Behavioral factors affecting herding bias, the case of ho chi minh stock exchange, vietnam

UNIVERSITY OF ECONOMICS HO CHI MINH CITY International School of Business  Doan Thi Mai Phuong BEHAVIORAL FACTORS AFFECTING HERDING BIAS: THE CASE OF HO CHI MINH STOCK EXCHANGE, VIETNAM SUPERVISOR: Prof Nguyen Dong Phong Dr Nguyen Phong Nguyen HO CHI MINH CITY - 2015 TABLE OF CONTENTS CHAPTER 1: INTRODUCTION 1.1 Background 1.2 Problem statement 1.3 Research question 1.4 Research scope 1.5 Research methods 1.6 Significance of the research 1.7 Structure of the study CHAPTER 2: LITERATURE REVIEW 10 2.1 Theorical background 10 2.2 Review on some behavioral factors and herding bias in stock market 12 2.2.1 Risk tolerance 12 2.2.2 Over-confidence 13 2.2.3 Self-monitoring 15 2.2.4 Gambler’s fallacy 16 2.2.5 Illusion of control bias 16 2.2.6 Herding bias 17 2.3 Hypothesis development 20 CHAPTER 3: RESEARCH METHODOLOGY 25 3.1 Measurement scales 25 3.1.1 Scales measurement of Risk Tolerance 25 3.1.2 Scales measurement of Over-confidence 25 3.1.3 Scale measurement of Self-monitoring 26 3.1.4 Scale measurement of Gambler’s Fallacy 27 3.1.5 Scale measurement of Illusion of control 28 3.1.6 Herding bias 28 3.2 Sampling 29 3.3 Data collection methods 29 3.4 Data analysis methods 30 3.4.1 Test of scale measurement reliability 30 3.4.2 Exploration factor analysis (EFA) 31 CHAPTER 4: DATA ANALYSIS AND FINDINGS 33 4.1 Descriptive statistics: 33 4.2 Refinement of measurement scales 34 4.2.1 Result of Cronbach’s alpha analysis of formal survey (N=205) 34 4.2.2 Factor analysis (EFA) 35 4.3 Testing the assumptions of regression 39 4.4 Results of hypothesis testing 40 4.4.1 Regression model 40 4.5 Chapter summary 43 CHAPTER 5: CONCLUSIONS AND IMPLICATIONS 44 5.1 Main Findings 44 5.2 Managerial Implications 45 5.3 Limitations 47 REFERENCES 48 APPENDICES 57 ABSTRACT The stock market is more and more unpredictable which traditional finance theories cannot give reasonable explanation.Behavioral finance, instead, can be helpful in the current situation of the fluctuating market Herding activities among investors have been a popular behavioral explanation for the excess volatility and short term trends observed in financial market However, the number of research focusing on herding and its impacts on the financial market, especially Vietnamese stock market, is limited.This research will identifythe behavioral factors affecting the herding bias existing at the HOSE since it was established (2000) and the impact levels of behavioral elements on the herding bias Using a data set form a sample of more than 200 investors, we find that investors’ decisions much depends on their own emotion rather than fundermental analysis and technical analysis Moreover, investors generally prefer short-term portfolios (T+3) to long-term ones In addition,due to the limited level of security understanding, investors usually consider the stock market as a “casino” where luck is the most vital element to win On the other hand, investorswho are too confident about their investment decision usually make mistakes in their short-term investment Keywords: Herding behavior, behavioral finance CHAPTER 1: INTRODUCTION 1.1 Background Vietnamese stock market was formed in 1998 including Ho Chi Minh City Stock Exchange (HOSE) and Hanoi Stock Exchange (HNX) At the very first stage, the Vietnamese stock market was launched on 28 July 2000 with merely listed companies along with security companies and the growth of the number of listed companies was quite slow Having passed so many upsand-downs, there are currently 181 security-company members1after approximately 14 years However, in comparison to other developed and emerging markets, Vietnamese stock market appears to be much smaller in terms of scale and maturity Although the Ho Chi Minh City stock exchange (HOSE) has witnessed considerable developments both in the number of listed stocks and in transaction value for 14 years, the price movement seems to fluctuate unpredictably over different periods Starting at 100 points in July 2000, after year, at June 2001, the VN-Index was fivefold and reached the peak at 571 points Investors were too excited and dreamed of earning money quickly that the demand rose significantly while there were only few listed stocks in the market (Huy, 2010) However, it was the lack of knowledge and trading experience of investors as well as the insufficient support from the authorities which made VN-Index suddenly unceasingly fall until it reached the bottom at 139 points in March 2003 (Huy, 2010) Investors who joined the market in this period and could not jump out quickly had to face the financial difficulties because of the huge loss of assets The stock market then seemed to fall in its hibernation status until 2005 and eventually woke up in 2006 The boom started in the second half of 2006 and 1According to the latest available data as at 20 May 2014 on the websites of Ho Chi Minh City and Ha Noi stock markets rocketed up to 1,170 points by March 2007 This fluctuated around 1,000 points until October 2007 The Ho Chi Minh City stock market had never been “hotter” than that time Nevertheless, VN-Index went to its decline stage after being pushed up to the peak Ho Chi Minh City stock market experienced a gloomy year in 2008 and VN-Index merely stopped falling in February 2009 at 235 points (Huy, 2010) The possible explanations for the sharp dip of the VN-Index were the impacts of numerous factors, namely, tightening of monetary policies, especially lending for stock investment, high deposit interest rates, high inflation rate, and a recession of the United State economy Moreover, lack of timely intervention by the authorities was also a breeding ground of the dramatically decrease of VN-Index (Vo and Pham, 2008) From 2009 to the first quarter of 2011, VN-Index continued to undergo many ups-and-downs, reached another peak at 542 points in May 2010 and another bottom at 351 points in November 2011 Nonetheless, it seemed to fluctuate between 400 and 500 points with no significant amplitude found in 2012 and finally stood at 505 points on December 31st 2013, increasing by 23% compared to 2012 (Luyen, 2013).According to Phu(2010), one of the most important factor resulting in the fluctuation of stock prices is the herding bias Specifically, when the price of stock increases, a huge number of investors begin buying with the hope that the price will continue to rise However, the price only goes up to a specific point and after that will go down When the price decreases, be afraid that the price decline unceasingly, almost investors will sell with high selling pressure In the other words, investors seem to takes action followed their psychology and emotion rather than rational mind which does not make the market complied with supply-demand rule and may lead to the collapse of the market To explain these fluctuations during the period, I assume that herding bias may be the most important factor leading to this phenomenon in the case of Vietnamese stock markets; therefore, it should be analyze in depth Regarding the theoretical background on behavioral factors and herding bias, if Expected Utility Theory (EUT) may be deemed by a fundamental theory of traditional finance, prospect theory is the basic theory of the behavioral finance Prospect theory concentrates on subjective decision-making affected by the investors’ value system, while EUT focuses on investors’ rational expectations (Filbeck, Hatfield & Horvath, 2005) EUT is the normative model of rational choices and descriptive model of economic behaviors, which dominate the analysis of decision making under risk However, this theory is criticized for failing to explain why people are appealed to both insurance and gambling People tend to under-weigh probable outcomes compared to certain ones and they differently response to the similar situation depending on the context of losses and gains in which they are presented (Kahneman & Tversky, 1979) Prospect theory describes some states of mine affecting an individual’s decision making process including Regret aversion, Loss aversion and Mental accounting (Waweru et al, 2003, p.28) Based on the background, the problem statement would state my concern about the impact of behavioral factors on herding bias in the case of Vietnamese stock market 1.2 Problem statement Only recently established in 2000, the Vietnamese stock market is characterized by weak reporting requirements, poor regulations and low accounting standards (Tran and Truong, 2011) During the formulation and development process, the Vietnamese stock exchange in general and the HOSE in particular went through many different stages Specifically, its prices fluctuate unpredictably and it seems difficult for investors to make rational decisions Despite the dramatic fluctuations of price through the period as well as the instability of the market, there have been few studies of the Vietnamese stock exchange, particularly dealing with herding issues Although a variety of commentaries based on the conventional financial theories have been proposed, they failed to explain what happened at the HOSE over the past period Alternatively, behavioral finance can be helpful in the case of the HOSE since it is based on the psychology to explain why people buy or sell stocks (Waweru et al., 2008) Behavioral factors include overconfidence, representativeness, availability, loss aversion, regret aversion, gamble’s fallacy, over-under reaction, herding and so on (Ritter, 2003) Herding activities among investors have been a popular behavioral explanation for the excess volatility and short term trends observed in financial market (Juan Yao et al., 2014) Moreover, many studies carried out in Vietnamese stock market such as Farber et al (2006), and Tran (2007)identified that herding effect in this market is very strong, especially toward positive return of the market Specifically, Farber et al (2006) conducted a study aboutthe impact of policy on Vietnam stock market and found out the empirical evidence for the well-known herding behavior among investors, by which people suppressed own private information and expectations to follow the market’s collective action They also stated that “the trend of herding behavior is stronger toward extreme positive returns of the market, and in fact, around the consecutive sequence of limit-hits” (Farber et al, 2006, p.25) Besides, Chen et al (2003) argued that herding was more likely to happen in emerging markets than developed ones as the government intervention was high, and the quality of information disclosure was low.Because of this intervention of the government, the market did not operate following the “demand-supply” rule; therefore, there was no random trend in the market As regard the low quality of information disclosure in emerging markets, it was the fact that individuals in these markets did not have sufficient official information Hence, they always followed the rumor or unofficial information, which resulted in the instability of markets Kaminsky and Schmukler (1999) asserted that during 1997-1998, Asian countries seemed to be driven by herding behavior; therefore, it was believed that the herding instinct was very strong in Vietnam.Tran and Truong (2011) shown that herding behavior in Vietnamese stock market was evident Specifically, investors had a tendency to follow the actions of those who were believed to be better informed They argued that the inadequacy of the regulatory framework in Vietnamese market, for example, the lack of transparency or an efficient mechanism for reporting information prevented investors from collecting accurate and rapid firm-specific information for their own evaluation Hence, this informational inefficiency together with a relatively high degree of market volatility might induce investors to make decisions based on consensus, which lead to higher correlations among stock returns The dispersion among returns was, therefore, likely to decrease or at least increase at a decreasing rate As a result, herding was observed Thus, the study aims to analyze herding effect and factors impacting it To understand and give several suitable explanations for the herding bias in the Vietnamese stock market, it is vital to explore which behavioral factors affecting the herding bias at the HOSE and how these factors affect the herding bias It would be useful for investors to understand common investment behaviors, from which justify their decisions for better returns Security companies can use this information for the better understanding about investors to forecast more exactly and issue better recommendations Above all, stock price would reflect its true value and the HOSE would also become the yardstick of the economy prosperity as well as help corporations raise capital for production and expansion 1.3 Research question This research identifies the behavioral factors affecting the herding bias existing at the HOSE over the period surveyed since the HOSE was established (2000) Furthermore, identifying the impact levels of behavioral elements on the herding bias at the HOSE is another purpose of this research For these reasons,this study proposes the following research question: What are the behavioral factors affecting investors’ herding bias in the Vietnamese stock market? 1.4 Research scope As mentioned above, the Vietnamese stock market solely includes two stock exchanges, namely, Ho Chi Minh City Stock Exchange (HOSE) and Hanoi Stock Exchange (HNX) In this study, the HOSE would be taken into account because Ho Chi Minh City is the biggest and the most unceasingly developing city in Vietnam Besides, the HOSE has become the yardstick of the economy wealth as well as the most effective channel helping enterprises raise capital for production and expansion (Luong and Ha, 2011) Above all, selecting the HOSE for this study provides a more convenient approach to collect data, enhance the accuracy of survey information as well as offer more reliable interview data due to the budget and time constraints 1.5 Research methods This research will employ quantitative methods In particular, the research process will include a pilot study and a survey The quantitative research will be conducted by sending survey forms to more than 200 investors investing in the HOSE In this study, the hypotheses will be built based upon the existing behavioral finance theories In particular, the quantitative method will initially be presented to test these hypotheses To check the reliability of the scales measurement, the research will use SPSS software to test Cronbach’s Alpha based on standardized items In the next step, this paper will test scales measurement validity: factor analysis – EFA (Exploratory Factor Analysis) Finally, the paper will apply regression for testing the proposed model and hypotheses 2.207.1 2.208.1 2.209.1 2.210.1 2.211.1 X43 Some gamblers are just born lucky 2.212.1 2.213.1 2.214.1 2.215.1 2.216.1 X44 Wins are more likely to occur on a hot machine X45 A good casino gambler is like a quarterback who knows 2.217.1 2.218.1 2.219.1 2.220.1 2.221.1 winning plays and when to use them X46 It is good advice to stay with the same pair of dice on a 2.222.1 2.223.1 2.224.1 2.225.1 2.226.1 winning streak X47.Show me a gambler with a well-planned system and I would 2.227.1 2.228.1 2.229.1 2.230.1 2.231.1 show you the winner 2.232.1 2.233.1 2.234.1 2.235.1 2.236.1 X48.The longer I've been losing, the more likely I am to win X49.The more familiar I am with a casino game, the more likely I 2.237.1 2.238.1 2.239.1 2.240.1 2.241.1 am to win 2.242.1 2.243.1 2.244.1 2.245.1 2.246.1 X50 One should pay attention to lottery numbers that often win 2.247.1 2.248.1 2.249.1 2.250.1 2.251.1 Herding bias friend's 2.252.1 2.253.1 2.254.1 2.255.1 2.256.1 Y52 I would bid the securities whose prices have risen for a 2.257.1 2.258.1 2.259.1 2.260.1 2.261.1 Y51 I would buy shares by following my recommendation period 2.262.1 2.263.1 2.264.1 2.265.1 2.266.1 Y53 I would bid the same stocks as my friends 2.267.1 2.268.1 2.269.1 2.270.1 2.271.1 Y54 I would follow the market information to trade Y55 I would invest in shares by following technical analysis (e.g 2.272.1 2.273.1 2.274.1 2.275.1 2.276.1 share price movements, trading volume) 2.277.1 2.278.1 2.279.1 2.280.1 2.281.1 Y56 I would invest in shares by following the media 2.282.1 2.283.1 2.284.1 2.285.1 2.286.1 Y57 I rely on expert's recommendation to trade shares Y59 I rely on information from friends and relatives to trade 2.287.1 2.288.1 2.289.1 2.290.1 2.291.1 shares 60 APPENDIX CRONBACH’S ALPHA; EXPLORATORY FACTOR ANALYSES AND MULTIPLES REGRESSION RESULT Risk Tolerance 61 Over-confidence 62 Self-monitoring 63 Gambler's Fallacy 64 Illusion of control 65 66 Herding bias 67 EFA for all 68 69 APPENDIX EFA and Reliability Test Results – Scales Without Modification Factor loading Risk Tolerance %variance extracted Eigen-value 46.12 1.384 Item-total correlation 0.658 RIS3 0.501 0.416 RIS4 0.946 0.598 RIS5 0.487 0.405 72.77 Over-confidence 1.456 0.61 CONFI6 0.853 0.456 CONFI7 0.853 0.456 45.387 Self-monitoring 2.269 0.689 SEMO7 0.644 0.408 SEMO11 0.596 0.378 SEMO13 0.598 0.367 SEMO14 0.807 0.611 SEMO16 0.701 0.478 76.852 Gambler's Fallacy 1.537 0.699 GAM2 0.877 0.537 GAM3 0.877 0.537 51.729 Illusion of Control 2.586 0.766 ILLUS3 0.519 0.451 ILLUS5 0.695 0.582 ILLUS7 0.631 0.534 ILLUS10 0.649 0.559 ILLUS11 0.652 0.55 62.034 Herding Bias 2.481 0.795 HERD1 0.734 0.543 HERD2 0.823 0.654 HERD3 0.834 0.67 HERD8 0.755 0.566 Factor %variance Eigen-value Cronbach’s alpha Item-total Cronbach’s 70 loading extracted 46.12 Risk Tolerance correlation 1.384 0.658 RIS3 0.501 0.416 RIS4 0.946 0.598 RIS5 0.487 0.405 72.77 Over-confidence 1.456 0.61 CONFI6 0.853 0.456 CONFI7 0.853 0.456 45.387 Self-monitoring 2.269 0.689 SEMO7 0.644 0.408 SEMO11 0.596 0.378 SEMO13 0.598 0.367 SEMO14 0.807 0.611 SEMO16 0.701 0.478 76.852 Gambler's Fallacy 1.537 0.699 GAM2 0.877 0.537 GAM3 0.877 0.537 51.729 Illusion of Control 2.586 0.766 ILLUS3 0.519 0.451 ILLUS5 0.695 0.582 ILLUS7 0.631 0.534 ILLUS10 0.649 0.559 ILLUS11 0.652 0.55 62.034 Herding Bias alpha 2.481 0.795 HERD1 0.734 0.543 HERD2 0.823 0.654 HERD3 0.834 0.67 HERD8 0.755 0.566 71 APPENDIX Original scale Refind scale Factor Item-total Factor loading correlation loading correlation RIS1 0.349 0.288 Eliminated Eliminated RIS2 0.304 0.261 Eliminated Eliminated RIS3 0.605 0.476 0.501 0.416 RIS4 0.774 0.55 0.946 0.598 RIS5 0.527 0.408 0.487 0.405 % variance extracted 29.148 46.12 Eigen value 1.457 1.384 Cronbach's alpha 0.643 0.658 Items Item-total Risk Tolerance Over-confidence CONFI1 0.53 0.262 Eliminated Eliminated CONFI2 0.787 0.197 Eliminated Eliminated CONFI3 0.429 0.315 Eliminated Eliminated CONFI4 0.095 0.121 Eliminated Eliminated CONFI5 0.163 0.223 Eliminated Eliminated CONFI6 0.352 0.37 0.853 0.456 CONFI7 0.35 0.284 0.853 0.456 % variance extracted 19.53 72.77 Eigen value 1.367 1.456 Cronbach's alpha 0.514 0.61 Self-monitoring SEMO1 0.45 0.17 Eliminated Eliminated SEMO2 0.325 0.247 Eliminated Eliminated SEMO3 0.274 0.67 Eliminated Eliminated SEMO4 0.439 0.277 Eliminated Eliminated SEMO5 0.401 0.286 Eliminated Eliminated SEMO6 0.51 0.47 Eliminated Eliminated SEMO7 0.46 0.181 0.644 0.408 SEMO8 0.456 0.304 Eliminated Eliminated 72 SEMO9 0.525 0.438 Eliminated Eliminated SEMO10 0.582 0.577 Eliminated Eliminated SEMO11 0.309 0.314 0.596 0.378 SEMO12 0.43 0.081 Eliminated Eliminated SEMO13 0.411 0.369 0.598 0.367 SEMO14 0.566 0.323 0.807 0.611 SEMO15 0.393 0.173 Eliminated Eliminated SEMO16 0.499 0.353 0.701 0.478 SEMO17 0.4 0.189 Eliminated Eliminated SEMO18 0.567 0.298 Eliminated Eliminated % variance extracted 18.653 45.387 Eigen value 3.358 2.269 Cronbach's alpha 0.656 0.689 Gambler's Fallacy GAM1 0.142 0.161 Eliminated Eliminated GAM2 0.714 0.241 0.877 0.537 GAM3 0.794 0.443 0.877 0.537 GAM4 0.122 0.141 Eliminated Eliminated % variance extracted 39.625 76.852 Eigen value 1.585 1.537 Cronbach's alpha 0.426 0.699 Illusion of control ILLUS1 0.028 0.12 Eliminated Eliminated ILLUS2 0.146 0.125 Eliminated Eliminated ILLUS3 0.586 0.533 0.519 0.451 ILLUS4 0.532 0.4 Eliminated Eliminated ILLUS5 0.664 0.583 0.695 0.582 ILLUS6 0.562 0.428 Eliminated Eliminated ILLUS7 0.612 0.481 0.631 0.534 ILLUS8 0.637 0.5 Eliminated Eliminated ILLUS9 0.341 0.291 Eliminated Eliminated ILLUS10 0.649 0.609 0.649 0.559 73 ILLUS11 0.467 0.652 % variance extracted 0.569 32.040 Eigen value 3.524 2.586 Cronbach's alpha 0.753 0.766 0.55 51.729 Herding bias HERD1 0.744 0.445 0.734 0.543 HERD2 0.8 0.626 0.823 0.654 HERD3 0.846 0.541 0.834 0.67 HERD4 0.139 0.509 Eliminated Eliminated HERD5 0.026 0.312 Eliminated Eliminated HERD6 0.421 0.475 Eliminated Eliminated HERD7 0.195 0.58 Eliminated Eliminated HERD8 0.661 0.755 0.566 % variance extracted 0.631 43.073 Eigen value 3.446 2.481 Cronbach's alpha 0.805 0.795 62.034 74 ... al Lin Sign of effect The behavioral factors of investors affecting the herding bias in the Vietnam stock market The research hypothesises that the four behavioral factors affecting herding bias... at the HOSE over the period surveyed since the HOSE was established (2000) Furthermore, identifying the impact levels of behavioral elements on the herding bias at the HOSE is another purpose of. .. show the relevance of the research model with the stock market and the accepted theory gives some practical significance of these factors impacting on Herding bias According to the results of