Empirical investigation of efficient market hypothesis in vietnam stock market

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 ofreal estate and seafoo

MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HOCHIMINH CITY

MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HOCHIMINH CITY

Supervisor: Dr Võ Xuân Vinh

Ho Chi Minh City 2011

I would like to express my heartfelt gratitude and deepest appreciation to myresearch Supervisor, Dr Vo Xuan Vinh for his precious guidance, share ofexperience, ceaseless encouragement and highly valuable advice and commentsthroughout the course of my research

I would like to thank many of my friends in our group from ebanking class, whohave 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 TuyetTrinh

My special gratitude is extended to all instructors and staff at Faculty of Bankingand 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 fortheir love and support Fulfilling this goal would not have been possible withoutthem

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 ofreal estate and seafood processing companies for the period from 2007 to 2010.Parametric and nonparametric tests including auto correlation test, run test, varianceratio 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 dailydata, even in case, returns are adjusted for thin trading However, with weekly data,results obtained from run test and autocorrelation test do not completely rejecthypothesis of weak form efficiency while result given from variance ratio test fullyprovides evidence against a random walk Besides that, the findings of no clearcalendar effect by examining day of week effect also give the evidence that even ifthe anomalies existed in the sample period, the practitioners who implementstrategies to take advantage of anomalous behavior can cause the anomalies todisappear

Keywords: efficient market hypothesis, randomness, calendar effect

Table of contents

Acknowledgement

Abstract

Table of contents

List of tables

Abbreviations

1 INTRODUCTION

2 LITERATURE REVIEW

2.1 The theory of Efficiency Market Hypothesis

2.2 Review of Literature on Weak Form Market Efficiency

2.2.1 Evidence from developed markets

2.2.2 Evidence from developing markets

3.DATA AND METHODOLOGY

3.1 Data Description

3.2 Methodology

3.2.1 Auto Correlation Test

3.2.2 Run test

3.2.3 Variance ratio test

3.2.4 Calendar effect

3.2.5 Thin trading adjustment

3.2.6 Robustness check

4.EMPIRICAL RESULT

4.1 Autocorrelation Test

4.2 Runs test

4.3 Variance ratio test 4.4 .Dayofweekeffects

5 CONCLUSION

REFERENCES

Appendix

Table A 1 Summary results of all tests for daily returns in 2007

Table A 2 Summary results of all tests for thin trading adjusted daily returns in 2007

Table A 3 Summary results of all tests for daily returns in 2008

Table A 4 Summary results of all tests for thin trading adjusted daily returns in 2008

Table A 5 Summary results of all tests for daily returns in 2009

Table A 6 Summary results of all tests for thin trading adjusted daily returns in 2009

Table A 7 Summary results of all tests for daily returns in 2010

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

List of tablesTable 3.1 Descriptive statistics of daily return 15Table 3.2 Descriptive statistics of weekly return 15Table 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 31Table 4.3 Results of autocorrelation coefficients and Ljung-Box Q statistics for

weekly returns 32Table 4.4 Results of autocorrelation coefficients and Ljung-Box Q statistics for thin

trading adjusted weekly returns 33Table 4.5 Results of run test for daily price & return 36Table 4.6 Results of run test for weekly price & return 37Table 4.7 Variance ratio test results for daily returns under homoscedasticity and

heteroscedasticity 40Table 4.8 Variance ratio test results for thin trading adjusted daily returns under

homoscedasticity and heteroscedasticity. 41Table 4.9 Variance ratio test results for weekly returns under homoscedasticity and

heteroscedasticity 42Table 4.10 Variance ratio test results for thin trading adjusted weekly returns under

homoscedasticity and heteroscedasticity. 43Table 4.11 Results of OSL and GARCH (1,1) models for daily returns 46Table 4.12 Results of OSL and GARCH (1,1) models for thin trading adjusted

daily returns 47

Stock Company

1 INTRODUCTION

Efficient Market Hypothesis (EMH) has been a popular topic for empirical researchsince the introduction of market efficiency theory by Fama (1965) There are manystudies examining whether the stock markets in both developed and emergingcountries behave in line with the Efficient Market Hypothesis Most of themfocused on weak form efficiency, the lowest level of Efficient Market Hypothesisand the results are mixed On the one hand, some studies reject the hypothesis thatthe 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 thatstock 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 ofEfficient Market Hypothesis in developed and emerging stock markets, there are notmany studies examining the weak form of market efficiency in stock returns inVietnam market The objective of this study is to investigate the existence of weakform of market efficiency in stock returns in Vietnam, and whether there are anyanomalies existing in Vietnam stock market The discovery of anomalous patterns instock returns can help investors take advantage of continuing to hold and adjusttheir buying and selling strategies accordingly to increase their returns by timing themarket

Since the establishment on 28 July 2000 with the first security trading center in HoChi Minh City (hereinafter called Hose) and only two listed companies that areRefrigeration Electrical Engineering Joint Stock Company (REE) and Saigon Cableand Telecommunication Material Joint Stock Company (SACOM), Vietnam stockmarket has continued to develop successfully by facing all the challenges anddifficulties Over ten years of operation, the total number listed companies haveincreased significantly to 635 companies with a total market capitalization of VND

650.150 billions (Hose VND 523.933 billions, HNX VND121.217billions) Themarket capitalization to GDP ratio has been increased year by year It goes up from0.24% in 2000 to 0.37% GDP in 2010 There are 102 securities companies licensedwith a total registered capital of VND 31,866 billion (USD 1,528 million) Totaltrading 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 rapidgrowth of Vietnam stock market is, of course, very appealing to domestic andforeign investors

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

This study focuses on testing the weak form market efficiency and some anomaliesexisting 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 seafoodprocessing companies in Ho Chi Minh stock exchange from Jan 2007 to Dec 2010

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

The research provides a number of complementary testing procedures for randomwalk 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, whichfocus on the information conveyed by past price In particular, we use theparametric 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 theassumption of homoscedastic and heteroscedastic random walk Finally, we use theordinary 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 calendaranomalies existing in Ho Chi Minh stock market

By using the latest data, more observations and conducting several robustnesschecks with the same methodology, our findings are consistent with the previousresults of Loc (2006) which report that Vietnam stock market is inefficient in theweak form with daily data However, the extent of inefficiency of Vietnam stockmarket decreases when the weekly observations are employed in our study.Moreover, our research also employs the calendar effect which explores the calendaranomalies in Vietnam market The result of calendar effect especially day of weekdoes not exist in Vietnam stock market during the studied period

Consequentially, this does not support the findings of Loc (2006) that the day ofweek 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 Vietnamapplying new econometrics, new methodology which has been affected the Brooks’(2008) methodology This study also has take advantages of all models which havebeen tested in the previous literatures The second contribution of this study is toprovide evidence against persistent patterns in anomaly in Vietnam stock market.Then, this study also enhances the established literature by providing the mostrecent analysis of our stock market

The remainder of this study is structured as follows Section two reports the relevanttheoretical background to the research and reviews the previous empirical evidences

on weak form efficiency in developed and emerging countries Section threedescribes the data and methodology Section four presents the empirical research.Finally, section five summarizes the results of the study, draws conclusions andprovides suggestions for further research

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 MarketHypothesis appear as early as the beginning of twentieth century in the theoreticalcontribution of Bachelier (1900) who laid the foundation for random walkhypothesis 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 successiveprice changes are independent The Efficient Market Hypothesis has been emergedfrom the combination of empirical findings of Fama (1965) and theory ofSamuelson (1965)

Fama (1970) summarizes this idea in his classic survey by writing: "A market inwhich prices always 'fully reflect' available information is called 'efficient'."According to this hypothesis, in an informatively efficient market, price changesmust be unforecastable Since news is announced randomly, price must fluctuaterandomly 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 eachcategory dealing with a different type of information

The weak form test is the lowest level of efficiency A capital market is said tosatisfy weak form efficiency if the current stock prices fully incorporate theinformation 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

that the current stock prices including all information known to all marketparticipants Hence, this reflects all public available information such as theinformation on stock splits, annual reports; new security issues… Trader can not getthe abnormal returns by analyzing the annual reports or available publicinformation Finally, strong form test of the efficient market theory tests whetherprivate or confidential information is fully reflected in security prices The currentprices of stock including all information known to any market participant includingthe public and private information, this assumption hardly exists in reality, so thestrong 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 newinformation

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

where: P t 1

Pt

The equation indicates that the price of a share at time t+1 is equal to the price of ashare 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 = P

t 

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 walkhypothesis with respect to stock markets in both developed and developingcountries However, the empirical research produce mixed results Most earlystudies are supportive weak forms of Efficient Market Hypothesis in developedcapital markets Recent studies, however, document that stock market returns arepredictable This section provides a review of the literature on the weak formefficiency in both developed and developing countries

Methodologically, testing the weak form efficiency used the random walk modelwhich is widely employed in the preceding literature Practically, several statisticaltechniques, runs test, unit root test, serial correlation test, and variance ratio test, arecommonly used for testing weak form efficiency Specially, the run test is used inthe literature of Fama (1965), Sharma Kennedy (1977), Cooper (1982), Chiat et al.(1983), Wong et al (1984), Yalawar (1988), Ko and Lee (1991), Butler andMalaikah (1992), Abraham (2002), Worthington and Higgs (2004), Squalli (2006);Daraghma et al (2009) Also, the serial correlation test of returns has also been usedextensively 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

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 thatsupport the weak form efficiency Groenewold (1997) conducts weak and semistrong efficiency tests of Australian stock market by using aggregate share priceindexes and finds that the results are consistent with the weak form efficiency Inaddition, Hudson et al (1996) find that the technical trading rules have predictivepower but not sufficient to enable excess return in United Kingdom market

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

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

Chan et al (1997) examine the weak form and the cross country market efficiencyhypothesis 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 Statesfor the period from 1962 to 1992 They conclude that all stock markets in thesample are individually weak form efficient and only a small number of stockmarkets 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 andheteroscedasticity to examine random walk hypothesis for Hang Seng Index onHong Kong Stock Exchange for period from 1985 to 1997 They conduct that HangSeng follows a random walk model and consequently that the index is weak formefficient

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

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

In a more recent research, Kima et al (2008) examine efficiency of stock prices ofgroup 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 ofIndonesia, 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 formefficiency on developing markets are mixed Most of developing countries sufferwith the problem of thin trading In addition, in smaller markets, it is easier for largetraders to manipulate the market Though it is generally believe that the developingcountries are less efficient However, the empirical evidence does not alwayssupport this thought Many papers report weak form efficiency in developingcountries Lima et al (2004) employ data of the daily stock price indexes ofShanghai, Shenzhen (China), Hong Kong, and Singapore Stock exchange over theperiod from 1992 to 2000 They find that the Hong Kong and A shares for both theShanghai, Shenzhen stocks exchanges are in weak form efficiency

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

Mojustafa (2004) examines the behavior of stock prices in United Arab Emiratemarket by using the nonparametric runs to test randomness The data consists ofdaily prices of 43 stocks for the period from 2001 to 2003 The results reveal that 40stocks out of the 43 are random Hence, this supports the weak form EfficiencyMarket 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 roottests for the period from 1999 to 2009 The results from the various unit root testsimply that the Iran daily stock price index follow the random walks process

Many authors, however, argue that markets of the developing countries are in theweak form inefficiency Mobarek et al (2000) study the efficiency of theBangladesh Security on the Dhaka Stock Exchange by using the autocorrelation, runtest for the period of 1988 to 1997 Basing on the result of runs and theautocorrelation tests, the authors argue that the returns of Dhaka stock market do notfollow 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 forthe period from 1991 to 1996 The findings of three tests consistently reject therandom walk hypothesis The author also examines a day of the week and month ofthe year effect on the CSE, but neither effect found to be on the stock market in SriLanka

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

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

Abrosimova et al (2002) test weak form efficiency in Russian stock market rangingfrom 1995 to 2001 by employing unit root, autocorrelation and variance ratio tests.The results of both autocorrelation and variance ratio tests reject the hypothesis ofthe 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 equitymarkets in Asia including Hong Kong, Indonesia, Korea, Malaysia, the Philippines,Singapore, Taiwan and Thailand from 1990 to 2004 The result of variance ratio testindicates that the stock prices of eight Asian countries do not follow the randomwalk with the exceptions of Taiwan and Korea

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

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

Overall, the empirical results from both developed and developing markets showcontrasting evidence on weak form efficiency Especially, results of whether or notemerging markets follow a random walk are rather conflicting Mixed results fromliterature on emerging stock markets efficiency are not surprising since it isobserved that emerging stock markets are generally less efficient than developedmarkets In addition, with the characteristic as high level of liquidity and tradingactivity, substantial market depth and low information asymmetry, developedmarkets are seem to be in the weak form efficiency market while most ofdeveloping markets are characterized as more information asymmetry, lowervolume and frequency of trading (thin trading) and weak institutional infrastructure,settlement delays, weaker disclosure and accounting requirement, which all togethercould cause market inefficiency (Islam et al., 2005) However, not all of developingmarkets 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 bothdeveloped and developing markets However, there are not many researchesempirically investigating the market efficiency in Vietnam In lieu of the currentliterature, Loc et al (2010) employ the weekly price of the market index and thefive 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 thatthe Vietnam stock market is inefficiency in the weak form

3 DATA AND METHODOLOGY

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 processingcompanies for the period from 2007 to 2010 All data is obtained from electronicdatabase from the website cophieu68.com A total of 996 daily and 202 weeklyobservations 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 becausestocks in real estate sector are highly sensitive to any change in the economy whilethe 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 beselected for studying in this literature The oldest seafood processing stocksincluding ABT, AGF, TS4, FMC also employed in the study as those stocks listedbefore 2007 at Hose

Returns are calculated as R t  ln(P t / P t−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 familiareconometrics methods that used in the literature to test the independence of pricesdata The study applies parametric and non-parametric methods to test the randomwalk hypothesis In particular, we use the parametric serial correlation test whichmeasures the relationship of the current stock return and its value in the previousperiod We will then use the run test, a nonparametric test, which is computed to testthe randomness of stock return Furthermore, variance ratio test which is proposed

by Lo and Mackinlay (1988) will be carried out to check whether uncorrelatedincrements exist in the series, under the assumption of homoscedastic

and heteroscedastic random walks Finally, the OSL, ARCH, GARCH(1,1) modelshave 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

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

Table 3 2 Descriptive statistics of weekly returns

Table 3.1 presents a summary of descriptive statistics of the daily returns forVnindex and eight individual stocks returns Sample means, maximums, minimums,standard deviations, skewness, kurtosis and Jacque-Bera statistics and p-values arereported It can be seen that except TS4 (0.0005), SJS (0.0006), CII (0.0003), allindexes 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 standarddeviations 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 notnormal distributed Given that the parameters skewness and kurtosis represent thestandardised fourth and third moments of a distribution These parameters are usedwith Jarque-Bera statistics to indicate whether a data set is normally distributed ornot Skewness measures the extent to which a distribution is not symmetric about itsmean value The skewness of the normal distribution is zero Positive skewnessmeans that the distribution has a long right tail and negative skewness implies thatthe distribution has a long left tail (Oskooe et al., 2010) Table 3.1 shows that thereturns of all stocks except Vnindex, TS4 are positive skewed although theskewness statistics are not large The positive skewness implies that the return ofdistributions of the shares traded on the exchanges have a long right tail of largevalues and hence a higher probability of earning positive returns

Moreover, Kurtosis measures the peakness or flatness of the distribution of theseries 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 lessthan 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 normaldistribution, there by indicating the platykurtic frequency distribution of all stocksreturn series

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

The weekly returns are calculated from the stock prices from Wednesday’s closingprice If the following Wednesday price is not available, then the Thursday price (orTuesday if Thursday is not available) is used If both Tuesday and Thursday pricesare not available, the return for that week is reported as missing The choice ofWednesday price aims to avoid the effects of weekend trading and to minimize thenumber of holidays Table 3.2 presents a summary of descriptive statistics of theweekly returns for Vnindex and eight individual stocks returns By the sameanalysis 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 toolfor testing of either dependence or independence of random variables TheAutocorrelation measures the correlation coefficient between the values of a randomvariable at time t and its value in the previous period In particular theautocorrelation measures the relationship between the current stock return and itsvalue 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

 (r t − r )(r t+k − r)

(rt

Where ρk is the serial correlation coefficient of stock returns of lag k; N is thenumber of observations; rt is the stock return over period t; rt+k is the stock returnover period t+k; r

is the sample mean of stock returns; and k is the lag of the period

The test aims to examine whether the autocorrelation coefficients are significantlydifferent from zero If the autocorrelation is zero, then the sample ofautocorrelations are approximately normally distributed with mean 0 and variance1/T Then this sample autocorrelation can be used to conduct significance tests forthe autocorrelation coefficients in a given confidence interval for an estimatedautocorrelation coefficient to determine whether it is significantly different fromzero Statistically, the hypothesis of weak form efficiency should be rejected if stockreturns (price changes) are successively correlated (ρk is significantly different fromzero)

To carry out the examination, this study used the Ljung–Box portmanteau statistic

(Q) to test the joint hypothesis that all autocorrelations are simultaneously equal to zero, has been computed as follow:

to three kinds of runs: an upward run (prices go up), a down ward run (prices godown) and a flat run (prices do not change).

The run test can also be designed to count the direction of change from stockreturns; for instance, a positive change could be one in which the return is greaterthan the sample of mean, a negative change one in which the return is less than themean, and zero change representing a change equal to mean Under the nullhypothesis of independence in share price changes (share returns), the actual runs

(R) are then counted and compared to the total expected number of runs (m) under the assumption of independence estimated as:

of observations (N>30), the sampling distribution of m is approximately normal andthe standard error of m (σ

m ) is given by:

σm 

The standard normal Z-statistics that can be used to test whether the actual number

of runs is consistent with the hypothesis of independence is given by:

Z 

Where R is the actual number of runs, m is the expected number of runs and 0.5 isthe continuity adjustment in which the sign of the continuity adjustment is negative(-0.5) if Rm

and positive otherwise Since there is evidence of dependence amongshare returns when R is too small or too large, the test is a two-tailed one Anegative Z value indicates a positive serial correlation, whereas a positive Z valueindicates a negative serial correlation The positive serial correlation implies thatthere is a positive dependence of stock prices, therefore indicating a violation ofrandom walks Since the distribution Z is N (0,1), the critical value of Z at the fivepercent significance level is ±1.96 Brooks (2008)

3.2.3 Variance ratio test

The variance ratio test which is developed by Lo et al (1988) is not only morepowerful but also reliable test of random hypothesis It is designed to test the nullhypothesis of random walk process for stock price under the homoscedasticity andheteroscedasticity (Lo et al., 1988) Hence it widely uses by both academics and

20

(Ayadi et al., 1994, C.Cheung et al., 2001, Kima et al., 2008, Lima et al., 2004, Loc

et al., 2010, Smith et al., 2003, Y.Liu et al., 1991)

The variance ratio test exploits the fact that if the logarithm of price series follows arandom walk, then the return variance should be proportional to the return horizon.That is the test is based on the assumption that the variance of increments in therandom walk series is linear in the sample interval Particularly, if a return seriesfollows a random walk process, the variance of its q differences would be q timesthe variance of its first differences (He, June 1991)

heteroscedasiticity-Z(q) =Where

φ(q) =

3q(nq)

Here nq is the number of observation and∅(q)

is the asymptotic variance of thevariance ratio under the assumption of homoscedascity

Since finance time series often possess time varying volatilities and deviate fromnormality Hence, besides the homoscedasticity, this study also uses the Lo etal.’s (1988) heteroscedasiticity robust standard normal test statistics Theheteroscedasticity consistent standard normal test statistic, Z*(q) is then definedas

Z

∅ * (q) 2

j= 1

22

Where ∅*(q)

is the asymptotic variance of the variance ratio under the

∧

assumption of heteroscedasticity: And δ(j)

is the heteroscedasticity – consistentestimator and computed as follows:

∧  ( pt − pt−1 − µ ) 2 ( pt− j − pt− j −1 −δ

δ =

3.2.4 Calendar effect

Calendar effect implies the changes in security prices in stock market following

certain trends based on seasonal effects Such trends occur at a regular interval or at

a specific time in a calendar year Presence of such anomalies in any stock market is

the biggest threat to the concept of market efficiency as these anomalies may enable

stock market participants beat the market by observing these patterns This concept

again violates the basic assumption of Efficient Market Hypothesis that no one can

beat the market and earn the profit in excess of market The most serious violations

of the random walk hypothesis have been related with calendar turning points such

as weekend which is significant effects of the calendar affect

This study has been conducted to test the market efficiency in Hose by examining

day of week effect present in Vnindex and eight selected stocks of real estate and

seafood processing companies To carry out this estimation, we use regression

model, ARCH and GARCH (1,1) which have been employed in many empirical

research (Abeysekera, 2001a, Hau, 2010, Loc, 2006, Gao et al., 2005, Solnik et al.,

1990)

Initially, the ordinary least standard (OSL) has been tested with the dummy variablefor Tuesday, Wednesday, Thursday and Friday in the day of week effect Theregression can be running under the below model:

respective day of the week and zero otherwise The α

k is the coefficients of theregression equation corresponding to the k dummy variables, c is the coefficient ofMonday; u

t is an error term and assumed to be independently and identically

We will then use the Generalised Autoregressive Conditional Heteroscedasticity(GARCH) model to test the estimation The GARCH models provide a moreflexible framework for capturing the time varying volatility in the return series In