(Luận văn thạc sĩ) empirical investigation of efficient market hypothesis in vietnam stock market

MINISTRY OF EDUCATION AND TRAINING
UNIVERSITY OF ECONOMICS HOCHIMINH CITY

LÊ ĐẶNG BÍCH THẢO

EMPIRICAL INVESTIGATION OF EFFICIENT MARKET
HYPOTHESIS IN VIETNAM STOCK MARKET

MASTER THESIS

Ho Chi Minh City 2011


MINISTRY OF EDUCATION AND TRAINING
UNIVERSITY OF ECONOMICS HOCHIMINH CITY

LÊ ĐẶNG BÍCH THẢO

EMPIRICAL INVESTIGATION OF EFFICIENT MARKET
HYPOTHESIS IN VIETNAM STOCK MARKET

MAJOR: BANKING AND FINANCE
MAJOR CODE: 60.31.12

MASTER THESIS

Supervisor: Dr Võ Xuân Vinh

Ho Chi Minh City 2011

Acknowledgement
I would like to express my heartfelt gratitude and deepest appreciation to my
research Supervisor, Dr. Vo Xuan Vinh for his precious guidance, share of
experience, ceaseless encouragement and highly valuable advice and comments
throughout the course of my research.

I would like to thank many of my friends in our group from ebanking class, who
have been sharing experience during doing research: Ms. Nguyen Thi Kim Ngan,
Ms. Tran Thuy Huyen, Ms. Do Ngoc Anh, Mr. Ta Thu Tin, Ms. Pham Thi Tuyet
Trinh.

My special gratitude is extended to all instructors and staff at Faculty of Banking
and Finance Postgraduate Faculty, University of Economics HoChiMinh City
(UEH) for their support and the valuable knowledge during my study in UEH.

Finally, the deepest and most sincere gratitude goes to my parents, my sisters for
their love and support. Fulfilling this goal would not have been possible without
them.

i


Abstract
This research examines the efficiency of Vietnam stock market at weak form level
by using daily and weekly observations of market index and eight selected stocks of
real estate and seafood processing companies for the period from 2007 to 2010.
Parametric and nonparametric tests including auto correlation test, run test, variance
ratio test, regression test, ARCH, GARCH (1,1) have been employed in this study.
All tests’ results fail to support the hypothesis of weak form efficiency with daily
data, even in case, returns are adjusted for thin trading. However, with weekly data,

results obtained from run test and autocorrelation test do not completely reject
hypothesis of weak form efficiency while result given from variance ratio test fully
provides evidence against a random walk. Besides that, the findings of no clear
calendar effect by examining day of week effect also give the evidence that even if
the anomalies existed in the sample period, the practitioners who implement
strategies to take advantage of anomalous behavior can cause the anomalies to
disappear.

Keywords: efficient market hypothesis, randomness, calendar effect

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Table of contents
Acknowledgement.......................................................................................................i
Abstract ......................................................................................................................ii
Table of contents.......................................................................................................iii
List of tables ............................................................................................................... v
Abbreviations ............................................................................................................vi
1. INTRODUCTION ................................................................................................. 1
2. LITERATURE REVIEW ..................................................................................... 5
2.1. The theory of Efficiency Market Hypothesis .................................................. 5
2.2. Review of Literature on Weak Form Market Efficiency................................. 7
2.2.1. Evidence from developed markets................................................................ 8
2.2.2. Evidence from developing markets ............................................................ 10
3.

DATA AND METHODOLOGY .................................................................... 14
3.1. Data Description ............................................................................................ 14
3.2. Methodology.................................................................................................. 17

3.2.1. Auto Correlation Test............................................................................. 17
3.2.2. Run test ................................................................................................... 19
3.2.3. Variance ratio test ................................................................................... 20
3.2.4. Calendar effect ........................................................................................ 23
3.2.5. Thin trading adjustment .......................................................................... 25
3.2.6. Robustness check .................................................................................... 26

4.

EMPIRICAL RESULT ................................................................................... 27
4.1. Autocorrelation Test ...................................................................................... 27
4.2. Runs test......................................................................................................... 34
4.3. Variance ratio test .......................................................................................... 38
4.4. Day of week effects ....................................................................................... 44

iii


5. CONCLUSION .................................................................................................... 48
REFERENCES ........................................................................................................ 50
Appendix .................................................................................................................. 56
Table A. 1. Summary results of all tests for daily returns in 2007........................... 56
Table A. 2. Summary results of all tests for thin trading adjusted daily returns in
2007 .......................................................................................................................... 56
Table A. 3. Summary results of all tests for daily returns in 2008........................... 57
Table A. 4. Summary results of all tests for thin trading adjusted daily returns in
2008 .......................................................................................................................... 57
Table A. 5. Summary results of all tests for daily returns in 2009........................... 58
Table A. 6. Summary results of all tests for thin trading adjusted daily returns in
2009 .......................................................................................................................... 58

Table A. 7. Summary results of all tests for daily returns in 2010........................... 59
Table A. 8. Summary results of all tests for thin trading adjusted daily returns in
2010 .....................................................................................................................................59

iv


List of tables
Table 3.1. Descriptive statistics of daily return........................................................ 15
Table 3.2. Descriptive statistics of weekly return .................................................... 15
Table 4.1. Results of autocorrelation coefficients and Ljung-Box Q statistics for
daily returns........................................................................................... 29
Table 4.2. Results of autocorrelation coefficients and Ljung-Box Q statistics for thin
trading adjusted daily returns................................................................ 31
Table 4.3. Results of autocorrelation coefficients and Ljung-Box Q statistics for
weekly returns ....................................................................................... 32
Table 4.4. Results of autocorrelation coefficients and Ljung-Box Q statistics for thin
trading adjusted weekly returns ............................................................ 33
Table 4.5. Results of run test for daily price & return ............................................. 36
Table 4.6. Results of run test for weekly price & return ......................................... 37
Table 4.7. Variance ratio test results for daily returns under homoscedasticity and
heteroscedasticity. ................................................................................. 40
Table 4.8. Variance ratio test results for thin trading adjusted daily returns under
homoscedasticity and heteroscedasticity. ............................................. 41
Table 4.9. Variance ratio test results for weekly returns under homoscedasticity and
heteroscedasticity. ................................................................................. 42
Table 4.10. Variance ratio test results for thin trading adjusted weekly returns under
homoscedasticity and heteroscedasticity. ............................................. 43
Table 4.11. Results of OSL and GARCH (1,1) models for daily returns ................ 46
Table 4.12. Results of OSL and GARCH (1,1) models for thin trading adjusted

daily returns........................................................................................... 47

v


Abbreviations

ABT

:

Ben tre Aqua product Import And Export Joint Stock Company

AGF

:

An Giang Fisheries Import and Export Joint Stock Company

ARCH

:

Autoregressive conditionally heteroscedastic

CII

:

Ho Chi Minh City Infrastructure Investment Joint Stock Company

EMH

:

Efficient Market Hypothesis

GARCH :

Generalised Autoregressive Conditional Heteroscedasticity

FMC

:

Sao Ta Foods Joint Stock Company

HOSE

:

Ho Chi Minh Stock Exchange

ITA

:

Tan Tao Investment Industry Corporation

OSL

:

Ordinary Least Standard

SJS

:

Song Da Urban & Industrial Zone Investment and Development Joint

Stock Company
TDH

:

Thu Duc Housing Development Corporation

TS4

:

Seafood Joint Stock Company No 4

vi


1. INTRODUCTION
Efficient Market Hypothesis (EMH) has been a popular topic for empirical research
since the introduction of market efficiency theory by Fama (1965). There are many

studies examining whether the stock markets in both developed and emerging
countries behave in line with the Efficient Market Hypothesis. Most of them
focused on weak form efficiency, the lowest level of Efficient Market Hypothesis
and the results are mixed. On the one hand, some studies reject the hypothesis that
the stock markets are in the weak form efficiency (Hoque et al., 2007, Abeysekera,
2001b, Lima et al., 2004). On the other hand, some papers provide the evidence that
stock markets in some countries are efficient (Chan et al., 1997, Lee, 1992,
Worthington et al., 2004).

Although there are many empirical studies devoted to testing for the weak form of
Efficient Market Hypothesis in developed and emerging stock markets, there are not
many studies examining the weak form of market efficiency in stock returns in
Vietnam market. The objective of this study is to investigate the existence of weak
form of market efficiency in stock returns in Vietnam, and whether there are any
anomalies existing in Vietnam stock market. The discovery of anomalous patterns
in stock returns can help investors take advantage of continuing to hold and adjust
their buying and selling strategies accordingly to increase their returns by timing the
market.

Since the establishment on 28 July 2000 with the first security trading center in Ho
Chi Minh City (hereinafter called Hose) and only two listed companies that are
Refrigeration Electrical Engineering Joint Stock Company (REE) and Saigon Cable
and Telecommunication Material Joint Stock Company (SACOM), Vietnam stock
market has continued to develop successfully by facing all the challenges and
difficulties. Over ten years of operation, the total number listed companies have
increased significantly to 635 companies with a total market capitalization of VND

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650.150 billions (Hose VND 523.933 billions, HNX VND121.217billions). The
market capitalization to GDP ratio has been increased year by year. It goes up from
0.24% in 2000 to 0.37% GDP in 2010. There are 102 securities companies licensed
with a total registered capital of VND 31,866 billion (USD 1,528 million). Total
trading accounts are about 1,031,000 (including the 15,000 trading stock accounts
of foreign investors), compared to the 2,908 accounts in 2000. The high and rapid
growth of Vietnam stock market is, of course, very appealing to domestic and
foreign investors.

Although Vietnam stock market has developed rapidly and taken liberalization
process recently, it still possesses many of features that are characteristics of
emerging markets like more information asymmetry, thin trading and weak
institutional infrastructure, which all together could cause market inefficiency.
However, not all of emerging markets are entirely inefficient such as some
researchers who find the evidence to support the weak form efficiency in
developing countries: Lima et al.(2004) found that Hong Kong and A shares for
both the Shanghai, Shenzhen stocks exchanges are in weak form efficiency.
Dickinson et al.(1994) also provided the evidence that Nairobi Stock Exchange is
behave in line with the market efficiency and Moustafa (2004) also supported the
weak form Efficiency Market Hypothesis of United Arab Emirates stock market…
Hence, considering the theoretical and practical significance, the testable
implications and conflicting empirical evidence of random walk hypothesis
motivate us to have a fresh look at this issue of weak form efficiency in the context
of an emerging market, namely Vietnam stock market.

This study focuses on testing the weak form market efficiency and some anomalies
existing in Vietnam stock market. To analyze this issue, we require a decomposition
of daily and weekly return of Vnindex and shares in real estate and seafood
processing companies in Ho Chi Minh stock exchange from Jan 2007 to Dec 2010

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and examine whether the successive stock prices or returns are independently and
identically distributed. Past stock price has no predictive content to forecast future
stock price (Fama, 1970). We will then adjust the data for thin (infrequent) trading
that is an important characteristic of Vietnam stock market and that could seriously
bias the results of empirical studies on market efficiency.

The research provides a number of complementary testing procedures for random
walk or weak form market efficiency which have been widely used in the literature.
We also perform various tests to examine market efficiency in the weak form,
which focus on the information conveyed by past price. In particular, we use the
parametric serial correlation test of independence which measures the relationship
of the current stock return and its value in the previous period. We then use run test,
a nonparametric test, which is computed to test the randomness of stock return.
Furthermore, the variance ratio test which is proposed by Lo and Mackinlay (1988)
is carried out to check whether uncorrelated increments exist in the series, under the
assumption of homoscedastic and heteroscedastic random walk. Finally, we use the
ordinary least standard (OSL), Autoregressive conditionally heteroscedastic
(ARCH), Generalised Autoregressive conditional Heteroscedasticity (GARCH(1,1))
models which have been widely employed in the literature to explore calendar
anomalies existing in Ho Chi Minh stock market.

By using the latest data, more observations and conducting several robustness
checks with the same methodology, our findings are consistent with the previous
results of Loc (2006) which report that Vietnam stock market is inefficient in the
weak form with daily data. However, the extent of inefficiency of Vietnam stock
market decreases when the weekly observations are employed in our study.
Moreover, our research also employs the calendar effect which explores the

calendar anomalies in Vietnam market. The result of calendar effect especially day
of week does not exist in Vietnam stock market during the studied period.

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Consequentially, this does not support the findings of Loc (2006) that the day of
week effect existing in Vietnam stock market as negative Tuesday effect.

The first contribution of our research is that this is one of the studies in Vietnam
applying new econometrics, new methodology which has been affected the Brooks’
(2008) methodology. This study also has take advantages of all models which have
been tested in the previous literatures. The second contribution of this study is to
provide evidence against persistent patterns in anomaly in Vietnam stock market.
Then, this study also enhances the established literature by providing the most
recent analysis of our stock market.

The remainder of this study is structured as follows. Section two reports the relevant
theoretical background to the research and reviews the previous empirical evidences
on weak form efficiency in developed and emerging countries. Section three
describes the data and methodology. Section four presents the empirical research.
Finally, section five summarizes the results of the study, draws conclusions and
provides suggestions for further research.

4


2. LITERATURE REVIEW
2.1 . The theory of Efficiency Market Hypothesis

The Efficient Market Hypothesis is a concept of informational efficiency, and refers
to market’s ability to process information into prices. The ideas of Efficient Market
Hypothesis appear as early as the beginning of twentieth century in the theoretical
contribution of Bachelier (1900) who laid the foundation for random walk
hypothesis of market efficiency. However, it was until the 1960s, Samuelson (1965)
has been developed the theoretical framework for the random walk and Fama
(1965) finds supportive evidence of the random walk hypothesis that successive
price changes are independent. The Efficient Market Hypothesis has been emerged
from the combination of empirical findings of Fama (1965) and theory of
Samuelson (1965).

Fama (1970) summarizes this idea in his classic survey by writing: "A market in
which prices always 'fully reflect' available information is called 'efficient'."
According to this hypothesis, in an informatively efficient market, price changes
must be unforecastable. Since news is announced randomly, price must fluctuate
randomly. Consequently, it states that it is not possible to exploit any information
set to predict future price change. In his early paper, Noble prize winner Fama
(1970) suggests that the tests of efficient markets could be subdivided into three
categories: weak form test, semi strong form test and strong form test efficiency and
each category dealing with a different type of information.

The weak form test is the lowest level of efficiency. A capital market is said to
satisfy weak form efficiency if the current stock prices fully incorporate the
information in past stock prices. Hence, trader can not make abnormal returns based
on the predication of past stock prices. The semi strong form efficiency indicates

5


that the current stock prices including all information known to all market

participants. Hence, this reflects all public available information such as the
information on stock splits, annual reports; new security issues… Trader can not get
the abnormal returns by analyzing the annual reports or available public
information. Finally, strong form test of the efficient market theory tests whether
private or confidential information is fully reflected in security prices. The current
prices of stock including all information known to any market participant including
the public and private information, this assumption hardly exists in reality, so the
strong form of market efficiency is not very likely to hold. Hence, no trader would
be able to get abnormal return above the average investor even if he was given new
information.

Fama (1970) also introduces three models for testing stock market efficiency
including: the Expected Return or fair game model, the submartingale model, and
the Random Walk model. In this study, we only concentrate on the random walk
model which is more powerful in support of the EMH than tests of the fair game
model and submartingale model. The Efficient Market Hypothesis is associated
with the idea of a “random walk”. The logic of the random walk idea is that if the
flow of information is unimpeded and information is immediately reflected in stock
prices, then tomorrow’s price changes will reflect only tomorrow’s news and will be
independent of the price changes today. But news is by definition unpredictable
and, thus, resulting price changes must be unpredictable and random. Hence, prices
fully reflect all known information, and even uninformed investors buying a
diversified portfolio at the tableau of prices given by the market will obtain a rate of
return as generous as that achieved by the experts. However, in an efficient market,
price changes must be a response only to new information. Since information
arrives randomly, share prices must also fluctuate unpredictably. The Random Walk
model can be stated in the following equation:

6

Pt +1 = Pt + ε t +1

(2.1)

where: Pt +1 : price of share at time t+1;
Pt

: price of share at time t;

ε t +1 : random error with zero mean and finite variance.

The equation indicates that the price of a share at time t+1 is equal to the price of a
share at time t plus given value that depends on the new information (unpredictable)
arriving between time t and t+1. In other words, the change of price ε t +1 = Pt +1 − Pt is
independent of past price changes.

2.2 . Review of Literature on Weak Form Market Efficiency

There is a large and growing literature concerning the validity of random walk
hypothesis with respect to stock markets in both developed and developing
countries. However, the empirical research produce mixed results. Most early
studies are supportive weak forms of Efficient Market Hypothesis in developed
capital markets. Recent studies, however, document that stock market returns are
predictable. This section provides a review of the literature on the weak form
efficiency in both developed and developing countries.
Methodologically, testing the weak form efficiency used the random walk model
which is widely employed in the preceding literature. Practically, several statistical
techniques, runs test, unit root test, serial correlation test, and variance ratio test,
are commonly used for testing weak form efficiency. Specially, the run test is used

in the literature of Fama (1965), Sharma Kennedy (1977), Cooper (1982), Chiat et
al. (1983), Wong et al. (1984), Yalawar (1988), Ko and Lee (1991), Butler and
Malaikah (1992), Abraham (2002), Worthington and Higgs (2004), Squalli (2006);
Daraghma et al. (2009). Also, the serial correlation test of returns has also been used
extensively by Kendell (1953), and Fama (1965), Fama and French (1988),
Worthington et al. (2004), Squalli (2006). And the unit root test used by David and

7


MacKinlay (1988), Worthington et al. (2004). And the variance ratio test also used
by Dockery and Vergari (1997), Grieb and Reyes (1999); Alam et al. (1999); Chang
et al. (2000); Cheung et al. (2001); Abraham et al. (2002); Seddighi et al. (2004),
Loc (2006), Hafiz et al (2007). In this study we use all tests that mentioned above
(Run test, serial correlation, and variance ratio test, regression test, ARCH;
GARCH(1,1)) to enhance the findings of this study.
2

.

2

.

1

.

Evidence from developed markets

The empirical papers in developed markets generally have similar conclusions that
support the weak form efficiency. Groenewold (1997) conducts weak and semi
strong efficiency tests of Australian stock market by using aggregate share price
indexes and finds that the results are consistent with the weak form efficiency. In
addition, Hudson et al (1996) find that the technical trading rules have predictive
power but not sufficient to enable excess return in United Kingdom market.

Lee (1992) employs variance ratio test to examine whether weekly stock returns of
the United States and ten industrialized countries: Australia, Belgium, Canada,
France, Italy, Japan, Netherlands, Switzerland, United Kingdom, and Germany
follow random walk process for the period from 1967 to 1988. He finds that the
random walk model is still appropriate characterization of weekly return series for
majority of these countries.

Ayadi et al. (1994) apply variance ratio test to examine the efficiency hypothesis of
Korean Stock exchange for the period from 1984 to 1988. Under the assumption of
homoscedasticity, the authors reject the random walk hypothesis. However, under
the heteroscedasticity, they could not reject the random walk for daily data. In
addition, they also employ the weekly, monthly, 60 day and 90 day interval data.
The results also could not reject the random walk hypothesis.

8


Chan et al (1997) examine the weak form and the cross country market efficiency
hypothesis of 18 international stock markets, including Australia, Belgium, Canada,
Denmark, Finland, France, Germany, India, Italy, Japan, Netherlands, Norway,
Pakistan, Spain, Sweden, Switzerland, the United Kingdom, and the United States
for the period from 1962 to 1992. They conclude that all stock markets in the
sample are individually weak form efficient and only a small number of stock

markets show evidence of co-integration with others by using Phillips-Peron (PP)
unit root and Johansen’s co-integration tests.

C.Cheung et al.(2001) employ variance ratio tests with both homoscedasticity and
heteroscedasticity to examine random walk hypothesis for Hang Seng Index on
Hong Kong Stock Exchange for period from 1985 to 1997. They conduct that Hang
Seng follows a random walk model and consequently that the index is weak form
efficient.

Worthington et al (2004) investigate random walk in 16 developed markets and four
emerging stock markets for the period from 1987 to 2003. By using various
methods including serial correlation, runs, three types of unit root test and multiple
variance ratio tests, the paper’s result indicates that the random walk hypothesis is
not rejected in major European developed markets. Particular, Germany and
Netherlands are weak form efficient under both serial correlation and runs tests,
while Ireland, Portugal and the United Kingdom are efficient under one test or the
other. Thus, rests of the markets do not follow a random walk. The ADF and
Phillips-Perron unit root tests reject the null hypothesis of random walk in the all 20
emerging and developed markets, while the KPSS unit root tests fail to reject the
null hypothesis excluding the Netherlands, Portugal and Poland. Under the variance
ratio test, the null hypothesis of homoscedasticity and heteroskedasticity are not
rejected in the United Kingdom, Germany, Ireland, Hungary, Portugal and Sweden.
The rejection of the null hypothesis of the homoscedasticity but not the

9


heteroscedasticity is found for France, Finland, Netherlands, Norway and Spain.
Among the emerging markets, only Hungary satisfies the strictest requirements for
a random walk in daily returns.

In a more recent research, Kima et al (2008) examine efficiency of stock prices of
group Asian markets. The weekly, daily data from 1990 are considered in this
study. By using new multiple variance ratio tests, it is found that the Hong Kong,
Japanese, Korean and Taiwanese markets are efficient in the weak form. The other
markets of Indonesia, Malaysia and Philippines are shown no sign of market
efficiency. Singapore and Thai markets become efficient after the Asian crisis.

2.2.2. Evidence from developing markets
In contrast with the evidence from developed markets, the findings of weak form
efficiency on developing markets are mixed. Most of developing countries suffer
with the problem of thin trading. In addition, in smaller markets, it is easier for large
traders to manipulate the market. Though it is generally believe that the developing
countries are less efficient. However, the empirical evidence does not always
support this thought. Many papers report weak form efficiency in developing
countries. Lima et al. (2004) employ data of the daily stock price indexes of
Shanghai, Shenzhen (China), Hong Kong, and Singapore Stock exchange over the
period from 1992 to 2000. They find that the Hong Kong and A shares for both the
Shanghai, Shenzhen stocks exchanges are in weak form efficiency.

Dickinson et al. (1994) also examine Nairobi Stock Exchange using the
autocorrelation and runs tests. Their data include weekly prices of the 30 most
actively traded stocks from 1979 to 1989. The results also support the weak form of
Efficient Market Hypothesis in Nairobi Stock Exchange.

10


Mojustafa (2004) examines the behavior of stock prices in United Arab Emirate
market by using the nonparametric runs to test randomness. The data consists of

daily prices of 43 stocks for the period from 2001 to 2003. The results reveal that 40
stocks out of the 43 are random. Hence, this supports the weak form Efficiency
Market Hypothesis.

In more recent research, Oskooe et al.(2010) examine the random walk hypothesis
in Iran stock market. By applying Augmented Dickey Fuller, Philip-Perron,
Kwiatkowski, Phillips, Schmidt and Shin and one structural break perron unit root
tests for the period from 1999 to 2009. The results from the various unit root tests
imply that the Iran daily stock price index follow the random walks process.

Many authors, however, argue that markets of the developing countries are in the
weak form inefficiency. Mobarek et al. (2000) study the efficiency of the
Bangladesh Security on the Dhaka Stock Exchange by using the autocorrelation,
run test for the period of 1988 to 1997. Basing on the result of runs and the
autocorrelation tests, the authors argue that the returns of Dhaka stock market do
not follow random walks.

Abeysekera (2001) indicates that the Colombo Stock Exchange (CSE) in Sri Lanka
is weak form inefficient by using the serial correlation, runs and unit root tests for
the period from 1991 to 1996. The findings of three tests consistently reject the
random walk hypothesis. The author also examines a day of the week and month of
the year effect on the CSE, but neither effect found to be on the stock market in Sri
Lanka.

Smith et al.(2003) examine the random walk hypothesis for five medium size
European emerging stock markets by using the multiple variance ratio tests for the
period from 1991 to 1998. The findings of Greece, Hungary, Poland, Portugal

11

markets are fail to support the hypothesis of random walk because the returns are
auto correlated. In Turkey, however, the Istanbul stock market follows a random
walk.

Abrosimova et al (2002) test weak form efficiency in Russian stock market ranging
from 1995 to 2001 by employing unit root, autocorrelation and variance ratio tests.
The results of both autocorrelation and variance ratio tests reject the hypothesis of
the random walk for daily and weekly, but not for monthly data. For monthly data,
the variance ratio under assumption of heteroscedasticity increments the hypothesis
of random walk can not be rejected.

Hoque et al. (2007) examine the random walk hypothesis for eight emerging equity
markets in Asia including Hong Kong, Indonesia, Korea, Malaysia, the Philippines,
Singapore, Taiwan and Thailand from 1990 to 2004. The result of variance ratio test
indicates that the stock prices of eight Asian countries do not follow the random
walk with the exceptions of Taiwan and Korea.

Abrim et al.(2009) employ the data of 35 stocks listed in the Palestine Security
stock exchange (hereinafter call PSE) to investigate whether the Palestine Security
stock exchange is of weak form efficiency by using autocorrelation test, unit root
test and run test. This paper’s result indicates that the PSE is inefficient at the weak
from all test results.

The findings from more recent research by Abdmoulah (2010) documents that the
stock market in Arab is not weak form efficiency by using the Garch M (1,1) model
implemented for 11 Arab stock markets including daily prices of the national
indexes of Saudi Arabia, Kuwait, Tunisia, Dubai, Egypt, Qatar, Jordan, AbuDhabi,
Bahrain, Morocco and Oman for periods ending in March 2009.

12


Overall, the empirical results from both developed and developing markets show
contrasting evidence on weak form efficiency. Especially, results of whether or not
emerging markets follow a random walk are rather conflicting. Mixed results from
literature on emerging stock markets efficiency are not surprising since it is
observed that emerging stock markets are generally less efficient than developed
markets. In addition, with the characteristic as high level of liquidity and trading
activity, substantial market depth and low information asymmetry, developed
markets are seem to be in the weak form efficiency market while most of
developing markets are characterized as more information asymmetry, lower
volume and frequency of trading (thin trading) and weak institutional infrastructure,
settlement delays, weaker disclosure and accounting requirement, which all together
could cause market inefficiency (Islam et al., 2005). However, not all of developing
markets are necessarily entirely inefficient such as Hong Kong (Lima et al., 2004),
Nairobi Stock Exchange (Dickinson et al., 1994), United Arab Emirate (UAE)
(Moustafa, 2004), Iran stock market (Oskooe et al., 2010).

Although there are many authors study about the market efficiency for both
developed and developing markets. However, there are not many researches
empirically investigating the market efficiency in Vietnam. In lieu of the current
literature, Loc et al. (2010) employ the weekly price of the market index and the
five oldest stocks listed at Ho Chi Minh stock exchange for the period from 2000 to
2004. The result from autocorrelation test, run and variance ratio tests indicate that
the Vietnam stock market is inefficiency in the weak form.

13

3. DATA AND METHODOLOGY
3.1. Data Description
The employed data in this study consists of time series (daily and weekly
frequency) of Vietnam stock market index and stock price in real estate and seafood
processing companies for the period from 2007 to 2010. All data is obtained from
electronic database from the website cophieu68.com. A total of 996 daily and 202
weekly observations for market index and individual stock are obtained. Vnindex is
selected as a representative for Vietnam stock market index to be studied in this
research. The stocks in real estate and seafood processing companies are chosen
because stocks in real estate sector are highly sensitive to any change in the
economy while the stocks in seafood processing industry are stable and less
changeable. Hence, some real estate stocks including CII, ITA, SJS, TDH which
listed before 2007 to be selected for studying in this literature. The oldest seafood
processing stocks including ABT, AGF, TS4, FMC also employed in the study as
those stocks listed before 2007 at Hose.
Returns are calculated as Rt = ln( Pt / Pt −1 )

(3.1)

Where Rt is return at time t, Pt and Pt-1 are price at time t and t-1 respectively.
In this study, we follow previous empirical works and employ the most familiar
econometrics methods that used in the literature to test the independence of prices
data. The study applies parametric and non-parametric methods to test the random
walk hypothesis. In particular, we use the parametric serial correlation test which
measures the relationship of the current stock return and its value in the previous
period. We will then use the run test, a nonparametric test, which is computed to
test the randomness of stock return. Furthermore, variance ratio test which is
proposed by Lo and Mackinlay (1988) will be carried out to check whether
uncorrelated increments exist in the series, under the assumption of homoscedastic

14


and heteroscedastic random walks. Finally, the OSL, ARCH, GARCH(1,1) models
have been employed in the literature to explore the calendar anomalies existing in
Ho Chi Minh Stock exchange.

Table 3. 1 Descriptive statistics of daily returns

Share

ABT

TS4

FMC

AGF

TDH

SJS

ITA

CII

VNINDEX

Observations 992

990

991

992

992

993

993

993

996

Mean

-0.0001

0.0005

-0.0014

-0.0013

-0.0009

0.0006

-0.0002

0.0003

-0.0004

Median

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0001

Maximum

0.0489

0.0491

0.0489

0.0488

0.0488

0.0488

0.0488

0.0488

0.0465

Minimum

-0.0513

-0.05156

-0.05856

-0.0513

-0.05133

-0.0513

-0.05132

-0.05129

-0.04802

Std. Dev.

0.0255

0.03434

0.02893

0.02745

0.02866

0.03098

0.02994

0.02764

0.01939

Skewness

0.0018

-0.0113

0.0285

0.0906

0.0801

0.0022

0.0230

0.0192

-0.03244

Kurtosis

2.5786

1.67762

2.08469

2.30382

2.23305

1.90021

2.11803

2.28742

2.92193

Jarque-Bera 7.34030** 72.15447*** 34.72820*** 21.39102*** 25.37381*** 50.04514*** 32.27168*** 21.06988*** 0.42763

Note: ***, ** and * denote a significance level of 1%, 5% and 10% respectively

Table 3. 2 Descriptive statistics of weekly returns

Share
Observations
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
Jarque-Bera

ABT
TS4
201
200
-0.0009
0.0017

0.0000
-0.0087
0.1455
0.2424
-0.2459
-0.2459
0.0640
0.1058
-0.5419
0.1692
4.0181
2.6302
18.5197*** 2.0939

FMC
201
-0.0078
-0.0073
0.2343
-0.2089
0.0715
0.0904
3.8685
6.5908**

AGF
201
-0.0067
-0.0091
0.2113

-0.1671
0.0715
0.3352
3.6033
6.8138**

TDH
201
-0.0042
-0.0095
0.2339
-0.2433
0.0765
0.1837
3.8466
7.1340**

SJS
201
0.0030
-0.0063
0.2276
-0.2427
0.0943
0.1547
2.9262
0.8474

ITA
201

-0.0010
-0.0016
0.2372
-0.1973
0.0844
0.2754
3.4604
4.3161

CII
201
0.0008
0.0000
0.2373
-0.1906
0.0742
0.3171
3.6433
6.8338**

VNINDEX
202
-0.0023
-0.0037
0.1332
-0.1626
0.0538
-0.1065
3.2305
0.8291

Note: ***, ** and * denote a significance level of 1%, 5% and 10% respectively

15


Table 3.1 presents a summary of descriptive statistics of the daily returns for
Vnindex and eight individual stocks returns. Sample means, maximums, minimums,
standard deviations, skewness, kurtosis and Jacque-Bera statistics and p-values are
reported. It can be seen that except TS4 (0.0005), SJS (0.0006), CII (0.0003), all
indexes have the negative mean of return. The lowest minimum return is in FMC
(-0.05856) while the highest maximum return is TS4 (0.04905). The standard
deviations of returns range from 0.01939 (Vnindex) to 0.03434 (TS4).
By and large, the statistics shows that the returns of Vnindex and all stocks are not
normal distributed. Given that the parameters skewness and kurtosis represent the
standardised fourth and third moments of a distribution. These parameters are used
with Jarque-Bera statistics to indicate whether a data set is normally distributed or
not. Skewness measures the extent to which a distribution is not symmetric about its
mean value. The skewness of the normal distribution is zero. Positive skewness
means that the distribution has a long right tail and negative skewness implies that
the distribution has a long left tail (Oskooe et al., 2010). Table 3.1 shows that the
returns of all stocks except Vnindex, TS4 are positive skewed although the
skewness statistics are not large. The positive skewness implies that the return of
distributions of the shares traded on the exchanges have a long right tail of large
values and hence a higher probability of earning positive returns.

Moreover, Kurtosis measures the peakness or flatness of the distribution of the
series. The kurtosis of the normal distribution is three. If the kurtosis exceeds three,
the distribution is peaked which is indicating as leptokurtic; if the kurtosis is less
than three, the distribution is flat, this is indicating as platykurtic. The kurtosis value

of all stocks and Vnindex are smaller than three, different from that of a normal
distribution, there by indicating the platykurtic frequency distribution of all stocks
return series.

16


Finally, the calculated Jarque-Bera statistics and corresponding p-values in table 3.1
are used to test the null hypothesis that the daily distribution of all stock market
returns is normally distributed. All p-values are smaller than the 0.01 level of
significance suggesting the null hypothesis can be rejected. Therefore, none of these
return series is then well approximated by normal distribution (Chen et al., 2001).

The weekly returns are calculated from the stock prices from Wednesday’s closing
price. If the following Wednesday price is not available, then the Thursday price (or
Tuesday if Thursday is not available) is used. If both Tuesday and Thursday prices
are not available, the return for that week is reported as missing. The choice of
Wednesday price aims to avoid the effects of weekend trading and to minimize the
number of holidays. Table 3.2 presents a summary of descriptive statistics of the
weekly returns for Vnindex and eight individual stocks returns. By the same
analysis with daily return, the weekly returns do not normally distributed.
3.2. Methodology
3.2.1. Auto Correlation Test
Autocorrelation test is the most common test which has been used as the first tool
for testing of either dependence or independence of random variables. The
Autocorrelation measures the correlation coefficient between the values of a
random variable at time t and its value in the previous period. In particular the
autocorrelation measures the relationship between the current stock return and its
value in the previous period. Hence, this test is employed in many empirical studies
(Mobarek et al., 2000, Abraham, 2002, Dickinson et al., 1994, Groenewold, 1997,

Lima et al., 2004, Islam et al., 2005, Loc et al., 2010). It is calculated as:
N −k

ρk =

∑ (r − r )(r

t +k

t

− r)

t =1

(3.2)

N

∑ (r − r )

2

t

t =1

17