuller (ADF) test. Suppose
that we want to test for the existence of a unit root of Y t (H0: = 0), three
possible forms of the ADF test are given by the following equations:
p
Yt
Yt
1
Yt
i
ut
i
(3.1)
i 1
p
Yt
0
Yt
1
Yt
i
i
ut
(3.2)
i 1
p
Yt
0
1T
Yt
1
i
Yt
i
ut
(3.3)
i 1
The difference between the three
deterministic elements α0 and α1T.
equations, this thesis will first plot
graph because it can, to which
deterministic regressors.
regressions concerns the presence of the
For choosing the best one among the three
the data (of each variable) and observe the
extent, indicate the presence or not of
According to Ibrahim (1999), the PP9 test for unit roots is conducted in a
similar manner using regression (3.3) without the lagged first differenced terms.
3.2 VECTOR AUTOREGRESSIVE MODELS AND CAUSALITY TESTS
3.2.1 Vector Autoregressive (VAR) Models
According to Asteriou and Hall (2007), when we are not confident that a variable
is really exogenous, we have to treat each variable symmetrically. Take for
example the series Yt that is affected by current and past values of X t, and
simultaneously, the series Xt to be a series that is affected by current and past
values of the Yt. In this case, we will have the simple bivariate model given by:
Yt =
8
9
10
-
12Xt
+
11Yt-1
+
12Xt-1
+ uyt
(3.4)
DF tests (three different regression equations) and ADF tests (three different regressions equations).
Yt
1
0
Yt
1
ut
26
Xt =
20
-
21Yt
+
21Yt-1
+
22Xt-1
+ uxt
(3.5)
where we assume that both Yt and Xt are stationary and uyt and uxt are
uncorrelated white-noise error terms. Equations (3.4) and (3.5) constitute a firstorder VAR model, because the longest lag length is unity. These equations are
not reduced-form equations since Yt has a contemporaneous impact on Xt, and Xt
has a contemporaneous impact on Yt.
The VAR model has some good characteristics. First, it is very simple
because we do not have to worry about which variables are endogenous or
exogenous. Second, estimation is very simple as well, in the sense that each
equation can be estimated with the usual OLS method separately. Third, forecasts
obtained from VAR models are in most cases better than those obtained from the
far more complex simultaneous equation models. However, on the other hand the
VAR models have faced severe criticism on various different points. First, they
are a-theoretic since they are not based on any economic theory. Second, as
higher order or higher lag length, they face the loss of degrees of freedom. Third,
the obtained coefficients of the VAR models are difficult to interpret since they
totally lack any theoretical background. In addition, the same lag lengths among
endogenous variables in VAR models are also another significant limitation.
3.2.2 Granger Causality Tests
According to Asteriou and Hall (2007), one of the good features of VAR models
is that they allow us to test the direction of causality. Causality in econometrics is
somewhat different to the concept in everyday use; it refers more to the ability of
one variable to predict (and therefore cause) the other. Suppose two (stationary)
variables, say Yt and Xt, affect each other with distributed lags. The relationship
between those variables can be captured by a VAR model. In this case, it is
possible to have that (a) Yt causes Xt, (b) Xt causes Yt, (c) there is a bi-directional
feedback (causality among the variables), and finally (d) the two variables are
independent. The problem is to find an appropriate procedure that allows us to
test and statistically detect the cause and effect relationship among variables. This
is also one of the most important objectives of this thesis. There are two
interchangeable causality tests, namely Granger causality test and Sims causality
test. However, the Sims test, using more regressors (due to the inclusion of the
leading terms), leads to a bigger loss of degrees of freedom (Asteriou and Hall,
2007). For this reason, the Granger causality test may be more appropriate in the
Vietnamese stock market context.
Engle and Granger (1987) developed a relatively simple test that defined
causality as follows: a (stationary) variable Yt is said to Granger-cause
(stationary) variable Xt, if Xt can be predicted with greater accuracy by using past
values of the Yt variable rather than not using such past values, all other terms
remaining unchanged.
27
The Granger causality test for the of two stationary variables Y t and Xt,
involves as a first step the estimation of the following VAR model:
r
Yt
a1
s
i
Yt
i
j
i 1
p
Xt
a2
Xt
j
yt
(3.6)
Yt
j
xt
(3.7)
j 1
q
iXt
i
i 1
j
j 1
where it is assumed that both εyt and εxt are uncorrelated white-noise error terms.
In this model, we can have the following different cases:
Case 1: The lagged X terms in (3.6) may be statistically different from zero as a
group, and the lagged Y terms in (3.7) not statistically different from
zero. In this case, we have that Xt causes Yt.
Case 2: The lagged Y terms in (3.7) may be statistically different from zero as a
group, and the lagged X terms in (3.6) not statistically different from
zero. In this case, we have that Yt causes Xt.
Case 3: Both sets of X and Y terms are statistically different from zero as a group
in (3.6) and (3.7), so that we have bi-directional causality.
Case 4: Both sets of X and Y terms are not statistically different from zero in
(3.6) and (3.7), so that Xt is independent of Yt.
The Granger causality test involves the following steps:
Step 1: Regress Yt on lagged Y terms as in the following model:
r
Yt
a1
i
Yt
i
(3.8)
yt
i 1
and obtain the RSS of this regression (which is the restricted one) and
label it as RSSR.
Step 2: Regress Yt on lagged Y terms plus lagged X terms as in the following
model:
r
Yt
a1
s
i
i 1
Yt
i
j
Xt
j
yt
(3.6)
j 1
and obtain the RSS of this regression (which is the unrestricted one) and
label it as RSSU.
28
Step 3: Set the null and alternative hypotheses as below:
s
H0 :
j
0 or X t does not cause Yt
j
0 or X t does cause Yt
j 1
s
H1 :
j 1
Step 4: Calculate the F statistic for the normal Wald test on coefficient
restrictions given by:
(RSS R RSS U ) / s
RSS u /(n k )
F
(3.9)
Where n is the included observations and k is the number of estimated
coefficients in the unrestricted model.
Step 5: If the computed F value exceeds the critical F value, reject the null
hypothesis and conclude that Xt causes Yt.
Similarly, we conduct the same procedure for equation (3.7) in order to test if Yt
causes Xt.
3.3 SEMI-STRONG FORM EFFICIENCY TESTS
According to Hanousek and Filer (2000), a market is semi-strongly efficient, if
two following conditions must hold. First, a contemporaneous relationship must
exist between real variables and market returns10. Second, lagged values of real
variables must not be enabling a potential investor to predict current returns in
the market (Case 2 or Case 4). Both of these relationships are important.
Although the first is often ignored in empirical research, if it fails to hold, then
the fact that the second does is not proof of efficiency. It may simply be that the
variable under examination is irrelevant in determining prices in the equity
market. These relationships can be expressed as follows:
r
Yt
0
Yt
0
i 1
i
Yt
i
t
i
Yt
i
0
(3.10)
r
10
i 1
Xt
t
(3.11)
Suppose that Yt in equation (3.6) denotes stock market return (or change in stock market index), and X t
in equation (3.6) denotes a certain economic variable.
29
and
r
Yt
0
i 1
s
Yt
i
i
0
Xt
j 1
j
Xt
j
t
(3.12)
where Y represents the stock market index, X is one of a set of macroeconomic
variables, and r and s are the appropriate lag lengths (which are usually
determined by AIC criterion). Hanousek and Filer (2000) use the first-order
difference form of Yt and Xt because most economic variables are integrated of
order 1 (Gujarati, 2003). Hopefully, this will be also a case in Vietnam.
The conventional test of Granger causality is whether or not equation (3.12)
better explains movements in the dependent variable than equation (3.11). In
addition, to ensure that a failure of lagged macroeconomic factors to have a
significant effect on stock market returns results from the market efficiently
processing information rather than from that information not being an important
determinant of prices, we also examine whether equation (3.11) better predicts
returns than equation (3.10). In summary, results support market efficiency if
both of null hypotheses hold:
(A) H0:
0
(B) H0:
j
0 (in equation 3.11)
= 0,
j (in equation 3.12)
A finding that hypothesis (B) does not hold suggests that the market is (semistrongly) inefficient. A finding that hypothesis (B) holds, but (A) does not
suggest that efficiency considerations are irrelevant (Hanousek and Filer, 2000).
3.4 ERROR CORRECTION MODELS
According to Ibrahim (1999), the standard Granger tests (just discussed above)
are not appropriate when the variables being analyzed are non-stationary and
cointegrated, so it is necessary to employ a priori tests of integration and
cointegration. These tests serve as a guide for proper specification of Granger
causality models. Eventually, two possibilities may occur. First, if two (or a set
of) variables under consideration are I(1) and they are not cointegrated, then we
need to implement the standard Granger causality tests using the first differences
of the variables. Second, if two (or a set of) variables are cointegrated, and then
an error correction model (ECM) should be used in evaluating the dynamic
interactions between them. The ECM conveniently combines the short-run
dynamics and long-run equilibrium adjustments of the variables concerned.
30
3.4.1 Cointegration
If two variables are non-stationary, then we can represent the error as a
combination of two cumulated error process. These cumulated error processes are
often called stochastic trends and normally we could expect that they would
combine to produce another non-stationary process. However, in the special case
that two variables, say X and Y, are really related, then we would expect them to
move together and so the two stochastic trends would be very similar to each
other and when we combine them together it should be possible to find a
combination of them which eliminates the non-stationarity. In this special case,
we say that the variables are cointegrated (Asteriou and Hall, 2007).
Cointegration becomes an over-riding requirement for any economic model using
non-stationary time series data. If the variables do not cointegrate, we usually
face the problems of spurious regression and econometric work becomes almost
meaningless. Inversely, if the stochastic trends do cancel to each other, then we
have cointegration.
Suppose that, if there really is a genuine long-run relationship between Yt and
Xt, the although the variables will rise overtime (because they are trended), there
will be a common trend that links them together. For an equilibrium, or long-run
relationship to exist, what we require, then, is a linear combination of Y t and Xt
that is a stationary variable (an I(0) variable). A linear combination of Yt and Xt
can be directly taken from estimating the following regression:
Yt = β1 + β 2 Xt + u t
(3.13)
And taking the residuals:
û t
Yt
ˆ
1
ˆ X
2
t
(3.14)
If û t ~ I(0), then the variables Yt and Xt are said to be cointegrated.
3.4.2 Error Correction Mechanism
We said before that when we have non-stationary variables in a regression model,
then we may get results that are spurious. So if we have Y t and Xt that are both
I(1), then if we regress:
Yt = β 1 + β 2 Xt + u t
(3.13)
we will not generally get satisfactory estimates of ˆ 1 and ˆ 2 .
One way of resolving this is to difference the data in order to ensure
stationarity of our variables. Therefore, after that we will have ∆Yt ~ I(0) and ∆Xt
~ I(0), and the regression model will be:
31
∆Yt = a1 + a2∆Xt + ∆ut
(3.15)
In this case, the regression model may give us correct estimates of the â 1 and â 2
parameters and the spurious equation problem has been resolved. However, what
we have from equation (3.15) is only the short-run relationship between the two
variables. Therefore, ECM becomes very useful in such a case.
If Yt and Xt are cointegrated, by definition û t ~ I(0). Thus, we can express the
relationship between Yt and Xt with an ECM specification as:
∆Yt = a0 + a1∆Xt - û t 1 +
∆Yt = a0 + b1∆Xt - [ Yt
1
ˆ
1
(3.16)
t
ˆ X ]+
2 t 1
t
(3.17)
which will now have the advantage of including both long-run and short-run
information. In this model, b1 is the impact multiplier (the short-run effect) that
measures the immediate impact that a change in Xt will have on a change in Yt.
On the other hand, is the feedback effect, or the adjustment effect, and show
how much of the disequilibrium is being corrected, i.e. the extent to which any
disequilibrium in the previous period effects any adjustment in Yt. Of course,
û t 1 Yt 1 ˆ 1 ˆ 2 X t 1 and therefore from equation (3.17), we also have β2 being
the long-run response. Because all variables in equation (3.16) are stationary, so
standard OLS is therefore valid. We can expand this for a more general model
with large numbers of lagged terms.
The coefficient in equation (3.16) is the error-correction coefficient and is
also called the adjustment coefficient. In fact,
tells us how much of the
adjustment to equilibrium takes place each period, or how much of the
equilibrium error is corrected. According to Asteriou and Hall (2007), it can be
explained in the following ways:
1. If = 1, then 100% of the adjustment takes place within the period, or the
adjustment is instantaneous and full.
2. If
= 0.5, then 50% of the adjustment takes place each period.
3. If
= 0, then there is no adjustment.
The ECM is important and popular for many reasons:
1. Firstly, it is a convenient model measuring the correction from
disequilibrium of the previous period which has a very good economic
implication. The coefficient provides us with information about the speed
of adjustment in cases of disequilibrium. According to Asteriou and Hall
32
(2007), when equilibrium holds, then ( Yt 1 0 1 X t 1 )11 = 0. However,
during periods of disequilibrium, this term will no longer be zero and
measures the distance the system is away from equilibrium. For example,
suppose that due to a series of negative shocks in the economy (captured
by the error term ut), the difference of ( Yt 1 0 1 X t 1 ) is negative
because Yt-1 has moved below its long-run steady-state growth path.
However, since is positive (and because of the minus sign in front of ),
the overall effect is to boost ∆Yt back towards its long-run path as
determined by Xt. The speed of this adjustment to equilibrium depends on
the magnitude of .
2. Secondly, if we have cointegration, ECMs are formulated in terms of first
difference, which typically eliminate trends from the variables involved;
they resolve the problem of spurious regressions.
3. A third very important advantage of ECMs is the ease with they can fit into
the general-to-specific approach to econometric modeling, which is a
search for the best ECM model that fits the given data sets.
4. Finally the fourth and most important feature of ECM comes from the fact
that the disequilibrium error term is a stationary variable. Because of this,
the ECM has important implications: the fact that the two variables are
cointegrated implies that there is some adjustment process which prevents
the errors in the long-run relationship becoming larger and larger.
3.4.3 Testing for Cointegration and ECM
Engle and Granger (1987) proposed a straightforward method which involves
four steps (hereafter called EG approach):
Step 1: Test the variables for their order of integration
By definition, cointegration necessitates that the variables be integrated of the
same order. Thus the first step is to test each variable to determine its order of
integration. This thesis will apply the ADF tests. There are three possible cases:
a) If both variables are stationary (I(0)), it is not necessary to proceed since
standard time series methods (OLS) can be correctly applied.
b) If the variables are integrated of different orders, it is possible to conclude
that they are not cointegrated.
11
We use the subscript 1 corresponding to the lag order.
33
c) If both variables are integrated of the same order, then we proceed with
step two.
Step 2: Estimate the long-run (possible cointegrating) relationship
If the results of step 1 indicate that both Xt and Yt are integrated of the same
order (usually in economics I(1)), the next step is to estimate the long-run
equilibrium relationship of the form:
Yt
ˆ
1
ˆ X
2 t
û t
(3.14)
and obtain the residuals of this equation.
If there is no cointegration, the results obtained will be spurious. However, if
the variables are cointegrated, then OLS regression yields “super-consistent”
estimators for the cointegrating parameter ˆ 2 .
Step 3: Check for (cointegration) the order of integration of the residuals
In order to determine if the variables are actually cointegrated, denote the
estimated residual sequence from the equation by û t . Thus, û t is the series of the
estimated residuals of the long-run relationship. If these deviations from long-run
equilibrium are found to be stationary, the Xt and Yt are cointegrated. Note that
because û t is a residual, so we do not include a constant nor a time trend in the
ADF equation.
Step 4: Estimate the error-correction model
If the variables are cointegrated, the residuals from the equilibrium regression can
be used to estimate the error-correction model and to analyse the long-run and
short-run effects of the variables as well as to see the adjustment coefficient,
which is the coefficient of the lagged residual terms of the long-run relationship
identified in step two.
3.4.4 ECM and Long-Run Efficiency
Ibrahim (1999) suggests a three-step analysis as follows. The first step is to
establish the order of integration of the variables concerned. Briefly stated, a
variable is said to be integrated of order d, written I(d), if it is stationary after
differencing d times. This means that the variable that is integrated of order
greater than or equal to 1 is non-stationary. In the present analysis, we employ
standard augmented Dickey-Fuller (ADF).
Once the order of integration is established for each variable (stock prices and
macroeconomic variables), we proceed to the second step to evaluate the
34
cointegration properties of the data series. The cointegration of the time series
such as the stock prices and a macroeconomic variable suggests the existence of a
long-run relationship that constrains their movements. That is, although the
variables are individually non-stationary, they cannot drift farther away from
each other arbitrarily. The requirements for cointegration are that the variables
are integrated of the same order and yet their linear combination is stationary. To
test for cointegration, we employ the most commonly used test – the residualbased test of Engle and Granger (1987). The Engle–Granger test is a two-step
procedure involving (i) an OLS estimation of a specified cointegrating regression
to obtain the residuals and (ii) a unit root test of the residuals obtained from (i).
The null hypothesis of no cointegration is rejected if the unit root statistics falls
below some critical values.
The last step in our investigation is to specify the dynamic interactions of the
variables based on the Granger causality models and the information on the
cointegration properties of the variables. In particular, according to Granger‟s
representation theorem, the dynamic relations between cointegrated series should
be modeled using an error-correction model (ECM). Thus, the dynamic causal
link from a macroeconomic variable (X) to the stock prices (Y) can be modeled as
r
Yt
0
s
i
i 1
Yt
i
j
Xt
j
ECM t
1
t
(3.18)
j 1
where ECM is the error correction term obtained from the cointegrating
regression or the linear long-run relationship of the variables. And the
coefficient must be negative. Note that, with this specification, the changes in the
stock prices will depend not only on the changes in the macroeconomic variable
but also on the long-run relationship between them. The latter allows for any
previous disequilibrium, measured by the error correction term ECM, to exert
potential influences on the movement of the stock prices. According to Toda and
Phillips (1994), the former may be termed „short-run causality‟ from the
macroeconomic variable while the latter may be termed „long-run causality‟.
Thus, the ECM conveniently combines the short-run dynamics and long-run
adjustment of the stock prices, introducing two channels of causality from the
macroeconomic variable to the stock prices. Accordingly, in the context of
equation (3.18), the stock prices can be informationally inefficient in either the
short run or the long run or both. In order to test whether the stock market is
informationally efficient in the long run, we can test the following null
hypothesis:
(C) H0:
is significant, but small (in equation 3.18)
The reverse causation from the stock prices to the macroeconomic variables of
interest may also be evaluated by reversing the roles of the two variables in
equation (3.18). From these tests, one of the following four patterns of causality
35
can be noted: (1) unidirectional causality from X to Y; (2) unidirectional causality
from Y to X; (3) bidirectional causality; and (4) no causality.
3.5 DATA COLLECTION AND ANALYSIS
From the conceptual model presented in equation (2.12) and empirical studies,
the stock prices can be specified as a function of the following macroeconomic
variables:
Pj = f(M1, M2, LR, DR, DC, IP, CP, FX, IM, EX, FR, MR)
(3.19)
The variables in equation (3.19) are defined as follows:
Stock Price: To measure stock prices for a comparative analysis purpose, this
thesis uses end-of-the-month values of the VN-Index (VNI) as the proxy for
stock prices.
Real output: End-of-the-month real output is measured by the real industrial
production (IP) because data on monthly GDP or GNP is not available.
Consumer Prices: We use the end-of-the-month consumer prices (CP) as a
measure of the aggregate price level.
Exchange Rates: We employ end-of-the-month VND/USD exchange rates (FX)
as a measure of the foreign exchange rate.
Balance of Payment: End-of-the-month imports (IM) and exports (EX) are used
to reflect the influence of the openness and trade on the stock market.
Monetary Variables: End-of-the-month M1, M2, lending rate (LR), deposit rate
(DR), and domestic credit (DC), foreign reserve (FR) and money reserve (MR)
are proxies for monetary policy.
The analysis is conducted on monthly data for the main stock market index in
Vietnam, namely, VN Index, from December 2000 to June 2009. Thus, we have
103 monthly observations. VN Index is collected from Thomson Reuters, while
macroeconomic variables are collected from Thomson Reuters, Bloomberg and
International Monetary Fund (IMF).
The issue of timing is important. Although most economic variables are
announced on about the twenty of each month, they‟re usually synthesized on
around the tenth to fifteenth of each month. While the VN Index is synthesized
on the end-of-the-month basis. Therefore, when we investigate a
“contemporaneous” relationship, we are examining the ability to predict, say June
stock market returns on the basis of May values of economic variables, which
would be announced about one-third of the way through the trading period. As
Hanousek and Filer (2000) said that unanticipated information is incorporated
into equity prices in periods as short as an hour (or even a single trade), so the
allowance of a two-to-three week period as a part of the current time period is
36
very generous, and should bias results in favour of finding semi-strong
efficiency. On the other hand, since there is no any theoretical background of the
so-called “proper” time period, we must accept this reality, instead of seeking
alternative market return periodicity12.
Since we want to investigate whether the financial crisis has any impact on
the efficiency, we conduct two separate analyses: (i) for the December 2000 to
June 2009; and (ii) for the December 2000 to June 2008. This milestone is
defined by the fact that stock prices and macroeconomic variables changed
significantly. Previous studies, such as Hanousek and Filer (2000), also did the
same, instead of using the dummy technique.
From now on, let‟s denote VNI as the VN Index, and M as the
macroeconomic variable in each VAR model. Using Eviews, this thesis will be
conducted in the following steps in order to answer the research questions.
Bivariate Analysis
Step 1: Transform all variables into the logarithmic form so as to remove possible
volatility.
Step 2: Test the variables in log-level for their order of integration. Suppose they
are integrated of the same order, say I(1), we then take their firs2t
differences, and name ∆VNIt and ∆Mt.
Step 3: Estimate the long-run (possible cointegrating) relationship
ˆ
VNI t
1
ˆ M
2
t
û t
(3.20)
ˆ VNI
2
t
û t
(3.21)
or
Mt
ˆ
1
and obtain the residuals (and name ECM) of these equations.
Step 4: Check for (cointegration) the order of integration of the residuals using
ADF tests or PP tests.
Step 5: Check for the optimal lag lengths (r and s) of each first-differenced
variable (∆VNIt and ∆Mt) in each VAR model.
12
An alternate is to use the quarterly time data, but this may substantially drop observations, and thus the
degrees of freedom.
37
Step 6: Test for the contemporaneous relationships (H0:
and ∆Mt (similar to equation 3.11):
0
0)13 between ∆VNIt
r
VNI t
0
VNI t
i
i
Mt
0
(3.22)
t
i 1
Step 7a: If the residual is a stationary series, and as the optimal lag lengths are
known, we estimate the error-correction models as below (similar to
equation 3.18):
r
VNI t
0
s
i
VNI t
i
Mt
j
ECM t
1
t
(3.23)
VNI t
j
ECM t
1
t
(3.24)
j
i 1
j 1
or
r
Mt
0
s
i
Mt
i
j
i 1
j 1
Step 7b: If the residual is a non-stationary series, and as the optimal lag lengths
are known, we estimate the conventional Granger causality models as
below (similar to equation 3.18, without ECM term):
r
VNI t
0
s
i
VNI t
i
Mt
j
t
(3.25)
VNI t
j
t
(3.26)
j
i 1
j 1
or
r
Mt
0
s
i
i 1
Mt
i
j
j 1
Step 8: Use Wald tests for short-run efficiency (H0: j = 0,
long-run efficiency (H0: is significant, but small).
j) and t tests for
Multivariate Analysis
Previous studies often conduct the multivariate analysis as the standard approach
to evaluate the robustness of bivariate results. It is generally argued that results
from bivariate analyses do not make a convincing case for causality between
variables. In particular, the results may be spurious due to the possibility that
relevant variables are being omitted from the regressions (Lutkepohl, 1982). In
the multivariate settings, the testing procedure is very similar to the bivariate
case, except for adding more relevant macroeconomic variables into each
regression equation. The specific explanatory variables will be discussed in
Section 4.3 of the next chapter.
13
This can be tested by using either student t statistic or F statistic.
38
CHAPTER 4
RESEARCH RESULTS
The analytical framework discussed in Chapter 3 clearly explains how we go on
analysing available data in order to test proposed research hypotheses. This
chapter builds upon the previous integration and cointegration tests to
appropriately specify a dynamic framework for assessing the interactions
between the stock prices and the macroeconomic variables of interest. In
particular, this thesis will proceed in the following ways. First, we will apply
various versions of ADF tests to check whether log-levels of all data series
contain unit roots as previous studies already implied. Second, if these variables
are non-stationary, we will test for their order of integration by applying the EG
approach14. Third, for cointegrated cases, we will implement the regression
equation (3.23) and (3.24); and for non-cointegrated cases, the error correction
term in equation (3.25) and (3.26), of which ECM term is omitted from the
regressions. Although our main focus is on the informational efficiency of stock
prices, we also evaluate the possible feedback from the stock prices to the
macroeconomic variables. The test results usually depend on the optimal lag
lengths, which are determined by the Akaike Information Criterion. Fourth, it is
generally argued that from bivariate analyses do not make a convincing case for
causality between variables because the results may be spurious due to the
possibility that relevant variables are being omitted from the regressions.
Therefore, this thesis will also extend the analysis to multivariate settings.
4.1 DESCRIPTIVE STATISTICS
This section will investigate the temporal properties of collected data series.
Instead of displaying our data in the traditional manner such as graph,
correlogram, and other statistics, we will concentrate on ADF and PP tests for
stationarity. Although they are not reported in this thesis, line graphs and
correlograms are being used as the first feeling of our data. From these views, we
believe that both VNI and twelve macroeconomic variables are non-stationary.
Table 4.1 reports the results of the ADF tests, the tests are implemented with
and without the time trend15. This table presents the results for the log-levels of
the data series, while Table 4.2 reports the results of ADF and PP tests for their
first differences. Figures in these tables are computed t (or exactly ) statistics.
Note that test results from Eviews respectively report critical t values of 1%, 5%,
14
This approach is introduced in Section 3.4.3.
15
Equations (3.1), (3.2), and (3.3).
39
and 10%. In addition, we have Eviews automatically select the optimal lag
lengths for each case.
 Table 4.1: Integration Tests (Log Levels)16
Variables
ADF Tests
None
No trend
Trend
VNI (VNIndex)
0.11
-1.65
CP (Consumer price)
1.33
-0.96
-1.34
IM (Import)
5.42**
1.77 (a)
2.26
-0.95
-2.55
-3.46***
-1.89 (a)
-12.08*
-0.44 (a)
-2.99
EX (Export)
3.02
-0.86
-2.69
FX (Exchange Rate)
2.04
-1.11
M1 (Money Supply)
2.15
0.33
1.86
0.96
0.44
-2.21
1.44
-0.90
2.06
0.09
FR (Reserves minus Gold)
1.75
-0.05
-2.15
-3.95**
-2.64 (a)
-3.45***
-1.58 (a)
-5.54*
-3.35*** (a)
-1.59 (b)
-4.94*
-3.47**(a)
-2.13 (b)
-3.25***
-2.46 (a)
-2.65
MR (Money Reserves)
2.87
1.19
-1.14
IP (Industrial Production)
M2 (Money Supply)
LR (Lending Rate)
DR (Deposit Rate)
DC (Domestic Credit)
Note: *, ** and *** denote significance at 1%, 5% and 10% levels respectively; and (a)
and (b) denote “Modified AIC” and “Modified SIC” respectively. Entries under
“Residual Tests” are statistics for testing the null hypothesis that the variable of
interest has a unit root.
The results from Table 4.1 consistently suggest that all time series considered
contain unit roots. That means they are all non-stationary time series. Some
points are worth making clear from these test results. With AIC criterion, we face
the mixed evidence of stationarity. That is, the null hypothesis of a unit root
cannot be rejected even at the 10% significant level using equations (3.1) and
(3.2) except industrial production. But the test results using equation (3.3)
indicate seven out of thirteen series are stationary. For this mixed evidence, we
try other information criteria. And the final results turn out to be consistent with
16
The test results for the December 2000 to June 2008 period are the same.
40
previous studies. With modified AIC and SIC, all variables are non-stationary
time series. Therefore, we must transform these variables into their first
differences for further investigation.
From the table 4.2, the null hypothesis for a unit root is rejected for all first
differenced series in most cases. In particular, the evidence from the PP tests
strongly supports the stationarity of some variables when their null hypothesis
cannot be rejected by ADF tests. This is similar to the case of Malaysian stock
market. Thus, the evidence seems consistently to suggest the stationarity of the
first-differenced series. In other words, these variables can be characterized as
I(1) variables. Because each set of variables are integrated of the same order, then
we can proceed with step two of the EG approach.
 Table 4.2: Integration Tests (First Differences)17
Variables
ADF Tests
None
No trend
Trend
IP (Industrial Production)
-6.59*
-1.19
-4.39* (c)
-1.98**
IM (Import)
-1.67***
EX (Export)
-1.78***
-6.56*
-1.96
-5.67* (c)
-6.59*
-2.28
-14.1* (a)
-5.82*
-6.52*
-2.38
-6.08* (c)
-6.74*
-2.37
-14.0* (a)
-3.81**
-9.49*
-0.73
-0.73 (a)
-0.19
-4.77*** (c)
-4.97*
-7.87*
-2.22
-4.23* (a)
-1.94
-8.70* (c)
-4.97*
-7.87*
-2.19
-8.23* (a)
-2.37
-8.91* (c)
-4.92*
-4.94*
-0.31
-4.87* (c)
-13.8*
-0.63
-10.16* (c)
-5.21*
-2.10
-8.63* (c)
-14.1*
-5.17*
-2.03
-8.77* (c)
-14.1*
-2.92**
-3.25***
VNI (VNIndex)
CP (Consumer price)
FX (Exchange Rate)
M1 (Money Supply)
M2 (Money Supply)
LR (Lending Rate)
DR (Deposit Rate)
DC (Domestic Credit)
FR (Reserves minus Gold)
MR (Money Reserves)
Note: *, ** and *** denote significance at 1%, 5% and 10% levels respectively; and (a)
and (c) denote “Modified AIC” and “Phillips-Perron Test” respectively. Entries under
“Residual Tests” are statistics for testing the null hypothesis that the first differenced
series of interest has a unit root.
17
The test results for the December 2000 to June 2008 period are the same.
41
Having concluded that each of the series is non-stationary, we proceed to
examine whether there exists a long-run equilibrium relationship between stock
prices and each macroeconomic variable of interest in a bivariate framework.
Following this framework, we first regress VNI to each macroeconomic variable,
and vice versa; save the residuals; then apply either ADF or PP tests for these
residual series. Similar to log-levels and first differences, we also view these
series by using line graphs and correlograms before conducting official tests.
Table 4.3 presents the results of both the ADF and PP tests for cointegration.
Surprisingly, we find evidence that, with both ADF and PP tests, the stock market
is cointegrated with all considered macroeconomic variables. These results are
lightly different from what Ibrahim (1999) had found in the Malaysian stock
market for the January 1977 to June 1996 period.
 Table 4.3: Bivariate Cointegration Tests (Residuals from each equation)18
Equation
VNI and Consumer Price
Residual Tests
ADF
PP
-2.48**
-1.76***
Cointegration
Yes/No
Yes
VNI and Industrial Production
-1.94***
-2.33**
Yes
VNI and Import
-1.88***
-2.01**
Yes
VNI and Export
-3.46*
-1.96**
Yes
VNI and Exchange Rate
-2.04**
-1.69***
Yes
VNI and M1
-2.81*
-1.71***
Yes
VNI and M2
-2.47**
-1.76***
Yes
VNI and Lending Rate
-2.38**
-2.03**
Yes
VNI and Deposit Rate
-2.22**
-1.78***
Yes
VNI and Domestic Credit
-2.44**
-1.77***
Yes
VNI and Reserves minus Gold
-2.12**
-1.88***
Yes
VNI and Money Reserves
-2.90*
-1.76***
Yes
Note: *, ** and *** denote significance at 1%, 5% and 10% levels respectively. Entries
under “Residual Tests” are statistics for testing the null hypothesis that the residual
series obtained from each bivariate model has a unit root.
These results indicate that the long-run relationship between these variables
and stock prices really exist in the Vietnamese stock market. These may also
imply that the presence of cointegration between the stock market on the one
hand and each macroeconomic variable on the other hand can significantly reject
the non-causality hypothesis between them. This finding seems to be slightly
18
The test results for the December 2000 to June 2008 period are the same.
42
different from the case of Malaysian case19. In the Vietnamese case, most
macroeconomic variables react to deviations from the long-run relationship.
Whether the stock market corrects for the disequilibrium remains to be
investigated, in which case it may be said that the Vietnamese stock market is, to
which extent, not informationally efficient in the long-run. The problem needed
is to further investigate how much long-run inefficient the stock market has
struggled. This depends on the significance and magnitude of coefficients in
the error correction models. Additionally, the dynamic interactions between these
variables need to be established to assess the informational efficiency of the stock
market in the short-run. These issues are dealt with in the next section.
4.2 BIVARIATE CAUSALITY TESTS
This section builds on the previous integration and cointegration tests to
appropriately specify a dynamic framework for assessing the interactions
between the stock prices and the macroeconomic variables of interest. In
particular, for cointegrated cases, we will implement the regression equations
(3.23) and (3.24). For non-cointegrated cases, however, the error correction term
in equation (3.25) and (3.26), ECM, is omitted from the regression. Accordingly,
the former becomes the central of this analysis in view of the above cointegration
tests. It is noted that although our main focus is on the informational efficiency of
stock prices, we also evaluate the possible feedback from the stock prices to the
macroeconomic variables. These are reported in the Table 4.4.
The first column of Table 4.4 presents two null hypotheses for each set of
variables: H01: Macroeconomic variable does not Granger cause stock prices,
and H02: Stock prices does not Granger cause macroeconomic variable. Although
our main focus is on H01, we also test H02. Testing these hypotheses will result in
one of the four patterns of causality: (1) unidirectional causality from
macroeconomic variable to stock price if H01 is rejected; (2) unidirectional
causality from stock price to macroeconomic variable if H02 is rejected; (3)
bidirectional causality between macroeconomic variable and stock price if both
H01 and H02 are rejected; and (4) no causality between macroeconomic variable
and stock price if both H01 and H02 are not rejected.
The second column of Table 4.4 presents optimal lag lengths of dependent
variable and independent variable in each VAR model (equation 3.18). Since we
have twelve macroeconomic variables, we have to run 24 VAR models. The first
equation of each VAR model is used to test whether macroeconomic variable
does Granger cause stock price; while the second equation of each VAR model is
used to test whether stock prices does Granger cause macroeconomic variable. In
order to determine the optimal lag lengths of dependent variable (or endogenous
variable), this thesis employs the “VAR estimation” technique using Eviews.
19
Three non-cointegrated cases (1977 to 1996 period).
43
 Table 4.4: Bivariate Causality Tests (all cointegrated series)21
Null Hypothesis
(a) CP dnc VNI
VNI dnc CP
(b) IP dnc VNI
VNI dnc IP
(c) IM dnc VNI
VNI dnc IM
(d) EX dnc VNI
VNI dnc EX
(e) FX dnc VNI
VNI dnc FX
(f) M1 dnc VNI
VNI dnc M1
(g) M2 dnc VNI
VNI dnc M2
(h) LR dnc VNI
VNI dnc LR
(i) DR dnc VNI
VNI dnc DR
(j) DC dnc VNI
VNI dnc DC
(k) FR dnc VNI
VNI dnc FR
(l) MR dnc VNI
VNI dnc MR
Optimal Lags
Dep.
Variable
12
25
12
14
12
18
12
20
12
22
12
24
12
25
12
9
12
8
12
24
12
4
12
12
Indep.
Variable
8
1
8
1
1
1
1
1
1
4
15
1
4
4
2
1
1
3
1
4
1
2
1
1
Granger Test
F Stats
1.71
0.10
1.56
0.16
0.42
0.03
2.28
0.56
3.81***
4.04*
2.60*
0.53
2.05***
2.58**
1.73
0.68
0.06
2.87**
0.84
1.57
0.21
1.13
0.48
1.50
2
ECM
Stats
13.7***
0.10
12.46
0.16
0.42
0.03
2.28
0.56
3.81***
16.15*
39.1*
0.53
8.18***
10.32**
3.47
0.68
0.06
8.62**
0.84
6.28
0.21
2.26
0.48
1.50
-0.05
-0.02***
-0.07**
-0.21*
-0.06***
-0.12**
-0.07**
-0.07
-0.05**
0.05
-0.04
-0.02
-0.08**
-0.001
-0.04***
-0.08***
-0.04
-0.07***
-0.08**
0.005
-0.07**
-0.03
-0.08**
0.001
Note: *, ** and *** denote significance at 1%, 5% and 10% levels respectively. The
term “dnc” means “does not Granger cause”. Entries under “Granger Tests” are F
statistics and 2 statistics for testing the null hypothesis that the coefficients‟ sums of
causal variables are zero. And the underlying presents the existence of contemporaneous
relationship at 15% level of significance.
The selection criteria for the appropriate lag lengths of the unrestricted VAR
models employ F-tests and 2 tests. The selection procedure involves choosing
the VAR(p) model with the highest absolute value22 of AIC, SIC or the Hannan–
Quinn (HQ) and lowest value of final prediction error (FPE). In practice, the use
of SIC is likely to result in selecting a lower order VAR model than when using
the AIC. Similar to previous studies, this thesis employs the AIC criterion. Once
21
This test results are for the December 2000 to June 2009 period.
22
Because AIC, SIC, and HQ usually have the negative values due to the logarithmic forms.
44
the appropriate lag lengths of dependent variable in each VAR model are
selected, we then apply the simple-to-specific approach to determine the
appropriate lag lengths of independent variable on the basic of AIC criterion.
The third column of Table 4.4 presents the Granger causality test results, as
discussed in equation (3.9). These tests are simply conducted by Wald –
Coefficient Restriction test in Eviews. The test procedure is done as the following
steps (for each set of VNI and a macroeconomic variable):
Step 1: Using Eviews, regress ∆VNIt on lagged ∆VNI terms plus lagged ∆M
terms as in the following model:
r
VNI t
s
a1
i
VNI t
i
i 1
j
Mt
j
yt
(4.1)
j 1
Step 2: Using Eviews, set the null hypothesis as below:
s
H0 :
0 or M t does not Granger cause VNI t
j
j 1
Step 3: Compare either the computed F statistic (or computed 2 statistic) with
its corresponding critical F statistic (or critical 2 statistic) at the chosen
level of significance; or the p-value with the chosen level of significance.
Step 4: Using Eviews, regress ∆Mt on lagged ∆M terms plus lagged ∆VNI terms
as in the following model:
r
Mt
s
a1
i
i 1
Mt
i
j
VNI t
j
yt
(4.2)
j 1
Step 5: Using Eviews, set the null hypothesis as below:
s
H0 :
j
0 or VNI t does not Granger cause M t
j 1
Step 6: Compare either the computed F statistic (or computed 2 statistic) with
its corresponding critical F statistic (or critical 2 statistic) at the chosen
level of significance; or the p-value with the chosen level of significance.
The final column of Table 4.4 presents the ECM test results. In this table, we just
provide coefficients (as presented in equations 3.23 and 3.24).From Table 4.4,
we may note four important points from the regressions.
45
First, three out of twelve macroeconomic variables, specifically consumer
prices, M2 money supply, and foreign exchange rates, have the contemporaneous
relationships with the stock prices.
Second, the bivariate causality test results show that two groups of
macroeconomic variables have different impacts on the stock prices. Group one,
including industrial production, imports, exports, lending rates, deposit rates,
domestic credit, foreign currency reserves, and money reserves, largely indicates
that the lagged changes in macroeconomic variables have no significant
predictive ability for the movements in stock prices. The null hypothesis that the
macroeconomic variables do not Granger cause stock prices is not rejected even
at the 10% significance level. It is noted that, except for consumer prices, the test
results appear to be consistent with both test statistics, F and 2. For consumer
prices, the null hypothesis that it does not cause stock prices in Granger sense is
rejected at the 10% significance level using the 2 statistic. Accordingly,
excluding consumer prices from this group is appropriate. However, the
contemporaneous information about these variables is not linked to stock market
returns because the hypothesis that H0: 0 0 is rejected even at the 15% level of
significance. Thus, the failure of lagged economic values to Granger cause stock
market returns should not be interpreted as implying that the stock market is
semi-strongly efficient with respect to this list of variables. In other words, the
stock market appears to divorce from reality of these economic activities.
For group two, including foreign exchange rates, M1 money supply, M2
money supply, and consumer prices as well, the lagged changes in
macroeconomic variables really have significant predictive ability for the
movements in stock prices. Saying differently, the null hypothesis for these cases
is rejected at the 10% significance level. The results from this bivariate analysis,
along with the existence of contemporaneous relationships, suggest that, in the
short-run, the Vietnamese stock market is not informationally efficient with
respect to consumer prices, foreign exchange rates, and M2 money supply. For
this reason, we expect that investors who traded on announcements of consumer
prices, foreign exchange rates, and M2 money supply might be able to earn
predictably positive returns23. For the M1 money supply, the fact that its current
values are not linked to current equity market returns reinforces the suggestion
that the market is also not efficient as conventionally understood. These findings
are likely to be in accordance with the newly-emerging equity markets in Central
Europe ten years ago24.
Third, we observe that stock price movements anticipate variations in the
foreign exchange rates and the M2 money supply. This results seem to be
23
We have not conducted a formal analysis of whether potential trading gains are sufficient to
compensate for transactions costs but the magnitude of the relationships suggests that they are.
24
Poland, Hungary, Czech Republic, and Slovak Republic.
46
somewhat consistent with what Ibrahim (1999) had found in the Malaysian stock
market for the last decade. The striking difference between the two markets is
that the Vietnamese stock prices do not provide any predictive power for real
industrial production. This finding is not consistent with the hypothesis that stock
prices contain the market participants‟ expectations of future real activities. For
this reason, the Vietnamese stock market may not do its best as the capital
mobilization for the economy. Saying differently, investors may not consider the
stock market as their long-run investment channel. Besides the contemporaneous
relationships, the causal link from stock prices to M2 money supply may reflect
the importance of the stock market on the M2 money demand. We can explain
this special link as following. While functioning its role as a capital mobilization
channel, the stock market may attract a majority of idle money from society.
Accordingly, the stock prices may, to which extent, provide predictive power for
the increase in the M2 money supply. This finding is consistent with the
hypothesis that stock prices contain the market participants‟ expectations of
future monetary policies. In other words, these findings may indicate the policy
reaction of the monetary authorities to the fluctuation in stock prices. Lastly, the
effects on the exchange rates of stock price changes possibly suggest portfolio
adjustments taken by investors.
Fourth, most coefficients of the error correction term reinforce our findings of
cointegration between stock prices and macroeconomic variables, except for the
consumer prices, the M1 money supply, and the deposit rates. They are signed as
expected and are significant in at least one equation. Specifically, the results
suggest that deviations from the equilibrium path are adjusted by about 4%–8%
the next month through the movements in stock prices. From the estimates, the
adjustment toward the long-run relationship is extremely slow. This means that,
after any shock that forces the stock prices from their long-run values it takes a
long time for the prices to return to their equilibrium values if there is no opposite
shock to counter the initial shock. Although the results indicate the need for
intervention in such a case, they also point to the danger of creating policy shocks
because it may take time for a certain policy to come into effect.
The relative efficiency of the Vietnamese stock market appears to have shifted
greatly over time. In other words, the financial crisis really has impact on the
efficiency pattern of the stock market. Table 4.5 contains the same analysis as
Table 4.4, except restricted to the period up to June 2008 (before the financial
crisis). The test results indicate the relationships are very different from what we
have found above. First, all lag lengths have significantly changed. If we
compare with Central Europe markets, the Vietnamese stock market is unstable.
The memory ability of the post-crisis stock market lasts longer. Second, the
market, to somewhat lesser extent, appears to be moving backwards. Before the
crisis, the Vietnamese stock market may have possessed elements of semi-strong
efficiency. In particular, the stock market is informationally efficient with respect
to M2 money supply, lending rates, and deposit rates. Speaking differently, the
stock market is informationally efficient with respect to monetary variables.
47
 Table 4.5: Bivariate Causality Tests (all cointegrated series)25
Null Hypothesis
(a) CP dnc VNI
VNI dnc CP
(b) IP dnc VNI
VNI dnc IP
(c) IM dnc VNI
VNI dnc IM
(d) EX dnc VNI
VNI dnc EX
(e) FX dnc VNI
VNI dnc FX
(f) M1 dnc VNI
VNI dnc M1
(g) M2 dnc VNI
VNI dnc M2
(h) LR dnc VNI
VNI dnc LR
(i) DR dnc VNI
VNI dnc DR
(j) DC dnc VNI
VNI dnc DC
(k) FR dnc VNI
VNI dnc FR
(l) MR dnc VNI
VNI dnc MR
Optimal Lags
Dep.
Variable
1
14
1
12
1
13
1
18
1
10
1
16
1
1
1
3
1
3
1
15
1
1
1
12
Indep.
Variable
12
10
8
1
8
1
8
1
8
1
8
1
8
2
8
4
8
12
8
1
8
4
12
1
Granger Test
F Stats
1.14
1.40
1.47
0.08
1.92***
0.04
1.05
0.48
2.47**
5.47**
2.63**
4.27**
1.59
4.04**
1.15
2.26***
0.98
2.14**
1.49
3.80***
0.15
1.72
2.35**
0.90
2
ECM
Stats
13.73
14.03
11.74
0.08
15.33**
0.04
8.41
0.48
19.72**
5.47**
21.03**
4.27**
12.7
8.09**
9.18
9.03***
7.83
25.7**
11.93
3.80***
1.23
6.69
28.16*
0.90
-0.04
-0.001
-0.04
-0.06
-0.03
-0.10***
-0.05***
0.05
-0.03
-0.04
-0.04
-0.05
-0.06***
0.003
-0.04***
0.05
-0.03
-0.04
-0.05***
-0.01
-0.04
-0.06
-0.04
-0.05
Note: *, ** and *** denote significance at 1%, 5% and 10% levels respectively. The
term “dnc” means “does not Granger cause. Entries under “Granger Tests” are F
statistics and 2 statistics for testing the null hypothesis that the coefficients‟ sums of
causal variables are zero. ”. And the underlying presents the existence of
contemporaneous relationship at 15% level of significance.
But it seems to disappear due to impact of the financial crisis. Third, the stock
prices had played an important role on almost all monetary variables, while the
investors could not able to trade on the announcements of monetary information.
This feature is very significant for the economic stabilization policies. Fourth, the
real economic activity seems to have impact on the stock market because the
significant causal link from the imports to the stock prices may contain the
market participants‟ expectations. Fifth, only four macroeconomic variables,
25
This test results are for the December 2000 to June 2008 period.
48
including exports, M2 money supply, lending rates, and domestic credit, indicate
the long-run relationship with stock prices; and the stock prices just have the
long-run relationship with imports. This may imply that the isolation between the
stock market and the economic reality has been established before the crisis. And
this could, to which extent, result in the fact that both the stock market and the
economy have weakly struggled against the attack of the financial crisis. In other
words, before the financial crisis, there are no significant adjustment processes
which could prevent the errors in the long-run relationships becoming larger and
larger. In terms of ECM analysis, we may say that during the crisis, the stock
market and the economy have started to look together. If this is a case, we may
expect that the future prospect of the stock market turn out to be much better.
In summary, while our bivariate analysis suggests that the Vietnamese stock
market seems to be informationally inefficient in both short- and long-run.
Generally, the stock market seems to divorce from the most part of the economy.
It is still possible for a “professional” trader to make abnormal returns by
analyzing good or bad news contained in some macroeconomic variables. The
findings re-assure that the Vietnamese stock market is not well functioning in
scarce resource allocation and attractive enough to encourage foreign investors.
In fact, investors of all kinds in the market only concern their short-term
portfolios. In addition, instead of becoming more efficient over time, as one
might expect, the Vietnamese stock market appears to have become increasingly
divorced from reality. This is because the stock market used to be informationally
efficient with respect to some monetary variables. Furthermore, the bivariate
analysis also confirms that the financial crisis has significant impact on the
efficiency pattern of the Vietnamese stock market.
4.3 MULTIVARIATE GRANGER TESTS
According to Lutkepohl (1982), it is generally argued that results from bivariate
analyses do not make a convincing case for causality between variables. In
particular, the results may be spurious due to the possibility that relevant
variables are being omitted from the regressions. Accordingly, to evaluate the
robustness of our previous bivariate results, this section extends the analysis to
multivariate settings.
To form the most appropriate multivariate framework from thirteen variables
– the stock price index and the twelve macroeconomic variables, we treat the M1
money supply (M1), the M2 money supply (M2), and the domestic credit (DC) as
alternative monetary quantity variables; the lending rates (LR) and the deposit
rates (DR) as alternative monetary price variables; and the imports (IM) and the
exports (EX) as alternative trade variables. This treatment is technically
supported by using the correlation coefficient matrix. Specifically, those
variables (in first difference form) with correlation coefficients of higher than 0.7
49
are grouped together26. Thus we have totally twelve multivariate models
consisting of nine variables each. Since the analysis is intended as a robustness
check of our previous bivariate evidence against market efficiency, we focus only
on the stock return equations.
For cointegration tests, we also conduct ADF and PP procedures. The results
of these tests are reported in Table 4.6 (12/2000–6/2009 period) and Table 4.7
(12/2000–6/2008 period) for twelve alternative multivariate models. As the tables
indicate, the null hypothesis of non-cointegration between the variables is
rejected in all cases at conventional significance levels. Thus, the tests suggest a
unique cointegrating vector that constrains the long-run movements of the
variables under consideration. For this reason, to assess the informational
efficiency of the stock market in multivariate settings, we estimate a stock return
equation using error correction model for each set of the variables.
 Table 4.6: Multivariate Cointegration Tests (2000M12 to 2009M6)
Equation
Residual Tests
Cointegration
ADF
PP
Yes/No
1. VNI, CP, IP, FX, FR, MR, IM, LR, M1
-3.69*
-3.71**
Yes
2. VNI, CP, IP, FX, FR, MR, IM, LR, M2
-3.97*
-4.02*
Yes
3. VNI, CP, IP, FX, FR, MR, IM, LR, DC
-3.66*
-3.29*
Yes
4. VNI, CP, IP, FX, FR, MR, IM, DR, M1
-3.69*
-3.70*
Yes
5. VNI, CP, IP, FX, FR, MR, IM, DR, M2
-3.45*
-3.54*
Yes
6. VNI, CP, IP, FX, FR, MR, IM, DR, DC
-3.36*
-3.51*
Yes
7. VNI, CP, IP, FX, FR, MR, EX, LR, M1
-3.69*
-3.83**
Yes
8. VNI, CP, IP, FX, FR, MR, EX, LR, M2
-3.82*
-4.01*
Yes
9. VNI, CP, IP, FX, FR, MR, EX, LR, DC
-3.74*
-3.85*
Yes
10. VNI, CP, IP, FX, FR, MR, EX, DR, M1
-3.62*
-3.78*
Yes
11. VNI, CP, IP, FX, FR, MR, EX, DR, M2
-3.37**
-3.59*
Yes
12. VNI, CP, IP, FX, FR, MR, EX, DR, DC
-3.82*
-3.93*
Yes
Note: * and ** denote significance at 1% and 5% levels respectively. Entries under
“Residual Tests” are statistics for testing the null hypothesis that the residual series
obtained from each multivariate model has a unit root.
26
This is not explicitly reported here.
50
 Table 4.7: Multivariate Cointegration Tests (2000M12 to 2008M6)
Equation
Residual Tests
Cointegration
ADF
PP
Yes/No
1. VNI, CP, IP, FX, FR, MR, IM, LR, M1
-3.42*
-3.74*
Yes
2. VNI, CP, IP, FX, FR, MR, IM, LR, M2
-2.96*
-3.75*
Yes
3. VNI, CP, IP, FX, FR, MR, IM, LR, DC
-3.66*
-3.50*
Yes
4. VNI, CP, IP, FX, FR, MR, IM, DR, M1
-3.35*
-3.35*
Yes
5. VNI, CP, IP, FX, FR, MR, IM, DR, M2
-2.57**
-3.27*
Yes
6. VNI, CP, IP, FX, FR, MR, IM, DR, DC
-3.03*
-3.63*
Yes
7. VNI, CP, IP, FX, FR, MR, EX, LR, M1
-3.27*
-3.47*
Yes
8. VNI, CP, IP, FX, FR, MR, EX, LR, M2
-2.77***
-3.07*
Yes
9. VNI, CP, IP, FX, FR, MR, EX, LR, DC
-3.37*
-3.50**
Yes
10. VNI, CP, IP, FX, FR, MR, EX, DR, M1
-3.31*
-3.49*
Yes
11. VNI, CP, IP, FX, FR, MR, EX, DR, M2
-2.91**
-3.24*
Yes
12. VNI, CP, IP, FX, FR, MR, EX, DR, DC
-4.04*
-4.12*
Yes
Note: * and ** denote significance at 1% and 5% levels respectively. Entries under
“Residual Tests” are statistics for testing the null hypothesis that the residual series
obtained from each multivariate model has a unit root.
As with the bivariate cases, we employ the AIC criterion to determine the lag
lengths of the changes in variables appearing on the right-hand-side of the stock
return equation. It is noted that since each equation contains lagged firstdifferenced terms of variables as explanatory variables, searching over all
possible combinations of lag lengths simultaneously would be burdensome. Most
previous studies suggest the following procedure. We first regress the stock
return on its own lags, varying the lag lengths from 1 to 12 (for monthly data),
then select the lag length that minimizes the AIC or FPE criterion. Then, we
extend the regression to bivariate cases by considering independently the
remaining eight variables with the lag length of the stock return being fixed at its
previously determined lag length. The optimal lag lengths of these variables are
then determined using the AIC or FPE criterion, among which we choose a
variable that results in the smallest AIC or FPE to be added first into the
regression. If the inclusion of lagged values of any variable does not lead to a
reduction in the AIC or FPE, then that variable is dropped from the regression.
Once the bivariate regression is specified, we then consider the remaining
variables in the same manner. The steps are repeated until all variables are
considered, added to or omitted from the regression.
However, since we have only 103 observations (2000M12 to 2009M6) or 91
observations (2000M12 to 2008M6), we cannot apply the above suggested
procedures for our case. Luckily, the “vector error correction” in Eviews
51
becomes the best alternative solution. Specifically, we can change the lag length
of all variables in each multivariate model, and then choose the best one with
smallest AIC criterion. The final specification that we obtain from this procedure
is used for our analysis. The final regressions for each period are reported in
Table 4.8 and Table 4.9.
These test results indicate three important points. First, they strengthen our
previous findings from the bivariate analyses. Specifically, the financial crisis
really has impact on the efficiency pattern of the stock market. Before the crisis,
the Vietnamese stock market may be semi-strongly efficient with respect to
monetary variables. Second, the stock return still reacts to deviations from the
long-run equilibrium path. The estimates indicate that 7% - 19% of the deviations
are corrected the next month by the market movements. Thus, the adjustments
seem to take place relatively faster than those in the bivariate cases. These
responses of the stock prices to the long-run disequilibrium, again, suggest
temporal causality from macroeconomic variables to the stock market in the long
run. Third, real economic activity seems not to be completely divorced from the
stock market. In both periods, lagged values of the industrial production, imports,
and exports, to which extent, turn out to have impact on the current stock prices.
And thus it is still possible for a “professional” trader to make abnormal returns
by analyzing good or bad news contained in some macroeconomic variables. In
summary, the multivariate evidence is in line with our conclusion that the
Vietnamese stock market is not informationally efficient.
52
 Table 4.8: Multivariate Granger Tests (2000M12 to 2009M6)
Granger Tests
Equation
ECM
CP
IP
M1
M2
LR
DR
IM
EX
FX
FR
MR
DC
Eq.1 [1]
0.73
7.57*
0.10
-
4.23**
-
7.36*
-
0.66
0.26
0.02
-
-0.119*
Eq.2 [1]
0.02
5.77**
-
0.006
8.53*
-
10.34*
-
1.46
0.013
0.804
-
-0.189*
Eq.3 [1]
0.74
1.73
-
-
7.11*
-
5.99**
-
2.64***
0.46
2.66***
5.63**
-0.076*
Eq.4 [1]
0.01
7.43*
0.01
-
-
0.56
6.41**
-
0.96
0.01
0.05
-
-0.135*
Eq.5 [1]
0.18
4.34**
-
0.03
-
1.77
10.99*
-
0.96
0.001
0.41
-
-0.177*
Eq.6 [1]
0.01
1.11
-
-
-
2.58***
6.54*
-
1.20
1.04
3.12***
6.77*
-0.087*
Eq.7 [1]
0.08
6.59*
0.21
-
4.11**
-
-
3.15***
0.41
0.18
0.004
-
-0.11**
Eq.8 [1]
0.001
6.67*
-
0.05
5.72**
-
-
4.99**
0.99
0.18
0.39
-
-0.166*
Eq.9 [1]
0.50
2.39
-
-
5.93**
-
-
3.43***
1.83
0.25
2.35
7.49*
-0.071*
Eq.10 [1]
0.05
7.06*
0.04
-
-
1.80
-
4.60**
0.11
0.00
0.07
-
-0.109**
Eq.11 [1]
0.33
5.94**
-
0.00
-
2.61***
-
8.43*
0.21
0.005
0.627
-
-0.165*
Eq.12 [1]
0.001
2.42
-
-
-
3.55***
-
6.73*
0.55
0.31
2.16
8.11*
-0.068*
Note: *, **, and *** denote significance at 1%, 5%, and 10% levels respectively. The numbers in squared brackets are the optimal lag lengths
determined by the AIC criterion. Entries under “Granger Tests” are 2 statistics for testing the null hypothesis that the coefficients‟ sums of causal
variables of interest are zero. And the underlying presents the existence of contemporaneous relationship at 10% level of significance.
53
 Table 4.9: Multivariate Granger Tests (2000M12 to 2008M6)
Granger Tests
Equation
ECM
CP
IP
M1
M2
LR
DR
IM
EX
FX
FR
MR
DC
Eq.1 [2]
0.65
5.33***
0.33
-
7.17**
-
3.68
-
6.07**
0.63
1.120
-
-0.106*
Eq.2 [1]
1.65
6.55*
-
0.08
3.48***
-
3.65***
-
0.85
0.96
0.057
-
-0.244*
Eq.3 [2]
1.86
1.93
-
-
5.56***
-
3.66
-
10.16*
0.33
3.22
1.13
-0.026*
Eq.4 [1]
2.27***
5.29**
0.65
-
-
0.00
7.67*
-
1.08
0.28
0.01
-
-0.200*
Eq.5 [1]
0.88
3.98**
-
0.46
-
0.01
10.44*
-
0.75
0.03
1.22
-
-0.211*
Eq.6 [1]
3.55***
0.81
-
-
-
0.13
6.89*
-
0.19
0.35
3.97**
2.38
-0.183*
Eq.7 [1]
1.43
9.23*
0.07
-
0.05
-
-
0.00
2.22
0.74
0.00
-
-0.138*
Eq.8 [1]
0.29
8.77*
-
0.00
0.61
-
-
1.05
1.14
0.57
0.49
-
-0.183*
Eq.9 [1]
1.83
3.59***
-
-
0.21
-
-
0.60
0.11
0.00
1.58
3.71***
-0.063*
Eq.10 [1]
0.16
9.09*
0.00
-
-
0.12
-
2.31
1.65
0.08
1.25
-
-0.179*
Eq.11 [1]
0.66
10.63*
-
0.00
-
0.14
-
6.54*
1.19
0.05
2.79***
-
-0.196*
Eq.12 [1]
0.64
2.43
-
-
-
0.57
-
7.04*
0.02
1.43
7.78*
8.33*
-0.132*
Note: *, **, and *** denote significance at 1%, 5%, and 10% levels respectively. The numbers in squared brackets are the optimal lag lengths
determined by the AIC criterion. Entries under “Granger Tests” are 2 statistics for testing the null hypothesis that the coefficients‟ sums of causal
variables of interest are zero. And the underlying presents the existence of contemporaneous relationship at 10% level of significance.
54
CHAPTER 5
CONCLUSION AND POLICY
RECOMMENDATION
This chapter will integrate what we have tested to answer the research questions,
and to solve the research problem raised in the first chapter. In particular, this
chapter is organized in the following sequences. We will first summarize the
main findings of the thesis. Some specific suggestions to policy makers are then
discussed regarding to what we have really found. Last, but not least, some
limitations for further studies are the most important findings of this thesis.
5.1 MAIN FINDINGS
This thesis employs the cointegration approach to Granger causality tests to
investigate whether the Vietnamese stock market exhibits the publicly
informational efficiency. Specifically, we investigate the lead-lag relationship
between stock prices and twelve macroeconomic variables for Vietnam. Data are
collected from three official sources, namely, Thomson Reuters, Bloomberg and
International Monetary Fund during the December 2000 to June 2009 period.
Generally, the test results can, to which extent, help us answer the research
questions. First, “past” changes in macroeconomic variables explain “current”
changes in stock prices, because the Vietnamese stock market is not
informationally efficient. Second, it is correct to say that the Vietnamese stock
market takes a long time to fully adjust previous shocks in the economy because
the coefficients are very small. Third, the stock market movements cannot be
used as a leading indicator in helping formulating current economic stabilization
policies in Vietnam, because inverse causal relationships are rarely significant.
Fourth, the financial crisis makes the inefficiency of the Vietnamese stock market
worse, because the stock market appears to be efficient with respect to monetary
variables prior to the crisis and it may disappear after that.
Specifically, the results from bivariate analysis suggest that the Vietnamese
stock market is not informationally efficient in both short- and long-run. The
stock market seems to even divorce from the most part of the economy. It is still
possible for a “professional” trader to make abnormal returns by analyzing good
or bad news contained in some macroeconomic variables. The findings re-assure
that the Vietnamese stock market is not well functioning in scarce resource
allocation and attractive enough to encourage foreign investors. In fact, investors
of all kinds in the market only concern their short-term portfolios (or so-called
speculators). This phenomenon is potentially risky for the economy because any
shock can result in a simultaneous capital outflow. This may, in turn, create
pressures on the balance of payment. For the market is not informationally
55
efficient, especially with respect to monetary variables, it may be dangerous for
policy makers to realize the role of monetary policies. However, in the investors‟
point of view, fundamental analysis is still significant for their investment
decisions. Thus, companies with strong equity analysts would have higher
comparative advantages in this inefficient market. In addition, instead of
becoming more efficient over time, as one might expect, the Vietnamese stock
market appears to have become increasingly divorced from reality. This also
reveals that the last financial crisis has serious impact on the Vietnamese stock
market. The results from multivariate analysis reinforce the fact that the
Vietnamese stock market is not informationally efficient.
5.2 POLICY IMPLICATIONS
The informational inefficiency implies that the scarce resources have not been
allocated into the best competing uses. In an inefficient stock market where stock
prices do not reflect the real value of the firms, well-managed firms may be
mistakenly undervalued by the market and, therefore, find it difficult to raise
funds, while fundamentally dysfunctional firms may be overvalued and easily
attract investors‟ funds. This “lemon-hazard” problem may result in not only
misallocating resources, but also encouraging speculation. An efficient market
can eliminate price distortions and channel funds to the most efficiently managed
firms. This is extremely significant for a „capital starved‟ economy like Vietnam.
To properly perform its function of resource allocation, the Vietnamese stock
market should be efficiently improved in terms of publicly information, of which
three supplementary levels are specially considered.
First, macroeconomic information disclosure should be promptly and
accurately promulgated. Most Vietnamese macroeconomic data officially
published by the General Statistics Office (GSO), International Monetary Fund
(IMF), and others often lag at least six to nine months27. More importantly, many
macroeconomic variables have never been widely reported, especially
government expenditures, balance of payments, and employment. In this
situation, traders who have absolute advantages in accessing “confidential”
information could dominate the market, and thus make abnormal returns by
analyzing good or bad news contained in these leakages. Inversely, uninformed
investors blindly act on others‟ decisions, formulating herding behavior. This
finding implies that an improvement of national statistical system is significantly
important. We should change our traditional wisdom that only public agencies
need macroeconomic variables for research and policy analysis. In financial
sector, however, everyone does.
Second, dissemination of information relating to listed companies is
necessarily improved by means of good corporate governance and market
transparency. These practices play an important role in protecting shareholders
27
This can be easily checked by exploring the IMF-CD ROM, World Development Indicators CD-ROM,
or the Vietnam GSO website.
56
and developing the market soundly. The current status of unclear and unreliable
disclosure can create invisible barriers for lasting the inefficiency longer. As
we‟ve already learned from the literature review that the probability of finding
inefficiency in an asset market increase as the transactions and information cost
of exploiting the inefficiency increases (Proposition 2, Section 2.3). The poor
information from listed companies, along with unavailable macroeconomic news,
turns out to be opportunities for some well-informed investors making prices.
Therefore, we suggest that most listed companies must adequately, accurately
and promptly announce all information updates about their management,
performance, and financial health on their websites. To attract foreign investors,
bilingual website should even become one of the compulsory requirements of
listed companies. In addition, some accounting principles need be properly
adjusted in accordance with international best practices.
Third, it is a need to promote liquidity of the stock market in order to perform
its role as a real capital mobilization channel. It is said that the probability of
finding inefficiencies in an asset market decreases as the ease of trading on the
asset increases (Proposition 1, Section 2.3). In doing so, we should scale up the
quantity of, and increase the quality of, and to diversify various kinds of goods,
including derivatives and corporate bonds, in order to meet the demands of the
market is: (i) to speed up the program on equitization of state-owned enterprises
and corporations, economic groups and state-owned commercial banks; to attach
equitization to listing in the securities market; and to expand the issue of new
shares to mobilize capital on the market. (ii) to request equitized enterprises
satisfying the listing conditions to be listed; to concurrently review and continue
the sale of state capital in enterprises in which the State is not required holding
controlling shares. (iii) to convert foreign invested enterprises into shareholding
companies with their shares to be listed and traded in the securities market.
However, experiences from other emerging markets suggest that the sequencing
of steps is critical to eventual success and that gradualism may be preferable to a
„big bang‟ approach. That means we should build the stock market slowly and
deliberately, adopting rules and procedures and only gradually allowing
companies that comply with listing requirements to trade.
Different from developed markets, the Vietnamese stock market is being led
by individual investors (account for 70-80% trading value, Thomson Reuters).
These investors are mostly characterized by short-run investments and unequable
mind. Consequently, the market is then characterized by high volatility and risk.
This risky environment shall lead to two remarkable dangers. First, investors,
especially institutional and foreign ones usually price stocks lower than they are
really worth. Second, the newly listed companies are often undervalued, and this
problem might be an impediment of the equalization process. Therefore, it is
vitally important to develop institutional investors such as investment funds.
Accordingly, we suggest that only investors who have sufficient conditions such
as capital and knowledge can independently join the market. Along with this
restriction, some special incentives for institutional investors can help reduce a
great number of uninformed individual investors.
57
5.3 FURTHER STUDIES
This research results also provide a good reference for those who are interested in
the efficient market hypothesis. First, it is worth conducting a similar research
using the HNX Index. If one does so, we will have the whole picture of the
Vietnamese stock markets. Second, it is necessary to conduct a comparative
research across ASEAN markets so that we can know what we can do to compete
with other regional markets in attracting foreign investors. Third, it is interesting
to conduct a Granger causality research using international stock price indices in
order to know whether the Vietnamese stock market has opened to the outside
financial world. Fourth, instead of employing the EG approach; one can use the
Johansen approach to analyzing the cointegration in multiple equations. Finally,
it is necessary to examining how to improve the efficiency of the Vietnamese
stock market in coming years for giving policy recommendation in detail.
58
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