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

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THEHAGUES VIETNAM 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 Academic Supervisor Dr LE VAN CHON MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, JULY 2012 UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THEHAGUES VIETNAM 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 HO CHI MINH CITY, JULY 2012 " UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THEHAGUES VIETNAM 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 in partial fulfillment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By NGUYEN DINH TU NHI Academic Supervisor Dr LE VAN CHON HO CHI MINH CITY, 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 ABBREVIATIONS ARCH Autoregressive Conditional Heteroskedasticity ARMA Autoregressive Moving Average EGARCH Exponential Generalized Autoregressive Conditional Heteroskedasticity GARCH Generalized Autoregressive Conditional Heteroskedasticity HNX Hanoi Stock Exchange HOSE Ho Chi Minh City Stock Exchange IGARCH Integrated Generalized Autoregressive Conditional Heteroskedasticity KSE Karachi Stock Exchange NASDAQ National Association of Securities Dealers Automated Quotations NGARCH Nonlinear Generalized Autoregressive Conditional Heteroskedasticity NSE National Stock Exchange NYSE New York Stock Exchange QGARCH Quadratic Generalized Autoregressive Conditional Heteroskedasticity SEM Stock Exchange of Mauritius SENDEX Sensitive Index S&P Standard & Poor's TGARCH Threshold Generalized Autoregressive Conditional Heteroskedasticity us United States VAR Vector Auto Regression VN-Index Vietnam Stock Market Index TABLE OF CONTENTS CHAPTER 1: INTRODUCTION ! 1.1 Problem statement 1.2 Research questions 1.3 Research objectives CHAPTER 2: LITERATURE REVIEW 2.1 The Efficient Market Hypothesis 2.2 The Mixture of Distributions Hypothesis 2.3 The Sequential Information Arrival Hypothesis 2.4 The Generalized Autoregressive Conditional Heteroskedasticity 10 2.5 Conceptual framework 13 CHAPTER 3: VIETNAMESE STOCK MARKET AND LISTED COMPANIES ON THE HO CHI MINH CITY STOCK EXCHANGE 14 Vietnamese stock market 14 3.2 Data 16 3.3 Summary statistics 21 3.4 Graphical analysis 24 CHAPTER 4: ECONOMETRIC MODELS AND DISCUSSION 26 4.1 Test for stationarity in stock return and trading volume: 26 4.2 Trading volume and return volatility 33 CHAPTER 5: CONCLUSIONS 46 Conclusions 46 5.2 Policy implications 47 5.3 Limitations and next findings .48 REFERENCE 49 LIST OF TABLES TABLE PAGE Table 3.1 Some numbers in 2012 15 Table 3.2 Description ofstocks 16 Table 3.3 Descriptive statistics 21 Table 4.1 ADF test 26 Table 4.2 PP test 30 Table 4.3 GARCH (1,1) model without LnVol 36 Table 4.4 Likelihood ratios of stocks 39 Table 4.5 GARCH (1,1) model with LnVol 41 LIST OF FIGURES FIGURES PAGE Figure 2.1 Conceptualized relationships among variables 13 Figure 3.2 Graphs of return, percentage of trading volume, percentage of foreign buying volume, and percentage of foreign selling volume for listed stocks 24 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 I 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 I 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 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 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 buying 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 tum to percentage of foreign selling volume DPM, FPT, and GMD have no interaction between foreign selling volume percent with return due to insignificant 43 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 selling 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 DP!vi, PVD, FPT, VNivi, 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 of0.81, 0.63, and 0.42 Generally, the trading volume causes FPT's return volatility strongly, but VIC's one weakly In contrast, the foreign investors' purchase power for almost stocks impacts negatively on return volatility The parameters of foreign buying 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 buying 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' selling 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 44 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 ofDPM 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 iower persistence of volatility shocks than the model excluding variable volume My findings support papers of Andersen (1996), Chan and Tse (1993), Ornran and McKenzie (2000), Zarraga (2003), Pyun et al (2000), and Bohland 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 Almost stock prices reflect information in the past As a result, the weakform efficiency seems to dominate other forms on the Ho Chi Minh Stock Exchange Likewise, I only suggest eight distinguishing industries presenting essential sectors which impact the economy seriously I have no studies on penny stocks 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 stocks 7• ;; Penny stocks: are common shares of small public companies with high risks and low liquidity Cent stocks: similar to penny stocks 45 CHAPTERS: 5.1 CONCLUSIONS 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-chip stocks on the Ho Chi Minh City Stock Exchange in the period of01 January 2007 to 31 December 2011, I find that there are impacts of trading volume, foreign buying and selling 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 selling volume for more than 60 percent of stocks (excluding DPM, FPT, and GMD) affect stock returns, but inconsiderably; whereas the foreign buying 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 buying volume (except for VIC, GMD, and VSH) influence return volatility negatively, meanwhile, the foreign selling volume impacts Blue-chip stocks: common stocks with high capitalization, strong finance, established companies and leader in the market 46 - 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 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 5.2 Policy implications 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 indicators 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 (Nasar, 1992) Consequently, in my own paper, the investors can use figures and numbers for their investment decisions For example, they can invest in stock DPM in fertilizer safely and consider stocks SSI and VIC in finance carefully They also decide a shortterm, medium-term or long-term investment horizon for each of stocks accordingly Moreover, they can refer statistics of foreign buying and selling volume to determine to buying or selling owned-shares rationally (but only for eight above stocks) For policy makers, they may consider indicators to adjust the various industries 47 5.3 Limitations and next findings On the other hands, the paper also presents some limitations noticed Firstly, the research has not controlled explicitly other macroecnonomic 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 HaNoi stock markets, such as stocks in other industries 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 ofNelson (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 ofGARCH model As a result, the next generations can collect more data series on macroecnonomic factors to conduct the model and derive supportive findings They can examine GARCH ( 1, 1) model for more stocks in various sections on both the Ho Chi Minh City and the Ha Noi City Securities Exchanges They also extend the researches on relationship between trading volume and returns for penny stocks Moreover, they can 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STUDIES HO CHI MINH CITY THEHAGUES VIETNAM THE NETHERLANDS VIETNAM- NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS AN EMPIRICAL STUDY ON STOCK RETURNS, VOLUME, AND VOLATILITY: LISTED COMPANIES. .. 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. .. model on the Ho Chi Minh Stock Exchange As a result, I continue to study more on the relationship between volume and returns and between volatility and volume on the data series of eight listed companies