Retial investors’ trading behaviors and determinants: Evidence from the viet nam stock market

Nội dung text: Retial investors’ trading behaviors and determinants: Evidence from the viet nam stock market

1. RETIAL INVESTORS’ TRADING BEHAVIORS AND DETERMINANTS: EVIDENCE FROM THE VIET NAM STOCK MARKET Nguyen Thị Nhung, PhD.8, Tran Thi Van Anh, PhD. - University of Economics and Business – Vietnam National University Abstract: The research aims to identify factors influencing retail investors’ trading behavior or contrarian style in the Vietnam stock market. To be precise, the study is conducted by using a structured questionnaire from a sample of 239 prospective retail equity investors in Vietnam. And then, by using exploratory factor analysis (EFA), the research indicates that there is no evidence about the relationship between retail investors’ trading behavior and 5 factors, including attitude towards investment (ATI), financial risk tolerance (FRT), herding (HER), overconfidence (OVE) and heuristics (HEU) while self-attribution (SAT) has a positive impact on retail investors’ contrarian style. Besides, retail investors in the Vietnam stock market seem to follow contrarian investing rather than momentum investing. Keywords: Financial behaviors, behavioral psychology, individual investors, investment performance. 1. Introduction During the 25 years of establishment and development, the Vietnamese stock market has played an important role in the Vietnamese economy. The scale of Vietnam's stock market has continuously increased in terms of market capitalization, number of products traded on the market and the number of investors participating in the market. At the time of establishment of the stock market, there were only 3,000 accounts, mainly of retail investors, participating in trading on the market. According to the data of the Securities Depository Center, by the end of May 2021, the total number of investor’s trading accounts in the Vietnamese stock market is 3,216, 828, of which the trading accounts of retail investors is 3,204,944 and institutional investors is 11,884. In fact, retail investors make up a majority of Vietnam's stock market. Since retail investors are mostly amateurs, although they participate in large numbers, they have a lower degree of portfolio diversification, smaller trading size and volume compared to institutional investors. Retail investors in the Vietnamese stock market often do not have a long-term investment strategy and do not follow specific investment philosophies, so they are vulnerable and susceptible to many factors, especially psychological factors. 8 Email: nguyenthinhung.1684@gmail.com 99
2. In Vietnam, the impact of psychological factors on the investors in recent years is very complicated, causing negative consequences that threaten stock market stability and financial system security. Therefore, the objective of our paper is to identify factors influencing retail investors’ trading behavior and their investment performance in Vietnam. To be precise, the study is conducted by using a structured questionnaire from a sample of prospective retail equity investors in Vietnam. And then, by using exploratory factor analysis (EFA), the research evaluates how much psychological factors namely financial attitude, financial risk tolerance, herding effect, overconfidence and self-attribution as well as heuristics have impact on investors’ decision making as well as their investment returns. The research gives an empirical analysis on behavioral pattern of Vietnamese retail equity investors so that they can consider avoiding common mistakes made in their decisions. Our paper made two major contributions. Firstly, we have added the literature on applying the principles of behavioral finance to explain the trading behavior of retail investors in the stock markets. Up to this point, there have not been many studies on this topic in Vietnam. Secondly, we provide scientific evidence on the main psychological factors influencing on retail investors trading behavior and their investment performance in Vietnam’s Stock Market. To the best of our knowledge, this is the first study to provide systematic quantitative evidence on these correlations. Therefore, our research is a useful reference for investors to avoid mistakes when they make investment decisions. In addition, our research can also assist policy makers to make appropriate adjustments to ensure the stability of Vietnam's stock market in the future. This paper consists of six parts. The first part is the introduction. The second part provides literature review about retail investors, their trading behavior and investment performance. The methodology and data are presented in the third part. The results are shown in part 4. The discussion is given in part 5. Some conclusions as well as some limitations of the research are found in the last part. 2. Literature review The decline in efficiency of traditional financial theories has led to the emergence of a new financial theory called behavioral finance. This theory reduces the assumption of rationality in standard financial theory and explains that investors are really affected by their psychological tendencies. These biases are translated into their behavior so that they can make decisions below optimal. Behavioral finance is the combination of many different sciences such as finance, psychology, and sociology (Ricciardi & Simon, 2000). From a financial perspective, behavioral finance uses basic human psychological theories to explain irregularities in the financial market. According to the definition of Goldberg & Von Nitzsch (1999), finance is the theory of financial markets in which individuals act rationally within a certain framework. Thaler (1999) points out that behavioral finance is an integration of classical economics and standard financial theory and behavioral finance tries to complement standard financial theory through the inclusion of psychological factors in the decision-making process (Ritter, 2003). According to Shefrin (2001), since psychology can explore human judgment, behavior and welfare, it is also possible to provide explanations of human actions that differ from traditional economic assumptions According to Raiffa 100
3. &Raiffa (1968) and Kahneman &Tversky (1979) because individual's behavior in theory is often different from reality so that classical financial models cannot explain or predict it all financial decisions, therefore, it is necessary to apply the principles of behavioral finance. Investor trading behavior or investment behavior is defined as how the investors judge, predict, analyze and review the procedures for decision making, which includes investment psychology, information gathering, defining and understanding, research and analysis (Slovic, 1972; Alfredo &Vicente, 2010). Investor trading behavior could affect various asset prices, including future prices, option prices, stock prices (Yang & Zhou, 2015; Gao & Yang, 2017; Le Pen & Sộvi, 2017; Jena et al, 2017), stock return (Barber et al, 2009; Chen et al, 2014). This working paper focus on what investment strategies or styles they follow. There are two kinds of investment style, including momentum investing and contrarian investing. Contrarian investing is an investment style in which investors purposefully go against prevailing market trends by selling when others are buying and buying when most investors are selling. Contrarian investors believe that people who say the market is going up do so only when they are fully invested and have no further purchasing power. At this point, the market is at a peak. So, when people predict a downturn, they have already sold out, and the market can only go up at this point. In fact, a decision-making process is based on making choices that result in the optimal level of benefit or utility for an individual. A rational behavior should analyze and evaluate information comprehensively to succeed in their investment activities. To be precise, they usually take information and historical data into account before making decisions on investment. In terms of trading frequency, rational investors don’t have an optimal trading times per week but decide to buy or sell stocks based on what they predict about securities’ price movements. Opposed to contrarian investing, momentum investing exploits a tendency for a stock's prior returns and prior news about its earnings to predict future returns. According to Phansatan et al (2012) individual investors tend to be contrarians in several developing and developed markets while momentum investing mostly belongs to institutional investors. The type of investors might lead to different trading behavior, which results to differences in trading/investment performance. Retail investors differ from institutional investors by way of their investment size, resources, access to research, and professional advice (Bhattacharya et al., 2012). S. Phansatan et al. (2012) note that foreign and institutional investors tend to be better informed and financially sophisticated, whereas individual investors can be subject to psychological biases which limit their trading performance. Individual traders are generally found to have relatively poorer trading performance in comparison with institutional investors (Barber & Odean, 2000; Kamessaka et al, 2003; Keniel et al, 2005). Moreover, retail investors are strongly influenced by several rational and irrational factors while making decisions related to where and when to invest (Seth et al., 2020) so that it may leads to irrational decisions or prediction errors (Barber & Odean, 2000) and poorer investment performance. Individual investors also have a strong preference for selling stocks that have increased in value since bought (winners) relative to stocks that have decreased in value since bought (losers). This behavior is called the “disposition effect” by Shefrin & Statman (1985). 101
4. In terms of factors affecting retail investors’ trading behaviors, a number of studies have listed behavioral factors influencing trading investors’ behavior and his/her investment performance. Each study focuses on analyzing the impact of a number of factors affecting different investors on different markets. After conducting an overview of studies on the factors influencing the trading behavior and investment performance of individual investors, we focus on considering the main factors, namely financial attitude, financial risk tolerance, herding effect, overconfidence, and self-contribution. Financial attitude influences the trading behavior of retail investors (M. Talwar et al, 2021). Financial attitude is a psychological inclination, which manifests when individuals evaluate the well- established practices of financial management with varying degrees of acceptance or non-acceptance (Parrotta &Johnson, 1998). Shim et al. (2009) describe financial attitude as an expression of the individuals’ underlying knowledge of finance and their ability to manage decisions related to financial dealings. Financial attitude can serve as a metric of individuals’ financial knowledge, which can then be improved through education. If investors are lack of financial literacy (Lusardi & Mitchell, 2007), they maybe do not approach information on financial markets and may fall prey to behavioral biases (Kahneman et al, 1991) and trade too much (Barber & Odean, 2000), which in turn leads to sub-optimal financial decision making (Campbell, 2006, Goetzmann & Kumar, 2008) and low investment performance. Therefore, it is critical to examine the attitude of retail investors because their financial attitude (Grable & Lytton, 1998), along with their trading behavior and knowledge (Joo & Grable, 2004 can effect on their investment performance in particular and financial well-being in general (Falahati et al., 2012). Financial risk tolerance is the second factor, which may affect the trading behavior of retail investors. Financial risk tolerance refers to an individual’s willingness to accept the negative changes in the investment performance or an adverse outcome that is different from the expected one (Brable & Lytton, 1999. It is commonly defined as the maximum amount of volatility one is willing to accept when making a financial decision (Sulaiman, 2012). Financial risk tolerance is one among the factors that determine the risky behaviors of an individual. The high financial risk tolerance is in general a prerequisite for getting higher investment performance (Yao & Hanna, 2005; Yao & Wang, 2011). However, there is a possibility that an individual's wealth may decrease if he makes bad financial decisions (Grable et al., 2008). Many factors may influence financial risk tolerance such as demographic, social, environmental and psychological factors. However, there is a consensus among practitioners that demographic factors could be used to differentiate and classify retail investors (M.Kannadhasa, 2015). The demographic features of individual investors may be listed such as age, gender, marital status, occupation, income, experience, level of education, time horizon, liquidity needs, portfolio size, investment knowledge and attitude toward price fluctuations (Sulaiman, 2012). The third factor affecting investor’s trading behavior and investment performance is herding behavior or herding effect. It is a term indicating that investors and fund managers can adopt a risky investment strategy in the market without collecting sufficient information just because many other investors do so (Bikhchandani & Sharma, 2000). 102
5. In financial market herding effect occur when investors mimic the behavior of other investors or make investment decisions based on the views, judgments, or actions of others. An investor may have a good reason for having a herding behavior. For example, financial market analysts may consider the opinion of the majority because in this way they can avoid the risk of damage to their reputation. However, many investors copy the behavior of other investors in an unreasonable and sometimes completely irrational way. In particular, when they are caught up in behaviors that often occur when the stock market is at an advantageous stage, showing that the stock price is at their expected level, or when they are panicked in the face of an undervalued stock price. Sometimes, social conventions or customs also play a role in the appearance of herding behavior (Spyrou, 2013). Individual investors often show the herding behavior when making transactions on the stock market (Barber & Odean, 2009 and that affects their investment performance. Such behavior occurs when investors ignore their personal beliefs and act on the opinions and feelings of other investors through social interactions (Redhead, 2008). The herding effect among investors is a common psychological behavior that arises because of many fluctuations and changes in short-term trends that often take place in financial markets (Lakonishok et al, 1992; Chang et al, 2000; Hwang & Salmon, 2004; Demirer et al, 2010; Yao et al, 2014; Galariotis et al, 2015; Economu et al, 2016; Bensaùda, 2017; Kabir & Shakur, 2018). Herding behavior exists in both institutional investors and individual investors as well as in both developed financial markets and emerging financial markets (Mẻli & Roger, 2013; Barber et al, 2009; Balcilar et al, 2013; Rahman et al, 2015). Jamshidinavid et al. (2012) point out that herding psychology is more common in less confident individual investors. Women are generally less confident investors than men (Barber & Odean, 2001; Salem, 2019), so they are often strongly affected by herding behavior, or in other words, female investors tend to imitate the actions of other investors when making investment decisions, especially in emerging market (Lin, 2011; Choi, 2013). Overconfidence is another factor affecting investors' trading behavior as well as investment performance. Overconfidence is a state in which people tend to think they are better than they actually are (Trivers, 1991). An overconfident investor often sees himself better than other investors; he appreciates himself more than others value him and often exaggerates his own understanding. This behavior may lead overconfident investors to make trading more frequently than other investors (Wang, 1998; Glaser & Weber, 2007; Liu & Du, 2016). Investors who show excessive confidence in their trading behavior often assume that they can get greater profits during booms in the financial markets because of their skills, when they fail, they simply assume it is bad luck. The greater the investor's overconfidence, the higher the risk because they rarely diversify their portfolios and focus only on securities that they think they are familiar with without further reference on the appropriate views of other investors (Campbell et al, 2004; Lichtenstein et al, 1982). Barber & Odean (2001) question why investors tend to overestimate their skills and knowledge. The results show that the number of transactions made by male investors is about 45% higher than that of female investors. However, the study also shows that in practice, regular transactions reduce the net profit of investors. For male investors, the net profit decline due to regular trading is 2.65% 103
6. annually while this ratio is 1.72% for female investors. The authors also supported the previous research results of Daniel et al. (1998) confirming that the market was ineffective due to excessive investor confidence. Overconfidence may depend on many factors including individual characteristics such as investment experience, gender, level of education (Mishra A., Mary J. Metilda, 2015) and cultural differences. For example, Vissing-Jorgensen (2004) uses wealth and investor experience as proxies for investor sophistication, who has weaker irrational behavior. Overconfidence is shown in the research of Bikas et al. (2013) when they find that most of small investors believe that they have enough knowledge and experience in investing. Graham et al (2009) try to assess how this psychological manifestation determines trading frequency. The researchers have built an empirical model to find out what factors affect the capacity of investors. They find that investors who feel competent can conduct transactions more often and tend to have a more diversified portfolio. Male investors or investors with higher levels of education and a larger portfolio tend to consider them more knowledgeable, more capable than women or investors, who have smaller portfolios and lower levels of education. As a result, overconfident investors are more likely to consider themselves to be more competent and more likely to conduct transactions (Graham et al, 2009; Liu & Du, 2016). Overconfidence and self-attribution bias are mutually reinforcing as self-attribution leads individuals to become more overconfident and vice versa (Billett & Qian, 2008; Mỉha et al, 2015). The self-attribution bias refers to the tendency to credit oneself and one’s own abilities excessively with preceded successes and blame others or external factors for failures (Campbell & Sedikides, 1999; Zuckerman, 1979). The self-attribution bias can be divided into two components. While self- enhancement bias refers to acknowledging success, self-protective bias means abdicating responsibility for failures. In financial decision-making, self-attribution can have material impacts on investor’s trading behaviors (Gervais & Odean, 2001; Hilary & Menzly, 2006). Choi et al. (2017) examine whether overconfidence coupled with a self-attribution bias affects the investment decisions of top corporate managers. They find that overconfidence intensified by managerial self-attribution exacerbates the stickiness of investment-cash flow sensitivity (Choi & Liu, 2017). Czaja and Rửder (2020) investigate the self-attribution bias among nonprofessional traders and find that one component of the self-attribution bias, the self-enhancement bias, leads to future underperformance. They also find evidence showing that overconfidence resulting from biased self-enhancement as a possible driver (Czaja, 2020). Heuristics are the strategies derived from previous experiences with similar problems. There are three heuristics, including anchoring and adjustment, representativeness and availability. Anchoring and adjustment arise out of people’s tendency to estimate by starting from an initial guess and then adjusting the initial guess to arrive at the final estimate. Representativeness refers to people’s tendency to consider a characteristic to be the presentative of the whole of the phenomenon regardless of whether the said characteristic related to the phenomenon or not. And availability refers to the tendency to rely on already available information. 104
7. 3. Methodology 3.1. Research design By considering retail investors as financial consumers, this working paper uses different theories related to consumers’ behaviors to investigates factors influencing retail investors’ trading behavior and investment performance. Firstly, from the perspective of the the theory of planned behavior (TPB), attitude towards investment and subjective norms or herding effects are the major concepts and explanations of investment behaviors. Secondly, the prospect theory formulated in 1979 and further developed in 1992 by Tversky & Kahneman (1992) shows that financial risk tolerance is also an important determinant of trading behaviors (or contrarian investing). Finally, behavioral finance theories indicates that individuals are significantly affected by psychological factors such as cognitive biases in their decision making, rather than being rational and wealth- maximizing (Forbes, 2009). In other words, retail investors employ biases and heuristics in their decisions to invest or not, and how much to invest. In terms of biases, overconfidence and self- attribution are taken into account while heuristics include anchoring and adjustment, representativeness and availability. It can be obviously seen that six factors, including: attitude towards investments, subjective norms or herding, financial risk tolerance, overconfidence, self-attribution bias and heuristics are investigated in the direct relationship with retail investors’ trading behaviors on the Vietnam stock market. The research design is summarized in the figure 1 as below. Figure 1: Research design Source: Authors 3.2. Data collection and data analysis Data are gathered by conducting online survey which includes two main parts, including respondent profile and survey content. The first one asks interviewees to give general information about their individual demographics like education, income, occupation and experience. The second part have 42 questions in total. In order to make sure that questions are comprehensible to the respondents as well as the fact that there is no bias, this survey was initially sent to some authors’ friends, colleagues and relatives who are working in finance field to check. Based on their feedbacks, all questions were carefully revised and shortened before posting on social media (Facebook and Gmail). Besides online surveys, 105
8. the research is also supported by brokers who are working in some securities firms in Vietnam to deliver them to their individual clients. Data collection was carried out from the beginning of July until 19th July 2021. As the data collection phase was coming to an end, the researchers had successfully received a total of 1250 respondents. There are 239 valid responses which is totally appropriate to exploratory factor analysis (EFA). There are 06 hypothesis to verify, as follows: • Hypothesis 1 : Attitude towards investment (ATI) has an impact on retail investors’ trading behavior (TRB) on the Vietnam stock market. • Hypothesis 2 : Herding (HER) has an impact on retail investors’ trading behavior (TRB) on the Vietnam stock market. • Hypothesis 3 : Financial risk tolerance (FRT) has an impact on retail investors’ trading behavior (TRB) on the Vietnam stock market. • Hypothesis 4 : Overconfidence (OVE) has an impact on retail investors’ trading behavior (TRB) on the Vietnam stock market. • Hypothesis 5 : Self-attribution (SAT) has an impact on retail investors’ trading behavior (TRB) on the Vietnam stock market. • Hypothesis 6 : Heuristics (HEU) has an impact on retail investors’ trading behavior (TRB) on the Vietnam stock market. To complete the research objective, the article uses the exploratory factor analysis (EFA) in SPSS with 4 distinct steps. First of all, data have to be examined by Cronbach’ Alpha in SPSS. Cronbach’ Alpha is considered to be a measure of scale reliability (Amit, 2010). A reliability coefficient of 0.60 is considered “acceptable”. Simultaneously, data must have Corrected Item-Total Correlation equal or bigger than 0.3. In particular, in case the previous condition is satisfied but Cronbach Alpha if Item Deleted is bigger than Cronbach’ Alpha, data should be verified carefully. Secondly, after testing Cronbach’s Alpha in SPSS, the research only keeps appropriate factors by removing unsuitable variables from data. Suitable variables are introduced in SPSS to test EFA. Analysis results can be interpreted as bellow: (i) The Kaiser Meyer Olkin (KMO) measuring the sampling adequacy should be close than 0.5 for a satisfactory factor analysis to proceed; (ii) Bartlett’s test is another indication of the strength of the relationship among variables. This ratio should be less than 0.05 to reject the null hypothesis. In other words, correlation matrix is not an identity matrix; (iii) Eigenvalue actually reflects the number of extracted factors whose sum should be equal to number of items which are subjected to factor analysis. Factors with Eigenvalue bigger than 1 will be kept in analysis model. Total Variance Explained bigger than 50% indicate the appropriateness of EFA model. 106
9. Factor Loading indicates the correlation between the observation variable and the factor. The higher the factor loading is, the greater the correlation between the observation variable and the factor is and vice versa. Because of sample of 96, the authors use factor loading of 0.5. Thỉdly, the research computes variables in appropriate groups before correlation analysis. Correlation is a bivariate analysis that measures the strength of association between two variables and the direction of the relationship. In terms of the strength of relationship, the value of the correlation coefficient varies between +1 and -1. In this research, the authors use Pearson correlation to measure the degree of the relationship between linearly related variables. Last not but least, the regression analysis is used in order to estimate the relationship between a dependent variable and 7 independent variables. This analysis allows to determine which is the most important independent variable that has the highest impact on the firms’ decision-making of choosing bank. 4. Research Results 4.1. Descriptive Statistics about respondents Table 1 shows statistics about retail investors in Vietnam. Of 9239 respondents, 123 (51.46%) of them are women and the remaining 48.54% are men. 81.59% of the respondents are Millennials (21-34), followed by 17.15% of X Generation (35-49). The remaining rate (1.26%) are Z Generation in the age from 15 to 20. The majority of the respondents completed bachelor’s degree (69.87%) and postgraduate (28.45%). 1.68% of them completed high school or less. For occupation status, a total of 173 (72.38%) of the respondents are employed in the area of finance, banking and assurance while 25.52% are involved in other fields and 2.09% are unemployed. In terms of respondents’ marital status, most of them (60.67%) are single or divorced while only 94 got married (39.33%). Concerning respondents’income, total of 93 respondents (38.91%) receives from 10 million dong to 20 million dong per month, followed by 30.96% respondents having monthly income from 20 million dong to 40 million dong. 18.41% of respondents have monthly earnings less than 10 million dong. Only 11.72% respondents earn more than 40 million dong per month. Regarding investment activities, a majority of retail investors (63.60%) have invested on the Vietnam Stock Market for 2 years, followed by 19.25% respondents having from 2 to 4 years of investment experience. Only 41 respondents have experienced more than 4 years on the stock market. Moreover, 44.77% respondents said that their invested capital is from 100 million dong to 500 million dong while 33.47% respondents reserve less than 100 million dong for their investment portfolio. A total of 32 respondents have invested amount from 1 to 5 billion dong but only 4 people has a portfolio of more than 5 billion dong. Table 1: Demographic profile of retail investors Gender Marital Status Coun Coun Percent Percent t t 107
10. Man 116 48.54% Married 94 39.33% Woman 123 51.46% Unmarried 145 60.67% 100.00 100.00 Total 239 Total 239 % % Age Monthly Income Coun Coun Percent Percent t t 15 - 20 3 1.26% Less than 10 million dong 44 18.41% From 10 million dong to 20 million 21 - 34 195 81.59% 93 38.91% dong From 20 million dong to 40 million 35 - 49 41 17.15% 74 30.96% dong From 40 million dong to 60 million 50 - 64 0 0.00% 14 5.86% dong Over 64 0 0.00% More than 60 million dong 14 5.86% 100.00 100.00 Total 239 Total 239 % % Level of Education Working field Coun Coun Percent Percent t t Less than high 2 0.84% Area of finance, banking and assurance 173 72.38% school High school 2 0.84% Other fields 61 25.52% Bachelor's 167 69.87% Unemployed 5 2.09% Degree Postgraduate 68 28.45% Retired 0 0.00% 100.00 100.00 Total 239 Total 239 % % Years of investment experience Invested capital Coun Coun Percent Percent t t Less than 2 years 152 63.60% Less than 100 million dong 80 33.47% From 2 to 4 From 100 million dong to 500 million 46 19.25% 107 44.77% years dong From 4 to 6 15 6.28% From 5 million dong to 1 billion dong 16 6.69% years From 7 to 9 18 7.53% From 1 to 5 billion dong 32 13.39% years More than 10 8 3.35% More than 5 billion dong 4 1.67% years 108
11. 100.00 100.00 Total 239 Total 239 % % Source: Authors 4.2. Empirical Results ▪ Overview about retail investors’ trading behavior in the Vietnam stock market Figure 2 shows survey results about retail investors’ trading behavior in the Vietnam stock market. On average, all respondents choose scores more than 3 for explaining their agreement about statements given in an online survey. In other words, retail investors in the Vietnam stock market seem to follow contrarian investing. Figure 2: Survey results about retail investors’ trading behavior in the Vietnam stock market You are likely to buy much more when there is a downward trend in 3.29 stock prices on stock market. You believe that herdings can take control of market direction but doesnt 3.65 make for a good investing strategy. You always sell “hot” stocks since you believe that these stocks are 3.45 overpriced due to investors’ overrreaction to news developments. Your number of trading time per week depends on your prediction about 3.18 the movement on securities’ price. You often use historical data to estimate future values and invest on the 3.41 basis of these estimates and don’t use technical analysis. You believe that stock prices adjust quickly to all new publicly available 3.64 information. 2.90 3.00 3.10 3.20 3.30 3.40 3.50 3.60 3.70 Source: Survey results ▪ Determinants of retail investors’ trading behavior in the Vietnam stock market Cronbach’ Alpha is executed in SPSS for groups of factors related to ATI, FRT, HER, HEU, OVE, SAT and TRB. the research shows that four groups of factors, including ATI, FRT, HEU, OVE are inappropriate since a reliability coefficient is smaller than 0.60 while the remaining groups of factors are higher than 0.60. However, only HER1, HER2, HER 3, SAT1, SAT2, TRB2, TR3, TR4 are kept for the next steps because their corrected Item-total correlation are equal or bigger than 0.3 while HER4, TRB1, TRB5, TRB6 are removed because of corrected Item-total correlation of less than 0.3. Then, all appropriate factors are introduced in SPSS to test Exploratory Factor Analysis (EFA). Table 2: KMO and Bartlett’s Test for independent variables Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .698 Approx. Chi-Square 352.114 Bartlett's Test of Sphericity Df 10 Sig. <.001 Source: Exploratory Factor Analysis (EFA) extracted in SPSS 109
12. Table 2 shows the Kaiser Meyer Olkin (KMO) measuring the sampling adequacy of 0.698 bigger than 0.5 and less than 1.0. Moreover, Bartlett's Test of Sphericity Sig is less than 0.05. This means that it is possible to proceed a satisfactory factor analysis. In addition, total initial Eigenvalues of 73.657% (bigger than 50%) and total extraction sums of squared loadings of 1.156 (bigger than 1) indicates the appropriateness of EFA model for independent variables [Table 3]. Table 3: Total Variance Explained for independent variables Extraction Sums of Squared Rotation Sums of Initial Eigenvalues Loadings Squared Loadings Component % of Cumulative % of Cumulative Total Total Total Variance % Variance % 1 2.527 50.546 50.546 2.527 50.546 50.546 2.233 2 1.156 23.110 73.657 1.156 23.110 73.657 1.450 3 .538 10.756 84.413 4 .485 9.701 94.114 5 .294 5.886 100.000 Source: Exploratory Factor Analysis (EFA) extracted in SPSS Table 4 shows that there are two groups of independent factors, including HER and SAT. Table 4: Rotated Component Matrix for independent variables Component 1 2 HER2 .901 HER3 .841 HER1 .775 SAT2 .901 SAT1 .754 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.a a. Rotation converged in 3 iterations. Source: Exploratory Factor Analysis (EFA) extracted in SPSS Similarly, tables from 5 to 7 indicate that TRB is reliable for the next steps. Table 5: KMO and Bartlett’s Test for the dependent variable Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .594 Bartlett's Test of Sphericity Approx. Chi-Square 84.506 df 3 Sig. <.001 110
13. Source: Exploratory Factor Analysis (EFA) extracted in SPSS Table 6: Total Variance Explained for the dependent variable Initial Eigenvalues Extraction Sums of Squared Loadings Component Total % of Variance Cumulative % Total % of Variance Cumulative % 1 1.687 56.235 56.235 1.687 56.235 56.235 2 .785 26.166 82.401 3 .528 17.599 100.000 Source: Exploratory Factor Analysis (EFA) extracted in SPSS Table 7: Rotated Component Matrix for the dependent variable Component 1 TRB3 .829 TRB2 .719 TRB4 .695 Source: Exploratory Factor Analysis (EFA) extracted in SPSS Table 8 shows that Sig of SAT and TRB (<0.001) is smaller than 0.05, leading to the conclusion that there is a linear relationship between SAT and TRB. By contrast, Sig of TRB and HER are 0.805, bigger than 0.05. This means that there is no linear relationship between these two variables. In theory, HER should be removed. However, considering their important role and impacts on retail investors’ trading behavior, research decide to keep these variables in the next regression analysis. Table 8: Correlations TRB HER SAT TRB Pearson Correlation 1 -.016 .345 Sig. (2-tailed) .805 <.001 N 239 239 239 HER Pearson Correlation -.016 1 .161* Sig. (2-tailed) .805 .013 N 239 239 239 SAT Pearson Correlation .345 .161* 1 Sig. (2-tailed) <.001 .013 N 239 239 239 . Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). Source: Correlation Analysis extracted in SPSS 111
14. Table 9 figures that adjusted R Square of 0.125 means that 02 independent variables (including HER and SAT) have an impact on a change of 12,5% in dependent factor. 87,5% of change in dependent factor come from random or other variables, which has never been mentioned. And Durbin-Watson of 2.332 indicate positive autocorrelation. Table 9: Model Summary Adjusted R Std. Error of the Model R R Square Durbin-Watson Square Estimate 1 .353a .125 .117 .73185 2.332 a. Predictors: (Constant), HER, SAT b. Dependent Variable: TRB Source: Correlation Analysis extracted in SPSS Table 10 shows clearly that only SAT has a positive impact on TRB since sig is smaller than 0.05 and standardized coefficients is 0.357. This shows a very significant impact of self-attribution on retail investors’ contrarian style. In other words, only the hypothesis 5 is accepted while other 5 hypothesis are rejected. Table 10: Coefficients Unstandardized Standardized Collinearity Model Coefficients Coefficients t Sig. Statistics B Std. Error Beta Tolerance VIF 1 (Constant) 2.192 .262 8.361 <.001 SAT .330 .057 .357 5.791 <.001 .974 1.027 HER -.066 .055 -.074 -1.192 .234 .974 1.027 Source: Correlation Analysis extracted in SPSS 5. Discussions and conclusions Firstly, the research results indicate that retail investors in the Vietnam stock market seem to follow contrarian investing. This is totally supported by Phansatan et al (2012) when they show that individual investors tend to be contrarians in several developing and developed markets while momentum investing mostly belongs to institutional investors. Moreover, instead of exploiting a tendency for a stock's prior returns and prior news about its earnings to predict future returns, retail investors in the Vietnam stock market purposefully go against prevailing market trends by selling when others are buying and buying when most investors are selling. They also believe that people who say the market is going up do so only when they are fully invested and have no further purchasing power. Secondly, the research shows that psychological factor called self-attribution has a positive impact on retail investors’ contrarian investing in the Vietnam stock market. According to Mishra & Metilda (2015), selt-attribution is a cognitive phenomenon by which people tend to attribute success 112
15. to innate aspects such as talent and foresight, and attribute failures to situational factors. Mishra & Metilda (2015) mention two motives for self-attribution. The first one is the self-enhancing motivation which helps individuals protect their self-esteem by creating causal explanations that serve to make them feel better. The second one is the self-presentation motivations which refers to the drive to convey a desired image to others. There are two types of self-attribution, including self- enhancing bias and self-protecting bias. The former refers to the tendency of people to claim an irrational degree of credit for their success. The latter refers to the irrational denial of responsibility for failure. An investor who is susceptible to the self-attribution bias would attribute the rise in the value of an investment that is purchased to his/her being investment or business savvy and to bad luck or some external factor if it comes down in value. However, the limitation of this study is related to data. Firstly, this working paper use initial data collected from the beginning of July 2021 to 19 July 2021 with only 239 responses, compared to 3,204,944 of total trading accounts of retail investors in the Vietnam stock market. That’s why, this research result cannot fully manifest retail investors’ psychology and behavior. Secondly, the research used EFA as a main research method for determining factors influencing retail investors’ trading behavior on the Vietnam stock market, instead of Structural equation modelling (SEM) or network analysis used by other studies. Therefore, there is a need for further follow-up studies with a richer sample of data collected from retail investors in Vietnam as well as more modern and appropriate research methods like SEM or network analysis. Acknowledgment This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 502.99-2020.02 Reference Ackert, L. F., & Deaves, R. (2010). Behavioral finance: Psychology, decision-making, and markets. Mason, USA: South-Western Cengage Learning, . Alfredo Martin Oliver, Vicente Salas-Fumas. (2010). I.T. Investment and Intangibles: Evidence from Banks. SSRN Working Paper Series. B. M. Barber, T Oden. (2000). Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors. THE JOURNAL OF FINANCE. Balcilar, M., Demirer, R., & Hammoudeh, S. (2013). Investor herds and regime-switching: Evidence from Gulf Arab stock markets. Journal of International Financial Markets, Institutions and Money, 23, 295-321. Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. A Quarterly Journal of Economics, 2, 261-292. Barber, B. M., Odean, T., & Zhu, N. (2009). Do retail trades move markets? Review of Financial Studies, 22(1), 151-186. 113
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