Relationship between stock return and trading volume in vietnam's stock market

luận văn, khóa luận, chuyên đề, đề tài

MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HO CHI MINH CITY --------------------------------------------- ĐỖ NGỌC HOÀNG YẾN RELATIONSHIP BETWEEN TRADING VOLUME AND STOCK RETURN IN VIETNAM’S STOCK MARKET Major : FINANCE – BANKING Code : 60.31.12 MASTER THESIS Instructor: Dr HỒ VIẾT TIẾN HO CHI MINH CITY, SEPTEMBER 2011 i ABSTRACT This thesis investigated the relationship between return and trading volume in the Vietnam’s stock market in the context of Granger causality test and GARCH model test. The sample, including two market indices and thirty seven largest market capitalization listed companies during the period since they firstly traded through July 2011, was used. The dynamic relation as marked by lead –lag relationship from return to volume was confirmed at both market level and firm level. I also found the evidences supported the interaction between two exchanges in Vietnam. When testing the mixture distribution hypothesis, the results indicated that volume was not a good proxy for information arrival in the stock market due to the persistence of volatility remained in most of the cases. This finding was similar to other emerging markets which less agreed with the mixture distribution hypothesis. ii CONTENTS Abstract i Contents . ii List of Tables . iv Chapter 1: Introduction 1.1 Introduction 1 1.2 Research background 1 1.3 Problem statement 3 1.4 Research objectives and questions . 4 1.5 Research methodology and scope . 5 1.6 Thesis structure . 5 Chapter 2: Literature Review 2.1 Theoretical background 7 2.2 Empirical studies 2.2.1 Studies on volume- price change relation . 9 2.2.2 Studies on volume- volatility relation 11 Chapter 3: Research Methodology 3.1 Hypotheses . 15 3.2 Data Description . 15 3.2 Econometric Methodology 3.2.1 Stationary and Unit Root test 16 3.2.2 Cointegration . 17 iii 3.2.3 Granger Causality tests . 19 3.2.4 ARCH models . 21 3.2.5 GARCH models . 23 3.2.6 Threshold GARCH models . 23 Chapter 4. Empirical results 4.1 Market level analysis 4.1.1 Descriptive statistic for markets 25 4.1.2 Unit root test and Granger causality test 26 4.1.3 GARCH(1,1) test and TGARCH (1,1) test 27 4.2 Firm level analysis 4.2.1 Descriptive statistic 29 4.2 .2Granger causality test 30 4.2.3 Restricted and unrestricted GARCH(1,1), TGARCH (1,1) test 33 Chapter 5. Conclusion and Implication 5.1 Main findings . 35 5.2 Implications 35 References 37 Appendix 1 41 iv LIST OF TABLE Table 4. 1 Descriptive statistics of two market indices. . 25 Table 4. 2 Stationary test for market indices . 26 Table 4. 3 Cointegration test (Unit root test for residuals) . 26 Table 4. 4 Granger causality test at market level . 27 Table 4. 5 ARCH effect test for indices 28 Table 4. 6 GARCH (1,1) model and TGARCH (1,1) model for indices 29 Table 4.7 Granger causality test at firm level . 31 Table 4.8 ARCH effect test for firms . 33 Table A1 Descriptive statistics of firms 41 Table A2 Unit root test for return and volume of firms 43 Table A3 Cointegration test at firm level . 44 Table A4 GARCH (1,1) model with and without volume for firms 45 Table A5 TGARCH(1,1) model with and without volume for firms 47 Table A6 List of 37 sample firms with their symbol . 48 1 CHAPTER 1: INTRODUCTION 1.1 INTRODUCTION This chapter explains why the link of volume, return and volatility is worth investigating in the case of the Vietnamese stock market. In particular, this chapter divides into six sections. The first section summarizes the structure of the chapter. The second one provides evidences that tell us why the return – volume relationship becomes a concern for market participants and policy makers. From this background information, the third section raises the problem necessary to make clear for the case of Vietnam. The fourth section covers the research objectives and research questions. The fifth section describes the methodology and scope. The last one ends with description about the structure of the thesis. 1.2 RESEARCH BACKGROUND The relationship between return, volatility, and volume has met the interest of many researchers over the past years. The motivation comes from the attempt to measure and model the volatility of financial assets return. Volume is evidenced to be an important part of pricing financial assets under influence of information arrival. Due to new information arrival, investors may adjust their expectations and this is the main source for price and return movements. However, the stock return may remain unchanged if some investors recognize the information as good news whereas others find it to be bad news. Clearly, it is necessary to examine the dynamics of stock return, volatility and trading volume so that it would improve the understanding of the microstructure of the stock market and then help the participants and policy makers in their own strategies. Most previous researches followed two leading theories (hypotheses), the mixture of distribution hypothesis (MDH) and the sequential information arrival hypothesis (SAI), to examine the information arrival process in financial markets. In general, both MDH and SAI hypotheses support a contemporaneous and positive relationship 2 between volume and absolute return and assume a symmetric effect for price changes. As pointed out in MDH, volume of trade can be a proxy of new arrivals [Clark (1973), Epps and Epps (1976)]. Clark (1973) implies that the value of price change and trading volume are distributed independently from each other. Also, the number of information arrivals per time period varies. Lamoureux and Lastrapes (1990) shows that a serially correlated mixing variable measuring the rate at which information arrives to the market helps explain the generalized autoregressive conditional heteroskedasticity (GARCH) effect in the return. According to them, volume that is considered as an explanatory in the conditional variance equation eliminates the GARCH effects. Sharma et al. (1996) extend Lamoureux and Lastrapes (1990) work by bringing out two main forms: (1) the ability of daily trading volume data to fully capture the information flow on the market return would partly rest on the degree of market efficiency, and (2) both firm – specific factors and market – wide factors (which affect volume) can generate volatility. This makes volume a good or poor proxy for news arrival that contributes to conditional heteroskedasticity. However, Najand and Yung (1991) and Bessembider and Seguin (1992, 1993) present evidence against MDH. In addition, Bessembider and Seguin (1992, 1993) suggest that the volatility –volume relation in the financial markets depends on the type of trade. On the other hand, the sequential arrival of information hypothesis (SAI) suggests gradual popularization of information. According to Grammatikos and Saunders (1986, p.326), the implication of SAI is that the information is sequentially observed by each trader in the market. Under SAI framework, McMillan and Speight (2002) argue that past absolute return provides information on current volume, and past volume contains information on current absolute return. In other words, this dynamic relationship is helpful and important to forecast return and volatility by using trading volume information. 3 1.3 THE PROBLEM STATEMENT The stock market in Vietnam, which is supervised and managed by the State Securities Commission, has developed rapidly since established in July 2000. With 289 firms listed on Hochiminh Stock Exchange and 384 firms listed on Hanoi Stock Exchange up to May 2011, the market is considered as a channel for companies to raise medium and long capital. Regarding capitalization value, it is recorded to grow considerably from VND270 billions in 2000 (approximate 0.28% GDP) to VND740,433 billion in 2010, approximate 45.2 percent of Vietnam GDP. The number of securities trading accounts has reached at 1,103,184 at April 30 th 2011, increasing 25.4 percent compared to one year before. On average, total trading volume of two exchange is 81,312,559 shares and fund certificates and VND2,534.93 billions trading value per day is recorded in 2010. During ten years, Vietnam‟s stock market has shown the ups and downs of a developing market. In the first five years, the market did not attract the public attention and made very little distribution to the economy due to the lack of merchandise and unattractive small listed companies. Since 2006, it has attracted more foreign and domestic investors with lively trading activities in two listed exchanges. It showed an excellent performance in 2006 when the market capitalization increased fifteen times, the Vnindex of Hochiminh Stock Exchange (HOSE) grew 144% and the Hnindex of Hanoi Stock Exchange (HNX) grew 152.6% only in one year. After reaching the highest peak of 1170.67 points in March 2007, Vnindex went down rapidly under effects of the global recession. The index fell as low as 239.69 points in February 2009. Since then, the index rose nearly 2.6 times to 530 points at the beginning of 2010. As being affected by the changes of the world finance and the difficulties inside the economy, the stock market of Vietnam continues to perform quietly during the 2010 and the first half of 2011. 4 For a young stock market, Vietnam‟s market clearly contains weaknesses. Firstly, this is an immature market with a weak legal environment and lack of capital. The Government strictly controls the rules and actively intervenes in stock trading. Accordingly, investors tend to speculate, and thus cause high market volatility. Secondly, the lack of transparency is widely known as a biggest problem facing the traders. Reporting requirements are not well-defined and public information disclosure is not clear and unreliable. From that reasons, it is harder for investors to build up a good portfolio in an inefficient market which contains lots of confusing information. It is the fact that while most of previous studies focused on developed markets, little empirical evidences for emerging markets have been found, especially in Vietnam. This analysis allows us to answer the important question of whether the linkage of volume, return and volatility in the case of Vietnam market at both market level and firm level exists. 1.4 RESEARCH OBJECTIVES AND QUESTIONS To solve the research problem, this study has following objectives:  To explore the causal relationship of stock return and trading volume  To find out the trading volume effect on the return volatility The research problem defined above leads to the following research questions:  Is there any long run relationship between the trading volume and stock return in Vietnam?  Does the causal relationship between stock return and trading volume exist in Vietnam‟s stock market? If yes then, what is direction and extent of relationship between these variables? 5  Does ARCH effect exist in stock return of two indices? If yes then, is this ARCH effect weaker when trading volume is added as an explanatory variable in GARCH equation?  Does ARCH effect exist in individual stock return? If yes then, does this effect reduce when trading volume is included as an explanatory variable in GARCH equation? 1.5 RESEARCH METHODOLOGY AND SCOPE The Granger causality and ARCH/GARCH effect tests are employed to test the proposed hypotheses. Theoretically, these tests are only appropriate when the variables analyzed, including stock price, the index and trading volume, are stationary and co-integrated. Therefore, it becomes necessary to conduct various prior tests of integration and cointegration. In so doing, this thesis will apply the unit root test (specifically the augmented Dickey-Fuller tests). Following previous studies, this thesis will employ the Akaike Information Criterion (AIC) and Schwarz Information Criterion (SIC) to determine the optimal lag lengths. Data used in Granger causality and GARCH models are collected from two official sources, namely, Hochiminh Stock Exchange and Hanoi Stock Exchange during the May 2006 to July 2011 period. I also use the stock price of 37 large size (sorted in market capitalization) companies as my sample. Similar to most previous studies, this thesis will use the daily data to meet the required observations in GARCH models. More details in handling variables will be discussed in chapter three. 1.6 THESIS STRUCTURE In terms of structure, the thesis has five chapters. After defining the research problem, questions for the study in chapter one, chapter two reviews previous researches related to relationship between volume and price change. Chapter three discusses in detail about the methodology including the data collection and analysis methods, and