INTRODUCTION
Background
The Vietnamese stock market, established in 1998 with the Ho Chi Minh City Stock Exchange (HOSE) and Hanoi Stock Exchange (HNX), officially launched on July 28, 2000, with just two listed companies and four securities firms Over the next 14 years, the market grew to include 181 securities company members, yet it remains smaller in scale and maturity compared to developed and emerging markets Despite significant developments in the number of listed stocks and transaction values on HOSE, price movements have been unpredictable The VN-Index started at 100 points in July 2000, surged to 571 points by June 2001, driven by investor excitement and high demand, only to plummet to 139 points by March 2003 due to a lack of investor knowledge, experience, and insufficient regulatory support.
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
As of May 20, 2014, the Ho Chi Minh City and Ha Noi stock markets experienced significant growth, reaching 1,170 points by March 2007 Following this peak, the index stabilized around 1,000 points until October 2007, marking a notable period in the markets' performance.
The VN-Index experienced a significant decline after reaching its peak, marking a challenging year for the Ho Chi Minh City stock market in 2008 The index only stabilized in February 2009 at 235 points, influenced by several factors including tightened monetary policies, high deposit interest rates, soaring inflation, and the recession in the United States Additionally, the absence of timely intervention from authorities contributed to the drastic drop in the VN-Index.
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 31 st
In 2013, stock prices experienced a significant increase of 23% compared to 2012 (Luyen, 2013) According to Phu (2010), herding bias is a critical factor influencing stock price fluctuations When stock prices rise, many investors rush to buy, hoping for continued growth; however, prices eventually peak and begin to decline Conversely, when prices fall, fear of further declines prompts investors to sell, creating high selling pressure This behavior indicates that investors often act based on psychology and emotion rather than rational decision-making, disrupting the market's adherence to supply and demand principles and potentially leading to market collapse Given these dynamics, herding bias appears to be a key contributor to the fluctuations observed in the Vietnamese stock market, warranting a thorough analysis.
Expected Utility Theory (EUT) serves as a foundational concept in traditional finance, while prospect theory underpins behavioral finance by emphasizing how subjective decision-making is influenced by investors' value systems Unlike EUT, which centers on rational expectations and serves as both a normative model for rational choices and a descriptive model for economic behaviors, prospect theory addresses the psychological factors that drive investor decisions Critics of EUT point out its limitations, particularly its inability to explain the simultaneous attraction to both insurance and gambling Additionally, individuals tend to undervalue probable outcomes in favor of certain ones and respond differently to similar situations based on the context of gains and losses.
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.
Problem statement
Established in 2000, the Vietnamese stock market is marked by weak reporting requirements, inadequate regulations, and low accounting standards (Tran and Truong, 2011) Throughout its development, particularly the HOSE, the market has experienced various stages characterized by unpredictable price fluctuations, making it challenging for investors to make informed decisions Despite these dramatic price changes and overall market instability, there is a notable lack of research on the Vietnamese stock exchange, especially concerning herding behavior among investors.
Traditional financial theories have struggled to clarify the recent events at the HOSE, highlighting the need for alternative approaches Behavioral finance offers valuable insights by focusing on the psychological factors that influence stock trading decisions Key behavioral elements such as overconfidence, representativeness, availability, loss aversion, regret aversion, gambler’s fallacy, overreaction, underreaction, and herding can significantly impact investor behavior and market dynamics.
Herding behavior among investors is a significant factor contributing to excess volatility and short-term trends in financial markets (Juan Yao et al., 2014) Research on the Vietnamese stock market, including studies by Farber et al (2006) and Tran (2007), has demonstrated a strong herding effect, particularly in response to positive market returns Farber et al (2006) highlighted how policy impacts in Vietnam lead to herding behavior, where investors often disregard their private information and expectations to align with the collective actions of the market.
“the trend of herding behavior is stronger toward extreme positive returns of the market, and in fact, around the consecutive sequence of limit-hits”
Chen et al (2003) suggested that herding behavior is more prevalent in emerging markets compared to developed markets due to higher government intervention and lower quality of information disclosure This government involvement disrupts the market's ability to function efficiently.
“demand-supply” rule; therefore, there was no random trend in the market
The low quality of information disclosure in emerging markets, particularly in Vietnam, has led investors to rely on unofficial sources and rumors, causing market instability Kaminsky and Schmukler (1999) noted that during the 1997-1998 Asian financial crisis, herding behavior was prevalent, with Vietnamese investors often mimicking the actions of perceived better-informed individuals Tran and Truong (2011) highlighted that the inadequacy of the regulatory framework, including a lack of transparency and efficient information reporting mechanisms, hindered investors from accessing timely and accurate firm-specific data This informational inefficiency, coupled with high market volatility, prompted investors to make consensus-based decisions, resulting in increased correlations among stock returns and a reduction in return dispersion Consequently, herding behavior became evident in the Vietnamese stock market, prompting the need to analyze its effects and influencing factors.
To comprehend the herding bias in the Vietnamese stock market, it's essential to investigate the behavioral factors influencing this phenomenon at the HOSE Understanding these factors can help investors recognize common investment behaviors, ultimately guiding them in making informed decisions that lead to improved returns.
Security companies can use this information for the better understanding about investors to forecast more exactly and issue better recommendations
The stock price serves as an accurate indicator of a company's true value, while the HOSE acts as a benchmark for economic prosperity, facilitating capital raising for corporations to enhance production and expansion.
Research question
This research explores the behavioral factors influencing herding bias at the Ho Chi Minh Stock Exchange (HOSE) since its inception in 2000 Additionally, it aims to assess the extent of these behavioral elements' impact on herding bias within the HOSE.
For these reasons,this study proposes the following research question: What are the behavioral factors affecting investors’ herding bias in the Vietnamese stock market?
Research scope
The Vietnamese stock market consists of two main exchanges: the Ho Chi Minh City Stock Exchange (HOSE) and the Hanoi Stock Exchange (HNX) This study focuses specifically on the HOSE for analysis.
Ho Chi Minh City is Vietnam's largest and fastest-growing city, with the Ho Chi Minh Stock Exchange (HOSE) serving as a key indicator of economic wealth and a vital channel for businesses to secure funding for growth and production (Luong and Ha, 2011) Choosing the HOSE for this study facilitates easier data collection, improves the accuracy of survey responses, and ensures more reliable interview data, all while considering budget and time limitations.
Research methods
This study will utilize quantitative research methods, incorporating a pilot study and a survey The research will involve distributing survey forms to over 200 investors in the HOSE, aiming to gather valuable data on their investment behaviors.
This study aims to formulate hypotheses grounded in established behavioral finance theories, utilizing a quantitative approach for testing To ensure the reliability of measurement scales, the research will employ SPSS software to calculate Cronbach’s Alpha using standardized items Subsequently, the validity of the measurement scales will be assessed through Exploratory Factor Analysis (EFA).
Finally, the paper will apply regression for testing the proposed model and hypotheses.
Significance of the research
This research has several meanings for individual investors, security organizations, the field of behavioral finance as well as for the author
Individual investors can benefit from research as it serves as a valuable reference for understanding stock investment behavior By analyzing market trends and recognizing the presence of herding bias, investors can make informed and suitable investment decisions.
The research offers security companies valuable insights into the behavioral factors influencing herding bias, enabling them to enhance the accuracy of their forecasts and provide more dependable advice to investors.
Behavioral finance, while relatively new compared to traditional financial theories, has seen widespread application in developed stock markets However, its implementation in less developed markets remains limited, particularly in the context of Vietnam, where research on herding behavior is scarce This study aims to demonstrate the relevance of behavioral finance across all types of stock markets and to contribute significantly to future research on the herding effect.
Structure of the study
The proposal of this study has 3 chapters:
Chapter 1: Introduction This chapter mentions two parts The first part involves a background of the HOSE from the initial stages to the present The second one presents the problem statement and proposes the reasons for choosing the topic
Chapter 2: Literature review The content of this chapter is reviewing behavioral factors impacting herding bias in previous studies Behavioral elements, namely, representativeness, monitoring, anchoring and so on Then, the paper suggests studying 5 behavioral factors: risk tolerance, overconfidence, self-monitoring, availability bias, and gambler’s fallcacy Moreover, the hypothesis development would also be proposed in this section
Chapter 3: Methodology Being presented throughout this chapter is the quantitative research method involving the survey approach After attaining relevant and reliable questionnaires, I would surveymore than 200 market participants Besides, I also presented the items measuring variables used in the questionnaires
After that, I would test the scale measurement sand employ regression to test the proposed model and hypotheses
Chapter 4: Data analysis and finding
This chapter presents the descriptive statistics of the data and the refinement of measurement scales It specifically highlights the results of Cronbach’s alpha and the factor analysis conducted on the formal survey.
Furthermore, the results of testing the assumption of regression and hypothesis testing are also mentioned
At the end of the chapter, a general summary would be stated
Chapter 5: Conclusions and implications Throughout this chapter, I would present a summary of main findings, discuss the meaning and contribution in investment practice
To finish the thesis, a limited number of topics and further research direction would be also stated
LITERATURE REVIEW
Theorical background
The Efficient Market Hypothesis (EMH), introduced by Fama in 1970, posits that markets are efficient and that current prices reflect all available information, making it impossible for investors to consistently achieve excess returns Similarly, the Random Walk Model (RWM) suggests that past price movements do not provide any advantage in predicting future returns, reinforcing the idea that market prices are inherently unpredictable Together, these theories have shaped financial literature over the past three decades, highlighting the challenges of outperforming the market.
According to the EMH theory, although not all investors are reasonable, the markets are supposed to be reasonable and make unbiased forecasts Fama
In 1970, the Efficient Market Hypothesis (EMH) was categorized into three forms based on the type of information available The weak form efficiency suggests that current asset prices incorporate all historical financial data accessible in the market In contrast, the semi-strong form indicates that stock prices quickly and objectively adjust to new publicly available information, implying that no excess returns can be gained by trading on such information.
Strong form, in its turn, includes both weak and semi-strong form efficiency
To be more exact, it implies that share prices reflect all kind of information which may be public and private, but none of these can earn excess returns
Behavioral finance challenges the notion of efficient markets, suggesting that information is not always effectively utilized (Ritter, 2003) Pioneers Tversky and Kahneman (1979) elucidated financial behaviors across different phases, while Olsen (1998) described behavioral finance as a paradigm shift aimed at replacing traditional finance theories Fromlet and Hubert (2001) defined it as an integration of individual behavior and market phenomena, drawing from psychology and financial theory Olsen further emphasized that behavioral finance applies psychological and economic principles to enhance financial decision-making This discipline has deepened our understanding of individual investor behaviors, shedding light on the psychological factors that influence investment intentions and choices (Ritter, 2003).
Prospect theory is a fundamental concept in behavioral finance, highlighting how psychological factors influence decision-making Key elements of this theory include regret aversion, loss aversion, and mental accounting, which collectively shape individuals' financial choices and perceptions of risk (Waweru et al., 2003, p.28).
Prospect theory highlights that regret is an emotional response following mistakes, leading investors to hold onto losing stocks while readily selling those that are gaining (Forgel & Berry, 2006) Loss aversion explains the greater psychological impact of losses compared to equivalent gains, with research indicating that individuals experience more distress from potential losses than pleasure from gains (Barberis & Huang, 2001; Barberis and Thaler, 2003) Additionally, mental accounting describes how individuals assess and categorize their financial transactions, enabling investors to effectively organize their portfolios into distinct accounts (Barberis & Thaler, 2003).
In summary, the EMH means that there is unsystematic method to beat the market and stock prices are reasonable, namely, they only reflect
“fundamental” or “utilitarian” characteristic like risk, but not “psychological” or “value-expressive” characteristics such as sentiment (Statman, 1999)
Behavioral finance explores the psychological factors influencing financial decision-making and market predictions, as noted by Talangi (2004) It introduces concepts like prospect theory, which revolutionizes traditional financial theory by emphasizing the psychological aspects of investment choices, challenging the Efficient Market Hypothesis (EMH).
Review on some behavioral factors and herding bias in stock market
Numerous researchers have examined the behavioral factors influencing herding bias, including risk tolerance, overconfidence, gambler’s fallacy, illusion of control, and self-monitoring This section will provide a literature review of these key factors.
Financial risk tolerance refers to the level of uncertainty an individual is willing to accept in financial decisions and is influenced by various demographic and socioeconomic factors such as gender, age, marital status, ethnicity, and income Research indicates that as individuals age, their risk tolerance tends to decrease, while higher income levels are associated with increased risk tolerance Studies have shown that single individuals generally exhibit greater risk tolerance compared to their married counterparts, with single males displaying the highest levels of risk tolerance, followed by married males, unmarried females, and married females Additionally, findings suggest that men typically have a higher risk tolerance than women.
Other factors that appear to impact a person’s risk tolerance include environmental factors such as financial knowledge, family situation (Roszkowski, 1999) and social development such as birth order (Sulloway,
1997), which is an example of a biopsychosocial factor
Research by Keller and Siergist (2006) indicates that financial risk tolerance is a significant positive predictor of stock investment willingness, with highly risk-tolerant investors maintaining high-value portfolios and engaging in frequent trading Similarly, Dorn and Huberman (2005) found that risk-tolerant investors exhibit more aggressive trading behaviors, leading to greater portfolio diversification and turnover Additional studies, including those by Tigges et al (2000), Warneryd (2001), and Clark-Murphy and Soutar (2004), further support the notion that individuals with higher risk tolerance tend to trade more often than their less risk-tolerant counterparts.
Wood and Zaichkowsky, 2004; Durandet al., 2008)
Research indicates that attitudes towards financial risk tolerance significantly influence the setting of financial goals and the formulation of financial plans and strategies, as highlighted by Grable and Joo (2004) This suggests that such attitudes may also affect herding bias in financial decision-making.
Herding bias can be influenced by the differing risk tolerance and risk preferences of investors, as noted by Lin (2012) He suggested that a lower risk tolerance significantly contributes to the development of herding behavior among investors.
Numerous studies have explored the phenomenon of overconfidence, particularly in the context of investing Kourtidis et al (2011) found that overconfident investors often overestimate their abilities, leading them to undervalue the perspectives of others This overconfidence results in a tendency to under-react to new information or to place excessive importance on certain data, while also fostering unrealistic expectations regarding potential returns (Barber and Odean).
Overconfident investors tend to overestimate their private information while disregarding available data, leading to asymmetric responses characterized by short-horizon momentum and long-horizon reversal in returns (Daniel et al., 1998) This overconfidence results in increased trading frequency and volume, as noted by Kourtidis et al (2011) Furthermore, Glaser and Weber (2007) found that a higher degree of investor overconfidence correlates with elevated trading volumes.
Research by Dow and Gorton (1997) revealed that trading volume tends to rise when individuals and insiders exhibit overconfidence This finding aligns with the perspective presented by Daniel et al., who also argued that overconfidence contributes to heightened trading activity.
(2001), Hirshleifer and Luo (2001), Wang (2001) and Scheinkman and Xiong
Research indicates that overconfidence in traders significantly increases trading activity and the likelihood of making poor decisions, such as investing in the wrong stocks (Kourtidis et al., 2011) Odean (1998) found that overconfident traders often make biased judgments, resulting in lower returns Similarly, studies by Fenton O’Creevy et al (2003) and Philip (2007) highlight the detrimental effects of overconfidence on trading performance.
(2001) supported that overconfident investors earned higher returns than less confident ones
The relationship between overconfidence and herding behavior indicates that overconfident individuals may not exhibit significant herding tendencies Research on overconfident trading behaviors reveals that individual investors often overestimate their information and abilities, as highlighted by studies conducted by Barber and Odean (2001a, 2001b).
Investors often underestimate risk and engage more frequently in higher-risk securities, which can result in investment performance that falls below the market average (Benartzi & Thaler, 1995; Odean, 1998, 1999; Lin, 2011).
Self-monitoring is a significant personality trait within social psychology, often regarded as a form of social intelligence It involves the ability to observe social cues and adapt one’s behavior to align with the expectations of a given social context (Snyder and Gangestad, 1986) This concept has garnered increased attention in recent studies, highlighting its importance in understanding interpersonal interactions (Parker and Fischhoff, 2001).
Decision-making competence is positively correlated with self-monitoring, which reflects an individual's awareness of their actions and has been linked to enhanced performance Research by Kilduff and Day (1994) indicates that high self-monitors are more likely to achieve promotions in managerial roles, while Mehra, Kilduff, and Brass (2001) found that self-monitoring positively impacts workplace performance High self-monitors act as impression managers, strategically adjusting their behaviors to gain advantages, particularly in competitive environments like trading games They may place orders to maximize profits while concealing their private information and manipulate others' beliefs through deceptive offers Monson (1983) noted that high self-monitors often project their own strategic behavior onto others, interpreting peers' actions as situational rather than reflective of their true dispositions Consequently, in market scenarios, they are less inclined to accept market prices at face value, leading them to analyze the underlying signals and strategies, which helps them avoid the winner's curse by recognizing the correlation between others' actions and their information (Eyster and Rabin, 2003).
The gambler’s fallacy refers to the mistaken belief that the probability of an event decreases after it has already occurred, despite the events being independent (Rabin, 2002) Essentially, gamblers assume that small samples should reflect the larger population, leading them to expect correlations when unexpected sequences arise (Tversky and Kahneman, 1971) In the context of stock markets, this fallacy manifests as investors misjudging reversal points, which they perceive as signals for the end of favorable or unfavorable market returns (Waweru et al., 2008) Furthermore, when influenced by this bias, investors often make suboptimal decisions based on previous selections (Kempf and Ruenzi, 2006).
The illusion of control, as described by David and Meira (1995), is the belief that individuals can influence the outcomes of random events This phenomenon arises from a person's difficulty or unwillingness to differentiate between situations governed by skill and those determined by chance While skillful actions can shape certain outcomes, when people attempt to apply similar strategies to purely chance events, it demonstrates an illusion of control.
Hypothesis development
Prospect Theory is a fundamental to explain the fluctuation of stock market
To be more specific, 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)
In accordance with the research of Ly and Thao (2012), over-confidence, risk tolerance, and herding bias have significant effects on Vietnamese investors
This research explores the behavioral factors influencing herding bias among investors in the Vietnam stock market It highlights that many Vietnamese investors base their buying and selling decisions primarily on recent information, suggesting that availability bias significantly impacts herding behavior Additionally, existing studies indicate that self-monitoring and gambler’s fallacy notably contribute to biases in stock markets Consequently, the study hypothesizes that four key behavioral factors—risk tolerance, overconfidence, self-monitoring, gambler’s fallacy, and availability bias—affect herding bias in this context.
Investors characterized by anxiety and emotional instability tend to follow the investment advice of friends or seek professional consultation, leading to herding behavior In contrast, high risk-tolerant investors, who are more accepting of uncertainty, are less likely to engage in herding Research by Garble and Lytton (2004) indicates that understanding the link between stock market returns and risk tolerance can clarify why investors exhibit herding by purchasing risky assets during market uptrends and selling during downtrends Lin (2012) further posits that the differences in risk tolerance and preferences contribute significantly to herding behavior, suggesting that risk tolerance negatively impacts herding bias Thus, the first hypothesis is established.
H1: Risk tolerance has a negatively effect on herding bias
Individual investors often exhibit herding behavior due to a lack of confidence and professional expertise, leading them to rely on market signals and the opinions of professional investors when making investment decisions.
Overconfidence significantly influences herding bias in investors, as highlighted by Goodfellow, Bohl, and Gebka (2009) When investors exhibit higher confidence levels, they tend to prioritize their private information in making investment decisions, leading to a decreased interest in conforming to herding behavior.
H2: Overconfidence has a negative effect on herding bias
Self-monitoring significantly impacts herding bias in market behavior High self-monitors, who adeptly gauge how their actions influence others, tend to predict that other market participants will also act strategically (Biais et al., 2005) This anticipation leads them to question market prices rather than accept them at face value, as they consider the underlying signals and strategies that shape these prices As a result, high self-monitors show reduced interest in herding behaviors, supporting the proposed hypothesis 3.
H3:Self-monitoring has a negative effect on herding bias
Gambler’s fallacy significantly influences herding bias, as individuals affected by this bias mistakenly believe that the probability of an event decreases after it has occurred, despite the independence of each trial This misconception leads them to trade against recent trends, thereby inhibiting herding behavior Consequently, the fourth hypothesis emerges from this understanding.
H4: Gambler’s fallacy has a negative effect on herding bias
The illusion of control significantly contributes to herding bias, as individuals with this bias believe they can influence random events and often underestimate the role of chance They perceive games of chance as games of skill, leading them to infer information from the decisions of others, assuming those choices are based on relevant insights rather than randomness This belief fosters herding behavior, reinforcing the hypothesis that the illusion of control encourages collective decision-making among investors.
H5: Illusion of control has a positive effect on herding bias
As is presented above, the research model is organized as follows
Year of publication Antecedent of hearding bias Sign of effect
Over-confidence, risk tolerance, and herding bias have significant effects on Vietnamese investors
The behavioral factors of investors affecting the herding bias in the Vietnam stock market
Self-monitoring and gambler’s fallacy have considerable effects to biases in stock markets
The research hypothesises that the four behavioral factors affecting herding bias are risk tolerance, overconfidence, self- monitoring, gambler’s fallacy, and availability bias
Investors having characteristics of anxiety, emotionally unstable and nervous always followed the investment suggestions of their friends or seek professional consultation or insider that will also lead to herding
Investors having characteristics of anxiety, emotionally unstable and nervous always followed the investment suggestions of their friends or seek professional consultation or insider that will also lead to herding
Understanding the relationship between stock market returns and risk tolerance
Explaining why investors exhibited herding behavior by purchasing risky investments during market uptrend and selling securities during market downtrend
Risk tolerance had a significant negative impact on herding bias
The original herding behavior might be the differences of risk tolerance and risk preferences of the behavior
A chart of research model proposed by the author
RESEARCH METHODOLOGY
Measurement scales
3.1.1 Scales measurement of Risk Tolerance
Variable of Risk Tolerance is symbolized of RIS Trone, Allbright and Taylor
Measuring an individual's financial risk tolerance is challenging due to its multidimensional nature and the various predisposing factors that influence it (1996) In this study, Risk Tolerance (RIS) will be assessed using a five-point Likert scale, as outlined by Grable and Joo (2004), with the evaluation based on five specific items.
Many individuals perceive investing as a complex endeavor, often preferring the security of a bank account over the uncertainties of the stock market The word "risk" frequently evokes thoughts of potential loss, leading to a belief that generating profits in stocks and bonds relies heavily on luck Consequently, for these investors, prioritizing safety in their financial decisions outweighs the desire for higher returns.
3.1.2 Scales measurement of Over-confidence
The variable of over-confidence, indicated as CONFI, will be assessed in this study using five-point Likert scale items derived from the research of Lin (2011) and Lakshmi et al (2013) The items utilized for this measurement are detailed in the following table.
I am confident in my ability to make sound investment decisions and believe I can anticipate future trends effectively My perspective on market trends often aligns with reality, and I attribute my investment profits to a well-defined strategy My past successes in investing are primarily due to my specialized skills, and I consistently achieve return rates that meet or exceed the market average Overall, I feel satisfied with my previous investment choices.
3.1.3 Scale measurement of Self-monitoring
Self-monitoring, represented by the acronym SEMO, is a stable personality trait that persists throughout an individual's life, as suggested by Jenkins (1993) Snyder and Gangestad (1986) created and validated a psychometric scale to measure self-monitoring In this paper, I will directly present their 18-item questionnaire, which is displayed in the following table.
Semo1 I find it hard to imitate the behavior of other people
Semo2 At parties and social gatherings, I do not attempt to do or say things that others would like Semo3 I can only argue for ideas which I already believe
I possess the ability to deliver impromptu speeches on unfamiliar topics, showcasing my talent for captivating an audience My inclination to put on a show suggests that I aim to impress or entertain those around me With these skills, I believe I could excel as an actor, utilizing my natural charisma and spontaneity.
Semo7 In a group of people I am rarely the centre of attention
In various situations and with different individuals, I tend to present myself in diverse ways I struggle to win the affection of others and often find that I am not truly the person I seem to be.
Semo11 I would not change my opinions (or the way I do things) in order to please someone or win their favor Semo12 I have considered being an entertainer
Semo13 I have never been good at games like charades or improvisations
Semo14 I have trouble changing my behavior to suit different people and different people and different situations Semo15 At a party I let others keep the jokes and stories going
I often feel uncomfortable in social situations, which affects how I present myself Additionally, I possess the ability to maintain eye contact and lie convincingly without revealing my true intentions This skill allows me to appear friendly, even towards those I may not actually like.
3.1.4 Scale measurement of Gambler’s Fallacy
The variable representing gambler's fallacy is denoted as GAM, measured using a four-item questionnaire based on a five-point Likert scale, as outlined by Waweru et al (2008) The specific items assessing gambler's fallacy are detailed below.
GAM Gambler's Fallacy gam1 You guess reserve points to decide your investment gam2
Price of a stock has fallen in multiple sessions You determine to buy this stock because you believe that it would be impossible to decline more gam3
The stock price has risen consistently over several sessions, prompting you to consider liquidating your shares, as you believe further gains may be unlikely Despite this, your decision to invest in stocks is grounded in thorough fundamental and technical analysis.
3.1.5 Scale measurement of Illusion of control
“Illusion of control” variable is symbolized as ILLUS According to Scottand Mariathis, variable could be measured by eleven five-point-Likert items The following table presents these items
Many gamblers believe in the illusion of control, thinking they can increase their chances of winning by using a system when playing slot machines For instance, after witnessing a coin land on heads ten times in a row, one might mistakenly think the next toss is more likely to be tails While some believe that certain secrets can lead to successful casino gambling, others argue that luck plays a significant role Gamblers often feel that wins are more frequent on "hot" machines, and a skilled player resembles a quarterback, strategically choosing the right moments to play Sticking with the same dice during a winning streak is considered sound advice, and those who develop a well-planned system often find success Additionally, the longer a player experiences losses, they may irrationally think their chances of winning increase Familiarity with casino games can also enhance winning potential, and paying attention to frequently winning lottery numbers may provide an edge.
The herding bias, represented by the acronym HERD, will be examined in this study This research will utilize a total of eight items, drawing four items from Lin's 2011 study and an additional four items from the research conducted by Lakshmi et al.
(2013); therefore, my research will contain eight five-point-Likert items in total to measure herding bias variable These eight-item questionnaires are revealed in the following table:
Herding bias influences investors to make decisions based on the actions and recommendations of others This can manifest in various ways, such as purchasing shares based on friends' suggestions, bidding on securities with rising prices, or investing in the same stocks as peers Additionally, many traders follow market information, technical analysis, and media reports to guide their investments Relying on expert recommendations and information from friends and relatives also plays a significant role in shaping trading choices.
Sampling
Hatcher (1994) suggests that for effective factor analysis (EFA), the minimum sample size should be five times the number of variables or at least 100 participants Given that this study includes 37 observed variables, the recommended sample size is calculated as N = 5 x 37, resulting in a total of 185 subjects.
The research will involve a sample size exceeding 200 participants, meeting the necessary requirements for validity According to Tabachnick and Fidell (2011), it is essential that the sample size for regression analysis is at least equal to or greater than a specified threshold.
50 + 8*k (N>P + 8*k), in which k is the number of independent variables In this study, N > 50 + 8*5 Hence, the survey form will be sent to more than
200 investors in each stock exchange all met the given condition.
Data collection methods
This study will utilize a quantitative approach, focusing on the distribution of targeted questionnaires to assess the level of agreement among over 200 investors in the HOSE Initially, these questionnaires will be disseminated to gather valuable insights.
20 investors (pilot survey) in order to test scales measurement reliability
The reliability of the measurement scales will be assessed using SPSS software to calculate Cronbach’s Alpha for standardized items Following the removal of unreliable questions, the refined questionnaires will be distributed to the remaining investors This process will ensure the collection of a robust dataset for analysis.
A total of 200 questionnaires were completed by investors to assess the reliability and validity of the scales used in the study The analysis involved factor analysis through Exploratory Factor Analysis (EFA) with a sample size of N=5 The final step utilized SPSS software for regression analysis to evaluate four key assumptions.
Data analysis methods
3.4.1 Test of scale measurement reliability
The Cronbach’s Alpha Test is utilized to assess the reliability of scale measurements using a five-point Likert scale This test evaluates the consistency of responses from a specific sample of respondents across various questions or items Additionally, it aids in predicting the reliability of respondents concerning the measurement (Helm, Henze, Sass, and Mifsud, 2006).
Cronbach’s Alpha is a key indicator of reliability in behavioral research, as noted by Liu, Wu, and Jumbo (2010) Its application is particularly fitting for this study, which utilizes a five-point Likert scale in the questionnaire and focuses on the field of behavioral finance.
As a consequence, this paper will employ Cronbach’s Alpha to test of scale measurements reliability including the factors built after the factor analysis
Cronbach’s Alpha is a crucial measure of reliability in research, with Nunnally (1978) suggesting a minimum value of 0.7 for acceptable measurements, while some statisticians consider values above 0.6 to be adequate It is essential to examine corrected item-total correlations, which should be at least 0.3 (Shelby, 2011) In this study, the acceptable Cronbach’s Alpha range is set between 0.7 and 0.8, with corrected item-total correlations also meeting the 0.3 threshold, focusing on the financial behavior of stockholders on the Ho Chi Minh Stock Exchange Additionally, the significant level for the F-test in this analysis should not exceed 0.05, and the Cronbach’s Alpha test will be conducted using SPSS software.
This paper will apply EFA to explore the factors that the variables of behavioral finance of the questionnaire (Question 1 to question 37) belong to
Exploratory Factor Analysis (EFA) is utilized to streamline the questionnaire by eliminating items that do not align with the analytical criteria (O’Brien, 2007) In this context, EFA serves to evaluate the hypotheses outlined in the research model discussed in Chapter 3.
The following criteria of the exploratory factor analysis applied in this study are: Factor loadings, KMO, Total variance explained, and Eigen-value
Factor loadings represent the correlations between individual items and their respective factors Hair et al (1998) suggest that for exploratory factor analysis (EFA) to be considered practically significant, the factor loadings should exceed 0.5 when the sample size is 100.
The Kaiser-Meyer Olkin Measure of Sampling Adequacy (KMO) reveals the level of appropriation of using EFA for the collected data Ali, Zairi and Mahat
(2006) suggested that the KMO should be flowed from 0.5 to 1.0(significant level less than 0.005) to ensure that factor analysis was suitable for the data
To determine the number of retained factors, total variance explained will be utilized, ensuring that factors are kept until the last one accounts for a minimal percentage of the explained variance According to Hair et al (1998), it is recommended that the total variance explained exceeds 50%.
Eigen-value is a key attribute in factor analysis, representing the total variance explained by a specific factor across all variables A valid eigen-value must exceed 1; values below this threshold indicate that the factor accounts for less variance than an individual item (Leech, Barret, and Morgan, 2005) Similar to Cronbach's Alpha, the extraction of eigen-values is performed using SPSS software during exploratory factor analysis (EFA).
To explore the correlation between herding bias and other behavioral factors,
I will also apply the regression analysis The model is presented as follow:
HERD stands for Herding bias RIS: Risk tolerance
CONFI: Over confidence SEMO: Self-Monitoring GAM: Gambler’s fallacy ILLUS: Illusion of control is constant : residual
DATA ANALYSIS AND FINDINGS
Descriptive statistics
A total number of 205 questionnaires were delivered to investors in HOSE
The final sample consisted of 205 participants, and data analysis was conducted using SPSS software The assessment of score distribution revealed that the significance values for all variables were above 05, a typical occurrence in large samples Additionally, the 5% trimmed mean values closely aligned with the original mean values, leading to the decision to retain the outliers in the dataset.
In this study, variables are assessed using a multi-item scale, specifically a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree) The average values derived from the observed variables effectively represent the concepts under investigation The findings are summarized through descriptive statistics, which are detailed in Table 4.1.
Table 4.1: Independent variables, dependent variables and items
1 Risk Tolerance RIS1, RIS2, RIS3, RIS4, RIS5
2 Over-confidence CONFI1, CONFI2, CONFI3, CONFI4, CONFI5, CONFI6,
SEMO1, SEMO2, SEMO3, SEMO4, SEMO5, SEMO6, SEMO7, SEMO8, SEMO9, SEMO10, SEMO11, SEMO12, SEMO13, SEMO14, SEMO15, SEMO16, SEMO17, SEMO18
4 Gambler's Fallacy GAM1, GAM2, GAM3, GAM4
5 Illusion of control ILLUS1, ILLUS2, ILLUS3, ILLUS4, ILLUS5, ILLUS6,
ILLUS7, ILLUS8, ILLUS9, ILLUS10, ILLUS11
Herding bias HERD1, HERD2, HERD3, HERD4, HERD5, HERD6,
Refinement of measurement scales
4.2.1 Result of Cronbach’s alpha analysis of formal survey (N 5)
Following the formal survey, several questions were removed due to a Cronbach’s Alpha score below 0.6, indicating insufficient reliability The remaining behavioral factor questions, ranging from X6 to X50, were deemed reliable and classified as independent variables Additionally, questions Y51 to Y59 were designed to assess investors' evaluations of their own herding behavior, serving as dependent variables The Cronbach’s Alpha scores for the independent variables are detailed below.
Table 4.2 Results of Cronbach’s alpha for behavioral factors
The behavioral factors Cronbach’s alpha Benchmark
Over-confidence 0.61 Self-monitoring 0.689 Gambler's Fallacy 0.699 Illusion of control 0.766
Table 4.2 displays the Cronbach’s alpha values for various psychological factors influencing decision-making, including Risk Tolerance (0.658), Over-confidence (0.61), Self-monitoring (0.698), Gambler’s Fallacy (0.699), Illusion of Control (0.766), and Herding Bias (0.795) All these values exceed the acceptable threshold of 0.6, indicating satisfactory internal consistency among the items analyzed For a detailed breakdown of the Cronbach’s alpha analysis, refer to Table 4.2.
Table 4.3 indicates that the corrected item-total correlations for all variables exceed 0.3, demonstrating sufficient reliability for use in the Exploratory Factor Analysis (EFA) Detailed results of Cronbach’s alpha for all items, calculated using SPSS, are presented in Appendix 2.
Exploratory factor analysis (EFA) was employed to examine the behavioral variables related to herding behavior, beginning with question X6, in order to identify the influencing factors The criteria for factor analysis outlined in Chapter 3 were met, facilitating the reduction of items.
After a thorough elimination process, the analysis revealed that the remaining data was categorized into five variables: four independent and one dependent Several items were discarded for not meeting the necessary criteria, as outlined in Chapter 3, where it is stated that the KMO value must exceed the acceptable threshold.
0.5 Factor loading is greater or equal 0.5, i.e if factor loadings of the items were less than 0.5, they would be got rid of The total variance explained was suggested to be more than 50% In terms of absolute values of difference among loading, the acceptable value was at least 0.3 The Corrected Item-Total correlation should be at least 0.4 to ensure that the items would make a good component of a summated rating scale Eigen-value should be greater than 1 because Eigen-value less than 1 means that less information was explained by the factor than by a single item (Leech, Barrett & Morgan, 2005, p.82) a Individual
Table 4.3 presented the brief results of EFA As is shown, KMO values of all factors are great or equal 0.5, therefore, factor loading was suitable for data of survey
Table 4.3 KMO index of behavioral factors
The behavioral factors KMO Benchmark
Self-monitoring 0.714 Gambler's Fallacy 0.5 Illusion of control 0.774
Appendix 3 shows that factor loadings are greater than 0.5, except for RIS5 (0.487) The values of the other indicators including total variance explained, Corrected Item-Total correlation, Eigen-value for all factors are acceptable b EFA for all
Following the removal of items based on Cronbach’s alpha analysis and exploratory factor analysis (EFA), the results indicated a KMO value of 0.631, which is greater than the acceptable threshold of 0.5 Furthermore, the Rotated Factor Matrix revealed that each item exhibited loadings exceeding 0.5, leading to their acceptance.
The paper employed the SPSS software to divide behavioral factors into 5 components as is presented in Table 4.5
ILLUS3 ILLUS5 ILLUS7 ILLUS10 ILLUS11
SEMO7 SEMO11 SEMO13 SEMO14 SEMO16
All loadings of items cluster were greater than 0.5 and met requirements to group them into 5 components The paper finished exploration factor analysis and used these components for regression analysis
Table 4.5: Summary of KMO and Bartlett’s Test, total variance explained and rotated component matrix (EFA time 2)
Initial Eigen values 1.658 (component 5) >1=> met requirement Rotation sums of squared loading 59.924% >50% => met requirement
The study utilized SPSS software to identify five components, as illustrated in Table 4.7, with all item loadings exceeding 0.5, thereby meeting the criteria for grouping Following the exploratory factor analysis, these components were employed for regression analysis Additionally, details of the Cronbach’s alpha analysis for time 2 are provided in Appendix 2.
Table 4.6: Result of joint factor analysis for all scales
Testing the assumptions of regression
According to Leech, Barrett, and Morgan (2005), regression analysis is a statistical method used to examine relationships between variables In my study, I gathered data on various variables and utilized regression to assess the impact of independent variables on a dependent variable To evaluate the model's effectiveness, I selected adjusted R², which is typically lower than unadjusted R², to determine the percentage of variance explained, influenced by the effect size and sample size Additionally, an F-test was conducted to assess the significance of the model, with hypotheses H₀ and H₁ being formulated accordingly.
H 1 : At least one (at least one independent variable affect Y)
If p-value < 0.05, H 0 would be rejected while H 1 is accepted and the conclusion is that the research model is suitable
The standardized beta coefficient is interpreted similarly to the correlation coefficient, helping to assess the significance of independent variables on the dependent variable A t-value of less than 0.05 indicates that the beta coefficient is significant, revealing whether the impact is positive or negative Additionally, collinearity statistics such as Variance Inflation Factor (VIF) and Tolerance are crucial for identifying multicollinearity; a low Tolerance value (less than 1) suggests the presence of multicollinearity among the variables.
1 – R 2 ), then there is likely a problem with multi collinearity.
Results of hypothesis testing
Table 4.7 Summary results of the hypotheses
H1 Risk tolerance has a negatively effcect on herding bias 0.109 0.086 > 05 Un-
Overconfidence and Gambler’s fallacy have a negative effect on herding bias
H3 Self –monitoring has a negative effect on herding bias 0.092 0.153 >.05 Un-
H5 Illusion of control has a positive effect on herding bias 0.358 0.000