An empirical study on stock returns, volume, and volatility listed companies on the ho chi minh city stock exchange stock exchange

UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUES THE NETHERLANDS VIETNAM – NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS AN EMPIRICAL STUDY ON STOCK RETURNS, VOLUME, AND VOLATILITY: LISTED COMPANIES ON THE HO CHI MINH CITY STOCK EXCHANGE By Nguyen Dinh Tu Nhi MASTER OF ARTS IN DEVELOPMENT ECONOMICS July 2012 UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUES THE NETHERLANDS VIETNAM – NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS AN EMPIRICAL STUDY ON STOCK RETURNS, VOLUME, AND VOLATILITY: LISTED COMPANIES ON THE HO CHI MINH CITY STOCK EXCHANGE A thesis submitted to Vietnam – Netherlands Programme in partial fulfillment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By Nguyen Dinh Tu Nhi Supervisor Dr Truong Tan Thanh July 2012 ACKNOWLEDGEMENTS I am not able to finish this thesis without the guidance of my supervisors and committee members, supports from classmates, and aids from my family I would like to express my very great appreciation to my supervisors, Dr Le Van Chon and Dr Truong Tan Thanh, for their patient guidance, enthusiastic assistance, and useful critiques, valuable and constructive suggestions during my research I am also particularly grateful for the assistance given by Dr Nguyen Trong Hoai and Dr Pham Khanh Nam for motivating and supporting me to complete the thesis I would like to offer another thank to Dr Duong Nhu Hung, who inspires me to choose this topic for my thesis My grateful thanks are also extended to staffs of the Administration Department and Library of Vietnam-Netherlands Programme in providing me good environment and facilities to complete the thesis Finally, I would like to express my love and gratitude to my family and friends for their understanding, supports, and encouragements throughout the research TABLE OF CONTENTS CHAPTER 1: INTRODUCTION 1 Problem statement Research questions 3 Research objectives CHAPTER 2: LITERATURE REVIEW The Efficient Market Hypothesis The Mixture of Distributions Hypothesis The Sequential Information Arrival Hypothesis 10 The Generalized Autoregressive Conditional Heteroskedasticity 10 CHAPTER 3: VIETNAMESE STOCK MARKET AND LISTED COMPANIES ON 15 THE HO CHI MINH CITY STOCK EXCHANGE 15 Vietnamese stock market 15 Data 17 Summary statistics 22 Graphical analysis 25 CHAPTER 4: ECONOMETRIC MODELS AND DISCUSSION 27 Test for stationarity in stock return and trading volume: 27 Trading volume and return volatility 33 CHAPTER 5: CONCLUSIONS 48 REFERENCE 51 LIST OF TABLES TABLE PAGE Table Description of stocks………………………………………………………….18 Table Descriptive statistics ………………………………………………………….22 Table ADF test……………………………………………………………………….28 Table PP test ……………………………………………………………………… 31 Table GARCH (1,1) model without LnVol………………………………………….36 Table Likelihood ratios of stocks…………………… …………………………… 39 Table GARCH (1,1) model with LnVol…………………………………………… 40 ABSTRACT AN EMPIRICAL STUDY ON STOCK RETURNS, VOLUME, AND VOLATILITY LISTED COMPANIES ON THE HO CHI MINH CITY STOCK EXCHANGE By Nguyen Dinh Tu Nhi The thesis examines the relationship between stock returns, trading volume and return volatility With the focus on listed companies on the Ho Chi Minh City Stock Exchange over the period between 01 Jan 2007 and 31 Dec 2011, the study conducts GARCH (1,1) to model the relationship between stock return, trading volume, and volatility We also include a dummy to capture possible effect of pre and post-crisis on stock return volatility The analysis results show that there exists an influence of trading volume on stock return, even after controlling effects of foreign trading volume It is also evident that trading volume has some predictive power to return volatility We also find our results consistent with previous studies such as Clark (1973) and Copeland (1976) The result also implies that Vietnamese stock market is efficiently weak at least for listed companies on the Ho Chi Minh City Stock Exchange Key words: Trading volume, stock returns, return volatility, foreign trading, GARCH CHAPTER 1: INTRODUCTION Problem statement In each country, the stock market reflects the health of its economy It does not only affect foreign exchange and gold markets but also credit market and option market Actually, when a stock market is strong, the investors often tend to convert foreign currency and gold into cash to invest in stocks It will depreciate foreign currency and gold consistently and vice versa In other hands, in term of weak stock market, the Government will tighten cash flow for stocks as well as the banks will reduce disbursement for stocks and vice versa For option market, the strong stock market will lead to growth of different kinds of options because investors expect to earn more profits In Vietnam, the stock market also plays an important role to mirror the changing economy For instance, when the information of bad debts or higher inflation is proclaimed, the VNindex will decrease sharply Likewise, when the Government introduces some supporting policies to the economy, the VN-index has a chance to increase The fluctuation of VNindex also indicates a development or recession of economy That is called bi-directional effect of information and stock market In the mean-variance analysis, the expected stock returns and return volatility are important factors that investors concentrate on because returns and volatility imply risks for investors’ portfolio Moreover, the volume of trade is also supposed to be an authoritative component of absorbing information in the stock market In case investors believe in higher return on stocks, they tend to deal more and lead to higher trading volume in the stock market In contrast, when they forecast lower return on stocks, they will trade less or the trading volume will decrease Hence, the higher or lower trading volume may be a signal of the fluctuations of stock returns As a result, the relationships among stock returns, trading volume and return volatility have become vital topics in empirical researches There are many papers on return-volume and volume-volatility relationships For the return-volume relationship, Karpoff (1987) finds the positive asymmetric relationship between volume and price change in the equity market Another model which also predicts the asymmetric relationship between trading volume and price changes is initiated by Epps (1975) and complemented by Jennings, Starks, and Fellingham (1981) Two above models relates to flow of information Furthermore, Granger, Morgenstern, and Godfrey (1964) and Granger (1968) use data of indices and individual stocks on the New York Stock Exchange to test the relationship between price changes and trading volume They find that price changes follow a random walk in which the past trend of stock price cannot predict its future trend Mohammadreza Mehrabanpoor, Babak Valizadeh Bahador, and Gholamreza Jandaghi (2005) also get the positive relationship between market turnover and indices on the Tehran Stock Exchange In addition, Michael Long (2007) finds a significantly positive interaction between absolute value of call price changes and trading volume in the option markets For the volume-return volatility relationship, Engle (1982) originates the Autoregressive Conditional Heteroskedasticity (ARCH) Model, which enumerates that stock returns follow a mixture of distribution Later, Bollerslev (1986) starts the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Model and consider trading volume as a proxy of information flow Thus, the model is developed by Lamoureux and Lastrapes (1990), Brailsford (1996), Mestel, and Gurgul and Majdosz (2003) Moreover, Timothy J Brailsford (1996) contends that the relationship between stock return volatility and volume is positive through GARCH model However, some present opposite views A Fujihara and Mbodja Mougoue (1997) find that there is no causal relationship between return and volume According to Roland Mestel, Henryk Gurgul, and Pawel Majdosz (2003), the relationship between stock return and trading volume is too weak to forecast each other Berna Okan, Onur Olgun, and Sefa Takmaz (2009) conclude that trading volume has negative effect on return volatility by applying GARCH, EGARCH, and VAR models For the Vietnamese market, Truong Dong Loc (2009) investigates the unilateral causality effect of HNX-index to trading volume Furthermore, Truong Dong Loc and Dang Thi Thuy Duong (2011) replicate the study with the data of foreign trading volume, and find that the index influences net foreign volume, but the reverse is not true There are only few researches that has examined relationship between stock returns and trading volume during the recent crisis, and accounted for effect of foreign trading volume across different industries using GARCH model This thesis attempts to fill this gap by examining the relationship between trading volume and stock return and between return and volatility for listed companies on the Ho Chi Minh City Stock Exchange Particularly, we test the effect of trading volume on stock return and return volatility by applying GARCH (1, 1) model Research questions To clarify the relationships among trading volume, stock returns and return volatility, I collect data series of intra-day stock prices to test the appropriate model The final purpose is that, in this paper, I am going to answer the following research questions: (i) Is there the relationship between trading volume and return volatility? and (ii) Does trading volume cause stock returns? 3 Research objectives To reach above aims, my objectives of the paper are: (1) To examine the relationship between trading volume and return volatility and, (2) To understand the impact of trading volume on stock return through GARCH (1,1) model To that end, I use data of eight listed companies on the Ho Chi Minh City Stock Exchange (HOSE) before and after the recession triggered by the US sub-prime mortgage crisis The remainder of the paper is arranged as follows Section Two gives a brief literature review of empirical studies Section three presents description of Vietnamese stock market and explains specific characteristics of data This section also exposes statistics of selected stocks in the HOSE The methodology and discussion of empirical results are in section four which is followed by Conclusion shock effects on the returns This sum of (βε + βσ) also indicates the volatility clustering5 which sustains the asymmetry and inefficiency in the emerging market The higher persistence is, the more slowly the volatility of returns falls In other words, the high persistence indicates slow regression to the mean, the low persistence shows rapid retrogression to the meal The volatility persistence of conditional variance of VIC is very high (greater than 90 percent), especially VIC is close to unity, indicating that the volatility shock is persistent Meanwhile, the persistence of PVD and SSI are very low (lower than 50 percent) The volatility persistence of the rest of stocks is moderate from 70 to 80 percent In the mean equation, all the constants for stocks are negative and significant at one percent level As a result, all returns tend to decrease However, the coefficient of lag returns is positive consistently and statistically significant at one percent level Obviously, the coefficients of lag return of VIC (0.24), SSI (0.23), and GMD (0.21) have larger effect on returns than FPT (0.15), VSH (0.12), VNM (0.12), and DPM (0.11) The coefficient of lag return of PVD has low impact on return (0.09) Hence, VIC and SSI have a giant impact of lag returns on current returns; however, PVD is reverse case For more than 80 percent of stocks, the coefficients of percentage of trading volume are positive and significant; therefore, there is a positive relationship between return and trading volume Still, in case of SSI, the parameter has no meaning However, trading volume percent merely affects average return slightly Particularly, parameters of trading Volatility clustering: large changes tend to be followed by large changes and small changes tend to be followed by small changes 43 volume are from 0.001 to 0.003 for almost stocks; especially the percentage of volume of SSI does not affect return In most of cases, foreign buy volume percent does not influence return because of insignificant parameters Still, the VIC’s coefficient is positive significantly at one percent level with negligible impact with only 0.00047 I turn to percentage of foreign sell volume DPM, FPT, and GMD have no interaction between foreign sell volume percent with return due to insignificant parameter For the remaining stocks, their parameters are negative significantly at one percent level, except for SSI’s positive effect at one percent level Nevertheless, the impact of foreign sell volume percent on stock return is also negligible The dummy variable of period influences somewhat on returns The coefficients of dummy variables for DPM, PVD, FPT, VNM, and SSI are positive significantly at one percent level, thus the impact of post-crisis on these stock returns is positive The pre or post-crisis does not determine return in case of VIC, GMD, and VSH Generally, the crisis only influences DPM, PVD, FPT, VNM, and SSI In term of return volatility equation, all constants for eight stocks are negative at one percent significant level The percentage of trading volume does affect return volatility positively at one percent significant level The percentage of trading volume strongly impacts volatility of FPT with parameter of 1.36, DPM, VSH, and PVD with 1.15 Meantime, the little impacts of trading volume on return volatility of SSI, GMD and VIC are demonstrated by parameters of 0.81, 0.63, and 0.42 Generally, the trading volume causes FPT’s return volatility strongly, but VIC’s one weakly 44 In contrast, the foreign investors’ purchase power for almost stocks impacts negatively on return volatility The parameters of foreign buy volume percents for more than 60 percent of stocks are significant at one percent level, involving FPT (-0.37), SSI (-0.23), DPM (0.21), VNM (-0.20), and VSH (-0.06) successively Besides, the percentage of PVD’s foreign buy volume influences slightly on volatility (-0.076) at ten percent significant level, while the VIC and GMD’s return volatilities are not caused by foreign purchase power any more The percentage of foreign investors’ sell power absolutely does not impact volatility in cases of DPM, PVD, SSI, and VIC It influences VNM and FPT’s return volatility at one percent level and GMD’s at five percent positively, whereas VSH’s at one percent negatively Finally, about sub-periods, there are negative impacts of the post-crisis on volatility in most of cases That is, 50 percent of stocks have negative parameters at one percent significant level accounting for PVD, SSI, VIC, and VSH More than 30 percent of stocks are significant at five percent However, there is no relationship between the pre and post-crisis period with return volatility of DPM My results probably support the MDH since the trading volume is in significantly positive relationship to return and volatility Moreover, the model including volume has lower persistence of volatility shocks than the model excluding variable volume My findings support papers of Andersen (1996), Chen and Tse (1993), Omran and McKenzie (2000), Zarraga (2003), Pyun et al (2000), and Bohl and Henke (2003) In my findings, for all stocks mentioned, all stock returns are mirrored by lagged return and the return volatility is measured by past values of squared residuals and past volatility 45 Almost stock prices reflect information in the past As a result, the weak-form efficiency seems to dominate over other forms on the Ho Chi Minh Stock Exchange Forecasting the volatility models is very important for application in investments, evaluation of stocks, risk management and monetary policy making According to Poon and Granger (2003), many financial institutions have to forecast volatility exactly in the financial market because financial volatility can cause a large consequence to the economy Therefore, the policy makers need to depend on estimators of financial volatility to consider damages for financial markets and the economy as a whole For instance, the Chicago Board Options Exchange Volatility Index (VIX-index) is used to measure volatility of S&P 500 index options next 30 days It is really necessary for both investors and the US Federal Reserve to predict securities, currencies for creating monetary policies (Nassar, 1992) Consequently, in my own paper, the investors can draw decision on investment based on understanding the relationship between volume, return, and return volatility and analyzing my numbers For example, they can invest in stock DPM in fertilizer safely and consider stocks SSI and VIC in finance carefully They also decide a short-term, medium-term or long-term investment horizon for each of stocks accordingly Moreover, they can refer statistics of foreign buy and sell volume to determine to buy or sell owned-shares rationally (but only for eight above stocks) For policy makers, they can follow estimators to recognize which industries should be stimulated or survived 46 Nevertheless, I only suggest eight distinguishing industries presenting essential sectors which impact the economy seriously I have no studies on penny stocks6 with small capitalization and slight impacts on the entire economy, but high speculation and risks Therefore, my findings and discussions in this paper may not support investors focusing on cent stocks7 Penny stocks: are common shares of small public companies with high risks and low liquidity Cent stocks: similar to penny stocks 47 CHAPTER 5: CONCLUSIONS The paper investigates the relationship between percentage of volume and return volatility by the GARCH (1,1) model Using data series of stock prices and trading volume of eight blue-chip8 stocks on the Ho Chi Minh City Stock Exchange in the period of 01 January 2007 to 31 December 2011, I find that there are impacts of trading volume, foreign buy and sell volume together with recession on return and volatility, respectively due to analysis of GARCH (1, 1) model The lagged returns for eight stocks influence the current returns extremely Meantime, VIC and SSI’ lagged returns have stronger effects on present returns than others The trading volume for more than 80 percent of stocks (except for SSI) and foreign sell volume for more than 60 percent of stocks (excluding DPM, FPT, and GMD) affect stock returns, but inconsiderably; whereas the foreign buy volume for more than 85 percent of stocks does not have any influences on returns (excluding slightly VIC effect) The dummy variable of recession intervals demonstrates its impact on returns for five stocks, comprising DPM, PVD, SSI, VNM, and FPT For return volatility, all ARCH and GARCH effects for stocks are significant and their persistence is lower than unity which is consistent to assumption of stationery data The significant ARCH expresses the dependence of current return volatility on the lagged error terms; in addition, the significant GARCH indicates the impact of the lagged variance on conditional variance In this case, there is a large impact of trading volume on return volatility for all stocks, especially largest impact on PVD, DPM, and VSH’s volatilities The foreign buy volume (except for VIC, GMD, and VSH) influence return volatility Blue-chip stocks: common stocks with high capitalization, strong finance, established companies and leader in the market 48 negatively, meanwhile, the foreign sell volume impacts return volatility for half of total stocks The recession influences return volatility for almost stocks negatively, except no impact on DPM The results may supplement to the MDH Moreover, the inclusion of trading volume in the GARCH (1,1) model is better than the exclusion of volume in the model Although the model with volume has lower persistence of volatility shocks than the model without volume, it is better than the model excluding volume corresponding to supposal of stationarity On the other hands, the paper also presents some limitations noticed Firstly, the research has not studied impact of other factors on the stock return, such as inflation rate, interest rate, price of gasoline, price of gold, macroeconomic policies, and so on yet Secondly, the selected high-capitalization eight stocks only present and result in deduction of eight major sectors on the Ho Chi Minh Stock Market Therefore, the above findings may not be representative to all stocks on both the Ho Chi Minh and the Ha Noi stock markets, such as stocks in other industries (outside above eight ones) or even penny stocks Finally, the GARCH (1,1) model has not resolved the asymmetric and skewness issues of stock returns GARCH (1,1) model also contains some disadvantages since it does not permit the asymmetric shocks in the conditional variance Therefore, the extended models should be employed, such as asymmetric Exponential GARCH (EGARCH) model of Nelson (1991), NGARCH model of Engle and Ng (1993), TGARCH model of Zakoian (1994), QGARCH model of Sentana (1995) or IGARCH model as a restricted version of GARCH model Therefore, the next generations can collect more data series on different factors (excluding trading volume) to conduct the model and derive supportive findings They can run GARCH (1,1) model for more stocks in various sections on both the Ho Chi Minh City and 49 the Ha Noi City Securities Exchanges to represent for all kinds of stocks They also research on relationship between trading volume and returns for penny stocks to exuberate findings Moreover, they can apply extended asymmetric models to learn more about GARCH models In addition, mentioning on the market efficiency, my findings entail that the weak-form efficiency is prominent in comparison with semi-strong and strong forms 50 REFERENCE [1] Admati, A.R., and Pfleiderer, P., A Theory of Intraday Patterns: Volume and Price Variability, Review of Financial Studies (1988), 3-40 [2] Ali Ahmed, Huson Joher, The Relationship between Trading Volume, Volatility and Stock Market Returns: A Test of Mixed Distribution Hypothesis for a Pre - and Post Crisis on Kuala Lumpur Stock Exchange, Investment Management and Financial Innovations (2005), 146-158 [3] Andrew, W.L., and Jiang, W., Trading Volume, Definition, Data Analysis and Implications of Portfolio Theory, Review of Financial Studies 13 (2000), 257-300 [4] Anirut Pisedtasalasai, Return and Trading Volume Transmissions Mechanisms between a Super-Large Stocks and General Stocks in the market: a New Zealand Case, International Research Journal of Finance and Economics 25 (2009), 217-230 [5] Berna Okan, Onur Olgun, and Sefa Takmaz, Volume and Volatility: A Case of Ise-30 Index Futures, International Research Journal of Finance and Economics 32 (2009), 93-103 [6] Bong-Soo Lee, Oliver M Rui, the Dynamic Relationship between Stock Returns and Trading Volume: Domestic and Cross-Country Evidence, Journal of Banking and Finance 26 (2002), 51-78 [7] Bollerslev, T., Generalised Conditional Autoregressive Heteroskedasticity, Journal of Econometrics 31 (1986), 307-327 [8] Bollerslev, T., A Conditional Heteroskedasticity Time Series Model for Speculative Prices and Rates of Return, Review of Economics and Statistics (1987), 542-547 51 [9] B.M Nowbutsing and S Naregadu, Returns, Trading Volume and Volatility in the Stock Market of Mauritius, African Journal of Accounting, Economics, Finance, and Banking Research (2009), 1-36 [10] Brajesh Kumar, Priyanka Singh, and Ajay Pandey, the Dynamic Relationship between Price and Trading Volume: Evidence from Indian Stock Market, 2009, Working Paper, Indian Institute of Management Ahmedabad [11] Brailsford TJ, the Empirical Relationship between Trading Volume, Returns, and Volatility, Accounting and Finance 35 (1996), 89-111 [12] Brooks, R., Faff, R., and Mckenzie, M., Bivariate Garch Estimation of Beta Risk in the Australian Banking Industry, Accountability Performance (2000), 81-102 [13] Brooks, R., Faff, R., and Fry, T., GARCH Modelling of Individual Stock Data: The Impact of Censoring, Firm Size and Trading Volume, Journal of International Financial Markets, Institutions and Money 11 (2001), 215-222 [14] Burton G Malkiel, the Efficient Market Hypothesis and Its Critics, 2003, CEPS Working Paper, Princeton University [15] Campbell, J., Grossman, S., and Wang, J., Trading Volume and Serial Correlation in Stock Returns, Quarterly Journal of Economics 108 (1993), 905-939 [16] Clark, P.K., A Subordinate Stochastic Process Model with Finite Variance for Speculative Prices, Econometrica 41 (1973), 135–155 [17] Copeland, T.E., A Model of Asset Trading Under the Assumption of Sequential Information Arrival, Journal of Finance 31 (1976), 1149-1168 52 [18] D Michael Long, An examination of the Price – Volume relationship in the Option Markets, International Research Journal of Finance and Economics 10 (2007), 47-56 [19] Dang Huu Man, A Research on Predictability of Capital Market Risk Management Models – Case of Value-At-Risk Models, Journal of Science and Technology (2009), 126-134 [20] Engle, R., and Mustafa, C., Implied ARCH Models from Option Price, Journal of Econometrics 52 (1992), 289-311 [21] Epps, T., and Epps, M., the Stochastic Dependence of Security Price Changes and Transaction Volumes: Implications for the Mixture of Distributions Hypothesis, Econometrica 44 (1976), 305-321 [22] Faid Gul and Tariq Javed, Relationship between Trading Volume and Stock Exchange Performance: A Case from Karachi Stock Exchange, International Business & Economics Research Journal 8(2009), 13-20 [23] Fauzia Mubarik & Attiya Y Javid, Relationship between Stock Return, Trading Volume and Volatility: Evidence from Pakistani Stock Market, Asia Pacific Journal of Finance and Banking Research (2009), 1-17 [24] George E Tauchen and Mark Pitts, the Price Variability-Volume Relationship on Speculative Markets, Econometrica 51 (1983), 485-505 [25] Jeff Fleming, Chris Kirby, and Barbara Ostdiek, Stochastic Volatility, Trading Volume, and the Daily Flow of Information, Journal of Business 79 (2006), 15511590 53 [26] Jingliang Xiao, Robert D Brooks, and Wing Keung Wong, Garch and Volume Effects in the Australian Stock Markets, Journal of Annals of Financial Economics (2009), 79-105 [27] Harris, L., Cross-Security Tests of the Mixture of Distributions Hypothesis, Journal of Financial and Quantitative Analysis 21 (1986), 39-46 [28] Harris, L., Transaction Data Tests of the Mixture of Distributions Hypothesis, Journal of Financial Quantitative Analysis (1987), 127-141 [29] Karpoff, The Relation between Price Changes and Trading Volume: a Survey, The Journal of Financial and Quantitative Analysis 22 (1987), 109-126 [30] Kemal Saatcioglu and Laura T Starks, The Stock Price-Volume Relationship in Emerging Stock Markets: The Case of Latin America, International Journal of Forecasting 14 (1998), 215-225 [31] Lamoureux, C.G., and Lastrapes, W.D., Heteroskedasaticity in Stock Returns Data: Volume versus Garch Effects, Journal of Finance 45 (1990a), 221–229 [32] Lamoureux, C.G., and Lastrapes, W.D., Persistence in Variance, Structural Change and the Garch Model, Journal of Business Economic Statistic (1990b), 225-234 [33] Malabika Deo, K Srinivasan, and K Devanadhen, The Empirical Relationship between Stock Returns, Trading Volume and Volatility: Evidence from Select AsiaPacific Stock Market, European Journal of Economics, Finance and Administrative Sciences 12 (2008), 58-68 [34] Martin Sewell, History of the Efficient Market Hypothesis, 2011, Research Note, UCL Department of Computer Science 54 [35] Marwan J Darwish, Testing the Contemporaneous and Causal Relationship between Trading Volume and Return in the Palestine Exchange, Interdisciplinary Journal of Contemporary Research in Business (2012), 55-64 [36] Michael T Maloney and J Harold Mulherin, the complexity of price discovery in an efficient market: the stock market reaction to the Challenger crash, Journal of Corporate Finance (2003), 453-479 [37] Mohammadreza Mehrabanpoor, Babak Valizadeh Bahador, and Gholamreza Jandaghi, Stock Exchange Indices and Turnover Value-Evidence from Tehran Stock Exchange, African Journal of Business Management (2011), 783-791 [38] Safi Ullah Khan, and Faisal Rizwan, Trading Volume and Stock Returns: Evidence from Pakistan’s Stock Market, International Review of Business Research Papers (2008), 151-162 [39] Sarika Mahajan, and Balwinder Singh, the Empirical Investigation of Relationship between Return, Volume and Volatility Dynamics in Indian Stock Market, Eurasian Journal of Business and Economics (2009), 113-137 [40] Sharma, L.J., Mougoue, M., and Kamath, R., Heteroscedasticity in Stock Market Indicator Return Data: Volume versus GARCH Effects, Applied Financial Economics (1996), 337-342 [41] Surya Bahadur G.C., Volatility Analysis of Nepalese Stock Market, the Journal of Nepalese Business Studies (2008), 76-84 [42] Robert Engle, Garch 101: The Use of Arch/ Garch Models in Applied Econometrics, Journal of Economic Perspectives 15 (2001), 157-168 55 [43] Roger A Fujihara and Mbodja Mougoue, Linear and Nonlinear Causal Relationships between Price Variability and Volume in Petroleum Futures Markets, The Journal of Futures markets 17 (1997), 385-416 [44] Tarika Singh and Seema Mehta, Volatility of Stock Returns: A Case Study of Selected Asian Indices, International Research Journal of Finance and Economics 45 (2010), 46-57 [45] Tarun Chordia and Bhaskaran Swaminathan, Trading Volume and CrossAutocorrelations in Stock Returns, The Journal of Finance 55 (2000), 913-935 [46] Tauchen, G.E and Pitts, M., the Price Variability–Volume Relationship on Speculative Markets, Econometrica 51 (1983), 485–505 [47] Timothy J Brailsford, The Empirical Relationship between Trading Volume, Returns and Volatility, 1994, Research Paper, University of Melbourne [48] Thomas E Copeland and Daniel Friedman, the Effect of Sequential Arrival on Asset Prices: An Experimental Study, the Journal of Finance 42 (1987), 763-797 [49] Truong Dong Loc, The Causal Relation between Stock Price and Trading Volume: Evidence from The Hanoi Stock Trading Center, Economic Development Review 178 (2009), 11-15 [50] Truong Dong Loc, and Dang Thi Thuy Duong, The Relationship between Hnx-Index and Volume of Trading of Foreign Investors, The Journal of Banking Technology 62 (2011), 4-8 56 [51] Vuong Thanh Long, Empirical Analysis of Stock Return Volatility with Regime Change Using Garch Model: the Case of Vietnam Stock Market, 2008, Working paper 084 http://www.vdf.org.vn/workingpapers/vdfwp084 [52] Wong, W.K., Leung, P.L., and Xu, J., the Garch Effects on the Volume of China Stock Markets, International Journal of Finance 17 (2005), 3290-3329 [53] W S Chan and Y K Tse, Price-Volume Relation in Stocks: A Multiple Time Series Analysis on the Singapore Market, Asia Pacific Journal of Management 10 (2005), 3956 57 ... companies on the Ho Chi Minh stock market 14 CHAPTER 3: VIETNAMESE STOCK MARKET AND LISTED COMPANIES ON THE HO CHI MINH CITY STOCK EXCHANGE Vietnamese stock market There are many researches on. .. 2008, there are 164 companies listed on HOSE and 154 ones on HNX The number of listed companies increases up to 627 ones in the year 2010 including companies on HOSE, on HNX, and fund management companies. .. and stock return and between return and volatility for listed companies on the Ho Chi Minh City Stock Exchange Particularly, we test the effect of trading volume on stock return and return volatility