INTRODUCTION
Research background
Established in 1998, the Vietnamese stock market comprises the Ho Chi Minh City Stock Exchange (HOSE) and the Ha Noi Stock Market (HAS) Initially, in 2000, it featured only 2 listed companies and 4 securities firms, but over a decade, membership grew to 682 securities companies, reflecting significant market development The VN-Index, which started at 100 points in July 2000, surged to a peak of 571 points by June 2001, fueled by investor enthusiasm and high demand despite the limited number of listed stocks However, a lack of investor knowledge and inadequate regulatory support led to a dramatic decline, with the VN-Index plummeting to a low of 139 points by March.
Investors entering the market in 2003 faced significant financial challenges due to substantial asset losses, leading to a prolonged downturn until 2005 The stock market began to recover in 2006, experiencing a remarkable surge in the latter half of the year, reaching 1170 points by March 2007 It maintained fluctuations around the 1000-point threshold until October 2007, marking a notable period in the Ho Chi Minh stock market's history.
The VN-Index faced a significant decline after reaching its peak, culminating in a challenging year for the Ho Chi Minh stock market in 2008, with the index dropping to 245 points by February 2009 Economic experts attribute this sharp decline to multiple factors, including tightened monetary policies, high deposit interest rates, soaring inflation, and the recession in the United States Additionally, the lack of timely intervention by authorities contributed to the dramatic fall of the VN-Index (Vo and Pham, 2008, p.15) From 2009 to the first quarter of 2011, the index experienced fluctuations, peaking at 542 points in May 2010 and dipping to 351 points by November 2010, but remained relatively stable around 400-500 points with no significant changes noted in 2012.
Table 1: The highest and lowest VN-index from 2000 to 2012
Year Highest prices Lowest prices
Problem of statement
Over the past twelve years, the HOSE and the Vietnamese Stock Exchange have experienced significant fluctuations, making it challenging for investors to make informed decisions The VN-Index boom in 2007 drew negative attention from foreign media, with the Financial Times labeling the HOSE as a "too hot place" and exaggerating its value Additionally, the American Chamber of Commerce (AMCHAM) criticized the growth of the Vietnam stock market as unrealistic, describing it as another instance of a "bubble market."
The Efficient Markets Hypothesis (EMH) fails to account for the causes of bubble markets, as it posits that stock prices reflect all available information and that capital markets are informationally efficient In contrast, behavioral finance suggests that financial markets can be informationally inefficient in certain situations, indicating that behavioral factors may play a significant role in the dynamics of bubble markets.
Behavioral finance offers valuable insights into bubble markets by utilizing psychological principles to elucidate the reasons behind investors' buying and selling behaviors (Waweru et al., 2008, p.25) Key behavioral factors such as overconfidence, representativeness, availability, herding, loss aversion, regret aversion, and the gambler’s fallacy significantly influence investment decisions (Ritter, 2003, p 437) Consequently, many researchers view behavioral finance as an effective framework for understanding the emotional and cognitive biases that impact decision-making in investments.
Proponents of behavioral finance argue that insights from social sciences, particularly psychology, can enhance our understanding of stock market behaviors, including the dynamics of market bubbles and crashes (Waweru et al., 2008, p.25).
A study by Johnsson, Andrén, Lindblom, and Platan (2002) in Sweden revealed that behavioral factors significantly influence the investment decisions of both individual and institutional investors Notably, 67% of individual investors are affected by loss aversion bias, while 33% are influenced by representative bias and 32% by regret aversion bias, among other factors.
Behavioral finance is not only prevalent in European countries but is also being explored in Asia A study by Chandra and Kumar (2011) investigates the factors influencing individual investor behavior in the Indian stock market, revealing a significant presence of representativeness bias that affects their investment decisions.
Investors often exhibit overconfidence bias, believing too strongly in the accuracy of their judgments Notably, 53.2% of investors are influenced by anchoring bias, while 35.5% fall prey to the gambler's fallacy in their investment decisions Additionally, 57.7% are affected by availability bias, and 55% experience loss aversion bias, all of which significantly impact their decision-making processes.
According to Vuong & Dao (2012), individual investors in the Vietnamese stock market exhibit various behavioral biases that significantly influence their investment decisions Notably, 51.2% of investors are affected by regret aversion bias, while 47.7% experience loss aversion bias Other biases include anchoring bias at 43%, overconfidence bias and self-control bias both at 42.4%, and illusion of control bias at 41.3% Additionally, 41.3% of investors are impacted by confirmation bias, 34.9% by framing bias, 30.3% by conservatism bias, 18% by representative bias, and 17.4% by availability bias.
Behavioral finance plays a crucial role in understanding the Vietnamese stock market (HOSE) for two main reasons Firstly, it remains a relatively new area of study, offering valuable insights into how financial market investors make decisions and how these choices impact the market (Kim and Nofsinger, 2008, p.1) Secondly, research indicates that Asian investors, including those in Vietnam, are more susceptible to cognitive biases than their counterparts from other cultures (Kim and Nofsinger, 2008, p.1) Consequently, it's essential to consider behavioral factors when analyzing the decision-making processes of Vietnamese investors This study aims to contribute to the existing body of research on behavioral finance and its influence on investment decisions in the Vietnamese stock market.
Research questions
This paper examines the behavioral factors that affect the investment decisions of individual investors at the Ho Chi Minh Stock Exchange (HOSE) and assesses the extent of their impact It formulates specific research questions to explore these influences on investment decision-making.
1/ What are the behavioral factors influencing individual investors‟ decisions at the HOSE?
2/ How strong is impact of behavioral factors on investment decision making of individual investors at the HOSE?
Research scope
Vietnam has two stock exchanges, located in Hanoi and Ho Chi Minh City This study emphasizes the Ho Chi Minh Stock Exchange, as it is situated in the country's largest and most rapidly developing city.
The Ho Chi Minh stock market serves as a key indicator of Vietnam's economic wealth, facilitating capital raising for businesses and investors alike By focusing on HOSE for this research, the author aims to streamline the process, ensuring more precise survey results and enhancing the effectiveness of interview data collection.
Research method
This research employs a combination of qualitative and quantitative methods, beginning with a pilot study that utilizes qualitative interviews to identify behavioral factors influencing investor decision-making The pilot interviews, conducted with 10 seasoned investors with over 10 years of stock market experience, aim to refine the questionnaires for the official research phase Subsequently, a quantitative survey is distributed to 220 investors in the HOSE, starting with a pilot survey of 52 participants to assess the reliability of the measurement scales After refining the questionnaire by removing irrelevant or unreliable questions, the final survey is sent to an additional 170 investors.
This study formulates hypotheses grounded in established behavioral finance theories, employing a quantitative method for initial testing followed by qualitative analysis for deeper insights To ensure the reliability of the measurement scales, the research utilizes SPSS software for testing.
This paper utilizes Cronbach's Alpha to assess the reliability of standardized items, followed by an examination of measurement validity through Exploratory Factor Analysis (EFA) Lastly, regression analysis is employed to evaluate the underlying assumptions and test the proposed hypotheses.
Significance of the research
• Help investors or organizations consider and analyze these behavioral factors before making decisions for investment
• Employ these behavioral factors effectively to increase success in business.
Structure of the study
Chapter 1, Introduction, is divided into two key sections: the first provides a comprehensive overview of the Ho Chi Minh Stock Exchange (HOSE) from its inception to the present day, while the second section outlines the problem statement and explains the rationale behind choosing this topic for exploration.
Chapter 2 of the Literature Review examines the behavioral factors influencing investment decision-making, as explored by previous researchers Key factors discussed include representativeness, availability bias, gambler's fallacy, herding, regret aversion, and loss aversion The paper emphasizes the importance of studying six specific behavioral factors: representativeness, availability bias, herding, gambler's fallacy, mental accounting bias, and over-underreaction Furthermore, it presents hypotheses and research models to guide future investigations in this area.
Chapter 3, Methodology, outlines the research methods utilized in this study, specifically qualitative and quantitative approaches The qualitative method involves interviewing 10 investors to explore their behavioral factors and gather insights on the questionnaire content Following the development of reliable questionnaires, the quantitative method is employed to survey 220 investors using a five-point Likert scale.
(extremely disagree to extremely agree) Additionally, the paper also mentions research population, samples, steps of investment decision through SPSS software
Chapter 4 presents the findings from data collection and analysis, divided into three sections The first part discusses the results of interviews, highlighting investors' valuable feedback on the questionnaires and their insights into behavioral factors The second part outlines the results of the pilot survey, focusing on the assessment of measurement reliability and validity.
This paper presents the findings of an official survey, focusing on the results of exploratory factor analysis (EFA) to assess measurement reliability and validity It rigorously tests six key assumptions: independence of residuals, linear relationships, homoscedasticity of residuals, absence of multicollinearity, lack of significant outliers or influential points, and normality of the residuals The results are then discussed in the context of the proposed hypothesis.
Chapter 5 presents key recommendations and conclusions, highlighting the impact of behavioral factors on investor decisions, such as representativeness, gambler's fallacy, and over- and under-reaction It also notes that certain biases, including availability bias, herd behavior, and mental accounting, do not significantly influence the investment choices of individual investors The chapter offers practical advice for individual investors while acknowledging the study's limitations Ultimately, it concludes with a summary of the findings and their implications for investment behavior.
LITERATURE REVIEW
Classical finance theory versus behavioral finance
The Efficient Market Hypothesis (EMH), proposed by Fama (1998), posits that capital markets are informationally efficient, meaning that prices reflect all available information In contrast, behavioral finance, as noted by Ritter (2003), argues that financial markets can be informationally inefficient under certain conditions While EMH suggests that markets make unbiased forecasts despite the presence of irrational investors, behavioral finance acknowledges that these inefficiencies can lead to systematic biases in market behavior.
Stock market efficiency, as defined by Statman (1999), encompasses two key aspects: the inability to consistently outperform the market through unsystematic methods and the notion that stock prices accurately reflect fundamental characteristics such as risk, while disregarding psychological factors like sentiment (p 18-27) In contrast, behavioral finance examines the psychological processes influencing decision-making and market predictions (Talangi, 2004, p 3-25) Within capital markets, efficiency is categorized into three forms: weak form efficiency, semi-strong form efficiency, and strong form efficiency.
The Efficient Market Hypothesis (EMH) outlines three forms of market efficiency Weak form efficiency indicates that current asset prices incorporate all historical financial data Semi-strong form efficiency signifies that stock prices quickly and unbiasedly adjust to new publicly available information, preventing excess returns from trading on such information Strong form efficiency encompasses both weak and semi-strong forms, asserting that share prices reflect all information, both public and private, with no opportunity to achieve excess returns.
Behavioral finance, pioneered by Tversky and Kahneman (1979), explores the interplay between individual behavior and market dynamics, marking a shift from traditional finance theories Olsen (1998) describes it as a new paradigm that aims to enhance financial decision-making by integrating psychological and economic principles Fromlet (2001) further emphasizes that behavioral finance merges insights from psychology with financial theory, highlighting its focus on understanding how psychological factors influence market phenomena.
In short, behavioral finance represents a revolution in financial theory It highlights the psychological edge of investment decision making process, in strong contradiction to the EMH.
Review some behavioral factors impacting on the process of investors’ decision
Numerous researchers have explored the behavioral factors that impact investment decision-making, including Representativeness, Availability bias, Anchoring, and Overconfidence This literature review focuses on how these behavioral elements specifically influence the investment choices of individual investors.
Representativeness refers to the extent to which a sample mirrors its parent population, influencing biases in decision-making According to DeBondt and Thaler (1995) and Kahneman and Tversky (1974), this concept highlights how individuals often overemphasize recent experiences while neglecting the long-term average rates of return, as noted by Ritter (2003).
Investors often rely on representativeness to assess a company's long-term growth potential, leading them to make positive investment decisions after observing several quarters of rising performance (Waweru et al., 2008, p.27) This cognitive bias can also manifest as "sample size neglect," where investors draw conclusions from insufficient data (Barberis & Thaler, 2003, p.1065) In the stock market, this tendency is evident when investors opt for "hot" stocks, overlooking companies with poor performance, thereby illustrating the influence of representativeness on their choices.
2003, p.1065) Particularly, in theVietnamese stock market, representative bias of individual investors was positively 18% in 2012 (Vuong & Quan, 2012, p.9).
Availability bias, as defined by Jahanzeb, Muneer, and Rehman (2012), is a cognitive bias that leads individuals to overestimate the likelihood of events that are memorable or vivid This bias causes investors to give disproportionate importance to readily available information when making decisions (p 534).
Availability bias in stock exchanges manifests as investors favoring local stocks due to their familiarity and ease of access to information This preference contradicts the fundamental principles of portfolio diversification, which emphasize optimal asset allocation (Waweru et al., 2003, p.28).
A study by Abhiject and Kumar (2011) revealed that 57.7% of investors in the Indian stock market experienced positive effects from a specific factor, while only 17.4% of investors in the Vietnamese stock market reported similar positive influences (Vuong & Quan, 2012).
Many people mistakenly believe that a random event is less likely to happen after a previous occurrence, a misconception rooted in probability This misunderstanding is linked to the "Law of Small Numbers," which can lead to the Gambler's Fallacy In the context of stock exchanges, this fallacy causes investors to misjudge potential reversal points, wrongly perceiving them as the end of favorable or unfavorable market trends Additionally, when investors fall prey to this bias, they tend to make suboptimal decisions based on previous selections.
Abhiject and Kumar, Ravinder (2011), they researched that in the Indian stock exchange, 35.5% of investors were positively influenced by this factor (p.15)
2.2.4 Herd behavior: buying and selling decisions
Herding behavior, as defined by Chaudhary (2013), involves the actions of a large group of investors who often rely on selective information rather than individual insights when making investment decisions This phenomenon can lead to both successful outcomes and significant losses Tan et al (2008) emphasize that during stock price fluctuations, the collective viewpoints of investors can either enhance profitability or mitigate risks In the stock market, the tendency to buy stocks based on 'price momentum'—disregarding the fundamental principles of supply and demand—exemplifies herd behavior, which can result in poor decision-making.
Investors' decisions are significantly influenced by various factors, including buying and selling patterns, investment duration, and the number of stocks held (Waweru et al., 2008) Additionally, the choices made by other investors also play a crucial role in shaping individual investment decisions Notably, herding behavior varies among different types of investors, with individual investors more likely to follow the crowd compared to institutional investors (Goodfellow, Bohl & Gebka, 2009).
Mental accounting, as defined by Barberis and Huang (2001), is the cognitive process through which individuals assess and manage their financial transactions (p.1248) This concept allows investors to categorize their portfolios into distinct segments, facilitating better financial decision-making (Barberis & Thaler, 2003, p.1108; Ritter, 2003, p.431).
Rockenbach (2004) empirically highlights that the relationship between different investment opportunities is often overlooked, which is crucial for maintaining arbitrage-free prices This oversight significantly influenced investor decisions in the Indian stock market, with 64.2% of investors affected in 2011 (Chandra, Abhiject, and Kumar, Ravinder, 2011, p.3) In contrast, the same factor had a positive impact of 11% on investment decision-making in the Vietnamese stock exchange in 2012.
Market fluctuations, along with the fundamental principles of underlying stocks and their prices, often lead to overreactions and underreactions among investors This behavioral aspect significantly impacts investment decision-making processes.
Researchers including DeBondt & Thaler (1985) studying over reaction (p.804) and
Research by Lai (2001) indicates that news significantly impacts investor strategies, leading to positive investment decisions Additionally, Barberis, Shleifer, and Vishny (1998) highlight that stock prices often under-react to earnings announcements while over-reacting to consecutive positive or negative news.
Research indicates that investors tend to overreact to stocks with strong past performance, leading to lower returns compared to those with poor past performance, which are expected to continue underperforming (Vishny, 1994) Additionally, DeBondt and Thaler (1995) highlight that fluctuations in stock prices and news events can trigger both over- and under-reactions among investors Furthermore, Waweru et al (2008) identify several behavioral factors influencing investor decision-making, including price changes, market information, historical stock trends, customer preferences, and reactions to price changes and stock fundamentals.
Investors' decisions can be either rational or irrational, with their investment outcomes evaluated based on the returns achieved Lin and Swanson (2003) identify three types of returns—raw returns, risk-adjusted returns, and momentum-adjusted returns—assessing them over five time horizons: daily, weekly, monthly, quarterly, and yearly (p.208) Furthermore, Oberlechner and Osler (2004) argue that the rate of investment return serves as an objective measure of decision-making, highlighting the positive influence of overconfidence on investment choices (p.1-33).
Suggested research model
The paper studies behavioral factors of investors in HOSE, Vietnam
Therefore it is mostly based on combining the framework of researches in Asia such as Vuong & Dao (2012) in the HOSE and results of research by Chandra and Kumar
The study focuses on the Indian Stock Exchange, highlighting the relevance of comparing behavioral factors between India and Vietnam, both located in Asia This geographical context enhances the validity of the findings The analysis aims to identify which behavioral factors are significant for further research Notably, as shown in Table 2, 53.2% of Indian investors exhibit the gambler's fallacy, a behavioral factor that has not yet been addressed by Vietnamese researchers.
This paper aims to explore the representativeness factor in the Indian stock market, a topic previously studied in the Vietnamese stock exchange Notably, while some behavioral factors have been analyzed in both markets, significant discrepancies exist in their impact levels For instance, availability bias affects 57.7% of Indian investors compared to just 17.4% in Vietnam Similarly, mental accounting bias influences 64.2% of Indian investors, whereas only 11% of Vietnamese investors are impacted These findings highlight the necessity for further research into these behavioral factors in the context of the Indian stock market.
Table 2.1: Results of behavioral factors affecting investment decision making of individual investors
Behavioral factors Percentage of affected
Percentage of affected Vietnamese investors (Vuong & Dao, 2012)
Regret aversion bias Some evidences 51,2
Herd behavior: buying and selling decisions
Over and Under – reaction Not concerned Not concerned
While Vuong and Dao (2012) identify several factors influencing the investment decisions of individual investors, additional elements warrant further exploration Vietnamese experts, including Ngo (2010) and Ho (2007), highlight four key behavioral factors prevalent in the Vietnamese stock market: overconfidence, availability bias, loss aversion bias, and herding behavior Although Vuong and Dao (2012) discuss the first three, herding behavior remains unexamined Therefore, drawing on Ngo (2010), this paper proposes to investigate herding behavior as a significant behavioral factor, leading to the formulation of Hypothesis 1.
H1: Herding behavior positively impacts the investment decisions of individual investors at the HOSE
Research by Ho (2007) highlighted several behavioral factors influencing the Vietnamese market, such as availability bias, representativeness, anchoring, gambler's fallacy, over and under-reaction, overconfidence, mental accounting, and herding behavior in decision-making Vuong and Dao (2012) also identified these factors, specifically noting the importance of over and under-reaction and gambler's fallacy Consequently, this paper proposes further investigation into these two factors, leading to the formulation of Hypotheses 2 and 3.
H2: Over and Under-reaction positively affects the investment decisions of individual investors at the HOSE
H3: Gambler’s fallacy positively influences the investment decisions of individual investors at the HOSE
Table 2 reveals significant differences in behavioral factors influencing investment decisions among Vietnamese and Indian investors, particularly in mental accounting and representativeness Notably, 64.2% of Indian investors are impacted by mental accounting, compared to only 11% of Vietnamese investors This stark contrast prompts a reevaluation of these two factors, specifically addressing hypothesis 4.
H4: Mental accounting bias positively impacts the investment decisions of individual investors at the HOSE
H5: Representativeness bias positively influences the investment decisions of individual investors at the HOSE
Research by Vuong and Dao (2012) indicates that availability bias accounts for only 17.4% of investor behavior, suggesting its minimal impact on behavioral finance However, Vietnamese experts Ngo (2010) and Ho (2007) highlight that this bias significantly influences investor psychology in the Vietnamese stock market.
Therefore the paper will study this factor more to consider whether or not this factor has much effect on investment decision-making And hypothesis 6 is presented below:
H6: Availability bias positively influences the investment decisions of individual investors at the HOSE
This article examines the Ho Chi Minh Stock Exchange by integrating behavioral factors identified in the research of Vuong and Dao (2012) along with insights from Vietnamese experts and studies conducted by Waweru et al (2008) and Chaudhary (2013).
This study, based on the works of Ho (2007) and Ngo (2010), aims to explore the key behavioral factors influencing individual investors' decision-making on the Ho Chi Minh Stock Exchange (HOSE) The research focuses on various psychological biases, including herding behavior, overreaction and underreaction to market information, gambler’s fallacy, mental accounting bias, as well as representativeness and availability biases.
Research model
Through presented above, the research mode is proposed as follows:
A chart of research model is proposed by the author
RESEARCH METHODOLOGY
Research design
The research utilized both qualitative and quantitative methods, beginning with a pilot study that employed qualitative techniques and in-depth interviews This approach allowed the author to identify behavioral factors influencing investor decision-making, which informed the subsequent official research The pilot interviews, conducted with 10 seasoned investors with over a decade of stock investment experience, revealed errors and highlighted questions that were not applicable in practice This feedback was crucial for refining the questionnaire, ensuring its effectiveness for the official study The finalized questionnaires, presented in both English and Vietnamese, are included in Appendix 5.1, detailing the qualitative research design and interview questions.
The official research employed quantitative methods, utilizing selected questionnaires to assess the agreement levels of 220 investors in the HOSE Initially, a pilot survey was conducted with 52 investors to evaluate the reliability of the measurement scales The study focused on testing the scales' measurement reliability (NR).
SPSS software to test Cronbach‟s Alpha based on standardized items After deleting some questionnaires that were not reliable, the author distributed the questionnaire to
The study engaged 170 additional investors and gathered a total of 220 questionnaires to assess measurement reliability and validity through exploratory factor analysis (EFA) Subsequently, SPSS software was utilized to conduct regression analysis, testing six key assumptions.
The five Ho Chi Minh Stock Exchanges with the highest trading volumes are Dong A Securities (DAS), Ho Chi Minh Securities Corporation (HSC), Saigon Securities Incorporation (SSI), FPT Securities (FPTS), and Viet Capital Securities (BVSC) For further insights, two representatives from each of these exchanges will be randomly selected for interviews.
Define reseach problem literature review research model qualitative research
(in- depth interview, n) proposed research model and adjusted questionnaires pilot survey
(formal questionnaires, nP) test of scales measurement reliability, NP test of Cronbach's alpha survey questionnaries, (N"0) test of scales measurement reliability (N"0)
(test of Cronbach's alpha) test of scales measurement validity - factor analysis - EFA (N"0) regression alnalysis test of six assumptions discussion, recommendation and conclusion
According to Hatcher (1994), the minimum sample size for exploratory factor analysis (EFA) should be at least five times the number of variables or 100, which in this case requires a sample size of 205 for the 41 questions in the study With a sample size of 220, the study meets this criterion Furthermore, Tabachnick & Fidell (2011) state that for regression analysis, the sample size must exceed R + 8*k, where k is the number of independent variables Here, the calculation shows that a sample size greater than 108 is necessary (N > 52 + 8*7) Consequently, 220 questionnaires were distributed to private investors across various stock exchanges, fulfilling the necessary requirements for both EFA and regression analysis.
Adjusted research model
Qualitative research indicates that behavioral factors significantly impact investor decisions, with representative bias, availability bias, gambler's fallacy, and over- or under-reaction being the most influential Investors tend to favor blue chip stocks, particularly in the current challenging financial climate in Vietnam, leading them to be cautious and avoid poorly performing stocks Availability bias drives a preference for local stocks over foreign ones, as government regulations complicate foreign trading and investors feel insecure about their understanding of foreign markets Consequently, they prioritize familiar stocks to safeguard their investments, reflecting a tendency to invest in assets they are knowledgeable about rather than unfamiliar options Additionally, the gambler's fallacy influences their decision-making process, causing them to overestimate the likelihood of favorable outcomes based on past experiences.
With a decade of stock investment experience, many investors have found they can effectively identify reversal points to guide their decisions, often making accurate predictions They commonly feel compelled to purchase stocks that have experienced multiple sessions of price decline, believing further drops are unlikely, and many have been fortunate in these instances Investors emphasize the importance of carefully analyzing stock price fluctuations before making investment choices, particularly in the current economic landscape Additionally, they recognize the critical role of market information, noting that the Vietnamese market is particularly sensitive and requires regular review; neglecting this information can lead to significant investment losses.
This paper identifies six key behavioral factors for research: representative bias, availability bias, gambler's fallacy, over- and under-reaction, herd behavior, and mental accounting bias Most investors expressed a desire for further exploration of these factors, noting that herd behavior and mental accounting bias were observed at moderate levels.
It means they do not support but they do not reject them either As a result, the research model after qualitative research and hypotheses do not change against at first
Scales measurement design
This study utilizes questionnaires informed by the practical research of Waweru et al (2008), with modifications made to align with the Vietnamese stock market context Six behavioral factors are examined: Representative bias, availability bias, herd behavior, mental accounting bias, gambler's fallacy, and over- underreaction A summary of the adjustments made to the measured questions based on interviews can be found in Appendix 5.2.
3.3.1 Scales measurement of Representative bias
The variable of Representative bias, denoted as REP, is measured through three key items, as outlined by Waweru et al (2008) One notable item states, "You buy 'hot stocks' and avoid stocks that have performed poorly in the recent past." However, many investors found this question challenging to comprehend clearly.
The term "hot stock" has become too vague for the current Vietnamese stock market, prompting a shift to more specific categories such as "blue chip stocks," "midcap stocks," and "penny stocks." Consequently, the original question regarding "hot stocks" has been replaced with three distinct inquiries: Question 1: Do you prefer buying blue chip stocks? Question 2: Do you prefer investing in midcap stocks? Question 3: Do you favor purchasing penny stocks? Additionally, a fourth question has been introduced, separate from the first: What factors influence your stock purchasing decisions?
Investors typically avoid purchasing stocks that have demonstrated poor performance in the recent past However, analyzing a select group of representative stocks can be challenging when making investment decisions for a broader range of stocks It was noted that the term "all" was inadequate and needed to be replaced with a more specific phrase Consequently, the question regarding the use of trend analysis for investment decisions was revised to state that investors analyze the effective operations of representative companies to inform their decisions for all other stocks within the same industrial field This approach highlights the importance of representative bias in investment strategy.
Table 3.1 outlines the concept of representativeness in investment behavior, highlighting various biases Investors often exhibit a preference for blue chip stocks, midcap stocks, or penny stocks, while typically avoiding those with poor recent performance Additionally, they analyze the effective operations of representative companies within a specific industry to inform their investment decisions Furthermore, there is a tendency to select stocks that are representative of the VN30 index in the stock market, reflecting a broader trend in investment strategies.
3.3.2 Scales measurement of mental accounting bias
Mental accounting bias, particularly prevalent among male investors, is measured through three key items identified by Waweru et al (2008) Investors often compartmentalize their investment portfolios, treating each stock independently due to its unique characteristics, business type, and industry This tendency leads them to overlook the interconnections between various investment options, resulting in decisions to buy or sell based solely on isolated factors rather than considering the overall portfolio.
Table 3.2: Items of mental accounting bias
Men often exhibit mental accounting bias, viewing each component of their investment portfolio in isolation This tendency leads them to overlook the interconnections between various investment options, resulting in evaluations based solely on individual factors rather than considering the overall performance of the entire portfolio.
3.3.3 Scales measurement of gambler’s fallacy
The gambler's fallacy, represented by the variable GAM, highlights the misconceptions investors hold regarding stock price movements Waweru et al (2008) identified four key items to measure this variable, revealing that most investors, with over a decade of experience in the HOSE, believe they can predict price reversals They tend to buy stocks after prolonged price declines, convinced that further decreases are unlikely, and sell stocks following significant price increases, believing that further gains are improbable This behavior aligns with technical analysis patterns, such as the shoulder-head-shoulder formation However, when asked if they could invest without both fundamental and technical analysis, most disagreed, indicating the necessity of both approaches for informed decision-making The ambiguity of the question led to confusion among participants, prompting a suggestion for a clearer alternative focused on economic indicators like GDP and CPI Additionally, investors acknowledged the influence of market rumors on their decisions, underscoring the sensitivity of the current stock market and reinforcing the gambler's fallacy, which is rooted in belief and luck Consequently, investors proposed two additional questions to better assess their decision-making processes.
„You decide to buy or sell stocks based on your forecast‟ and „You believe you are often lucky to invest in stocks‟ Items of gambler‟s fallacy were shown below:
Table 3.3: Items of gambler’s fallacy
The gambler's fallacy can significantly influence investment decisions For instance, some investors may choose to buy a stock after its price has declined over several sessions, believing it unlikely to drop further Conversely, others might sell a stock after multiple price increases, thinking it cannot rise any higher Many investors also prioritize economic indicators such as GDP and CPI before making investment choices Additionally, some rely on rumors or personal beliefs about their luck when deciding to buy or sell stocks, while others base their actions on their own forecasts.
3.3.4 Scales measurement of availability bias
Variable of availability bias is signed as AVAIL According to Waweru et al
In a study conducted in 2008, three key items were identified to measure the investment behavior of individuals with over 10 years of experience in stock investment Despite their extensive knowledge, these investors expressed a reluctance to engage in foreign stock exchanges, favoring local stocks due to the greater availability of information They also regarded insights from close friends and relatives as reliable resources for making investment decisions, particularly when they were busy This reliance on personal networks contributed to their confidence in investing in companies with which they had prior knowledge Overall, the findings highlighted the persistence of availability bias in their investment choices.
Table 3.4: Items of availability bias
Availability bias influences your investment choices, leading you to favor local stocks over international options due to the greater accessibility of information You often rely on insights from close friends and family, viewing their opinions as trustworthy references for your investment decisions Additionally, you are inclined to invest in companies where you have personal experience, either through previous employment or familiarity.
3.3.5 Scales measurement of herd behavior
Variable of herd behavior is signed as HERD According to Waweru et al
In 2008, a study revealed that many investors actively listened to and observed the strategies of their peers, particularly in a volatile stock market They were significantly influenced by the decisions of other investors, which impacted their choices regarding stock selection and the quantity of shares bought or sold This led them to question the motivations behind others' trading activities, pondering whether there was undisclosed positive or negative news affecting specific stocks Consequently, it became evident that making informed decisions based on comprehensive analysis was essential.
“crowd” and they agreed that „unitied we stand, divided we fall.‟ In short, items of herd behavior maintain and were shown below:
Table 3.5: Items of herd behavior
Herd behavior significantly influences your investment choices, as the decisions made by other investors regarding stock types can affect your buying decisions Similarly, their choices related to stock volume impact your selling strategies Ultimately, the collective actions of investors in buying and selling stocks play a crucial role in shaping your overall investment decisions.
3.3.6 Scales measurement of over-underreaction
The variable of over-under reaction, known as CREAT, is measured by three key items as identified by Waweru et al (2008) In today's stock market, investors must react swiftly to stock price changes to avoid significant financial risks While market information can often be misleading, it remains crucial for investors aiming to mitigate potential losses When stock prices fluctuate, investors analyze the underlying reasons before making investment decisions, responding not only to price shifts but also to company-specific news, such as financial results that can significantly influence stock value To enhance the understanding of investor behavior, a fourth question was proposed: "Do you usually react quickly to information relating to the stock's company?" Additionally, awareness of industry-related information is essential, prompting the fifth question: "Do you usually react quickly to information relating to the stock's industry?" Furthermore, global economic changes, particularly in European and American markets, have a substantial impact on local economies and stock exchanges, leading to the suggestion of a sixth question: "Do you usually react quickly to changes in the world economy?" In summary, six items related to over-under reaction have been identified for further exploration.
Table 3.6: Items of over-underreaction
Market information plays a crucial role in stock investment decisions Investors must carefully analyze price changes of stocks they intend to buy, as overreaction to these fluctuations can lead to poor choices Quick reactions to news about a company's performance, its industry, and broader economic changes are common, but it's essential to balance these responses to make informed investment decisions.
3.3.7 Scales measurement of investment decision
Data analysis approach
3.4.1 Test of scales measurement reliability
Cronbach’s Alpha Test is utilized to assess the reliability of measurement scales using 5-point Likert scales, ensuring the consistency of responses from a specific participant sample This test also aids in predicting the reliability of respondents' answers (Helms, Henze, Sass & Mifsud, 2006, p.633) Commonly employed in behavioral research, Cronbach’s Alpha serves as a key indicator of reliability (Liu, Wu & Zumbo, 2010, p.5).
Cronbach's alpha is ideal for this research, as it utilizes a 5-point Likert scale and focuses on behavioral finance The study applies Cronbach's alpha to assess the reliability of measurement scales derived from factor analysis According to Nunnally (1978), a Cronbach's alpha of at least 0.7 is necessary for reliable measurements, while Shelby (2011) suggests that a value above 0.6 may also be acceptable Additionally, it's crucial to consider corrected item-total correlations, which should be 0.3 or higher to ensure validity This research aims for a Cronbach's alpha between 0.7 and 0.8, with corrected item-total correlations of 0.3 or more, given that the financial behavior measurements are novel for Ho Chi Minh Stock Exchange stakeholders Furthermore, the acceptable significance level for the F-test in the Cronbach's alpha analysis is set at 0.05 or lower.
Cronbach‟s alpha test is finished by SPSS software
This study employs Exploratory Factor Analysis (EFA) to identify the underlying factors related to the behavioral finance variables from questions 12 to 43 of the questionnaire EFA helps streamline the questionnaire by eliminating items that do not meet analytical criteria, as noted by O'Brien (2007) It is specifically used to test the hypotheses outlined in the research model from Chapter 3 The analysis adheres to several criteria, including factor loadings, Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy, total variance explained, and Eigenvalue Factor loadings, defined as the correlation between each item and its corresponding factor, should exceed 0.5 for a sample size of 100 to ensure practical significance (Hair et al., 1998) Additionally, the KMO value indicates the appropriateness of EFA for the data, with acceptable values ranging from 0.5 to 1.0.
0.005) to make sure that factor analysis is suitable for the data (Ali, Zairi & Mahat,
In factor analysis, total variance explained is crucial for determining the number of factors to retain, with a recommendation that it exceeds 50% (Hair et al., 1998) Each factor's Eigen-value indicates the amount of variance explained by that factor, and it should be greater than 1; a value less than 1 suggests that the factor explains less variance than a single variable (Leech, Barrett & Morgan, 2005) The Exploratory Factor Analysis (EFA) is typically conducted using SPSS software.
DATA ANALYSIS AND FINDINGS
Data description
The study revealed that the majority of investors were male, comprising 75.46% of the total, while female investors accounted for 24.54% Additionally, the data indicated that most investors fell within the age range of 26 to 45 years old.
Dissecting the data even more, 57.27% were between the age of 26 and 35 and
42.73% were between the ages of 36 and 45 Next was the work experience of the respondents: most investors had worked for less than 5 years with 10.09%, from 5 to
Over a decade, 47.27% of investors accumulated their experience, while 33.64% have over ten years in the field The average monthly income of investors indicates that 48.18% earn between 12 to 20 million VND, 41.82% earn from 6 to 12 million VND, and around 10% exceed 20 million VND In terms of investment duration, 35.45% have been involved in stocks for 5 to 10 years, 39.09% for 3 to 5 years, and only 11.82% for 1 to 3 years Notably, 85.45% of investors received formal training in stock market investment, while 14.55% did not participate in any training courses Regarding their investments in the HOSE last year, 30.91% invested between 60 to 120 million VND, 20.91% between 120 to 300 million VND, 14.55% between 600 to 900 million VND, and 11.82% invested over 900 million VND Detailed data can be found in Appendix 5.4.
Factor analysis of behavioral variables influencing the individual investment
A pilot survey was conducted to assess the reliability of measurement scales, aiming for Cronbach's alpha values between 0.7 and 0.8 to ensure measurement consistency Independent variables were represented by behavioral factor questions ranging from X12 to X38, while dependent variables focused on investor evaluations of their own investment decisions, represented by questions Y39 to Y41.
Table 4.1: Idependent variables, dependent variables and items
1.Representativeness rep1, rep2, rep3, rep4, rep5, rep6
2.Mental accounting bias men1, men2, men3
3.Gambler‟s fallacy gam1, gam2, gam3, gam4, gam5, gam6 and gam7
4.Availability bias avail1, avail2 and avail3
5.Herding behavior herd1, herd2 and herd3
6.Over and under creation creat1, creat2 and creat3, creat4, creat5 and creat6
Investment decision return1, return2, return3
4.2.2 Results of Cronbach’s alpha analysis of pilot survey (NR)
The study assessed the reliability of measurement scales, revealing that the first factor, representativeness, had a low Cronbach's alpha for item rep3, leading to its removal The second factor, mental accounting bias, maintained acceptable reliability with no items below 0.7 The third factor, Gambler's fallacy, had items gam6 and gam7 with low Cronbach's alpha, resulting in their deletion The fourth and fifth factors, availability bias and herd behavior, exhibited no items with low reliability However, the sixth factor, over and under creation, included item creat6, which had a Cronbach's alpha below 0.7, prompting its removal Notably, the dependent variable showed no items with a Cronbach's alpha lower than 0.7.
Table 4.2: Cronabach’s alpha test of items (NR)
1.Representativeness rep1, rep2, rep3, rep4, rep5, rep6
2.Mental accounting bias men1, men2, men3
3.Gambler‟s fallacy gam1, gam2, gam3, gam4, gam5, gam6 and gam7 4.Availability bias avail1, avail2 and avail3
5.Herding behavior herd1, herd2 and herd3
6.Over and under creation creat1, creat2 and creat3, creat4, creat5 and creat6
Investment decision return1, return2, return3
4.2.3 Results of Cronbach’s alpha analysis of official survey (N"0)
Following the pilot survey, certain questions were removed due to unsatisfactory Cronbach's alpha values, which were below the acceptable threshold of 0.7 The remaining reliable behavioral factor questions, ranging from X12 to X34, served as independent variables Meanwhile, questions Y35 to Y37 were designed to assess investors' evaluations of their own investment decisions, functioning as dependent variables The Cronbach's alpha values for the independent variables were as follows: representative bias (0.789), mental accounting bias (0.709), gambler's fallacy (0.748), availability bias (0.711), herd behavior (0.777), and over-under reaction (0.748), all exceeding the 0.7 threshold The dependent variable, investment decision, also demonstrated reliability with a Cronbach's alpha of 0.788 Furthermore, the corrected item-total correlations for all variables were above 0.3, indicating adequate reliability for exploratory factor analysis (EFA) Detailed Cronbach's alpha analysis results are available in Appendix 5.5.
Table 4.3: Cronbach’s alpha test of items (N"0)
Items Scale Mean if item deleted
Scale variance if item deleted
Cronbach‟s alpha if item deleted
REP Representative bias: Cronbach’s alpha: 0.789 rep1 12.21 12.595 0.776 0.655 0.665 rep2 12.91 14.202 0.412 0.318 0.795 rep3 12.16 13.626 0.688 0.518 0.700 rep4 12.52 13.612 0.604 0.538 0.724 rep5 12.40 15.757 0.365 0.231 0.799
MEN Mental accounting bias: Cronbach’s alpha: 0.709 men1 6.56 3.382 0.347 0.120 0.827 men2 6.85 2.713 0.622 0.505 0.490 men3 6.77 2.697 0.630 0.508 0.480
GAM Gambler’s fallacy: Cronbach’s alpha: 0.748 gam1 12.68 12.090 0.444 0.319 0.727 gam2 12.71 10.015 0.602 0.478 0.667 gam3 12.37 12.025 0.529 0.344 0.699 gam4 12.26 12.302 0.419 0.284 0.736 gam5 12.61 11.471 0.581 0.426 0.679
AVAIL Availability bias: Cronbach’s alpha:0.711 avail1 6.68 2.713 0.440 0.223 0.731 avail2 6.99 3.333 0.501 0.338 0.636 avail3 6.64 2.642 0.640 0.434 0.448
HERD Herd behavior: Cronbach’s alpha: 0.777 herd1 7.19 2.210 0.473 0.225 0.831 herd2 6.48 1.876 0.662 0.549 0.605 herd3 6.58 2.309 0.695 0.555 0.607
CREAT Over-underreaction: Cronbach’s alpha: 0.748 creat1 14.46 8.523 0.390 0.255 0.743 creat2 13.79 7.198 0.499 0.384 0.711 creat3 13.55 7.171 0.641 0.514 0.650 ceat4 13.75 7.565 0.618 0.427 0.663 creat5 14.28 8.651 0.428 0.282 0.729
RETURN Investment decision: Cronbach’s alpha: 0.788 return1 6.25 2.802 0.595 0.427 0.746 return2 6.15 2.652 0.744 0.555 0.583 return3 6.13 3.070 0.550 0.355 0.789
Table 4C indicates that all variables have Cronbach's Alpha values exceeding 0.7, with corrected item-total correlations above 0.30, confirming the reliability of the items in Table 5B for exploratory factor analysis (EFA) Detailed Cronbach's Alpha results for these items, calculated using SPSS, are available in Appendix 5.5.
The exploratory factor analysis (EFA) was conducted to examine the behavioral variables influencing investment decisions, specifically focusing on questions starting from X12 The analysis adhered to the requirements outlined in Chapter 3, successfully reducing the items without deleting any that met the criteria The results revealed seven grouped variables, comprising six independent variables and one dependent variable Key conditions for the analysis included a KMO value greater than 0.5, a significant Bartlett test (p < 0.05), and factor loadings of 0.5 or higher; items with lower loadings were excluded Additionally, the total variance explained was above 50%, and the absolute differences between loadings were at least 0.3 An Eigen-value greater than 1 was also confirmed, indicating that the factors accounted for more information than individual items.
Results of EFA presented that KMO=0.528> 0.5 Therefore factor loading was suitable for data of survey Bartlett – Significant level is 0.000 less than 0.05
The factor analysis revealed a significant correlation among the items (p < 0.05), justifying its use While most factor loadings exceeded 0.5, items such as gam4 and gam1 were removed due to lower loadings The Rotate Factor matrix indicated that items like herd1, avail2, create1, rep3, creat5, gam5, and creat1 had loadings above 0.5; however, herd1, avail2, and creat1 were deleted as their differences in loadings were less than 0.3 The total variance explained showed that seven factors had initial eigenvalues greater than 1, with rotation sums of squared loadings at 66.142%, confirming the measurement scales' validity Consequently, five items, including herd1, gam4, gam1, avail2, and creat, were eliminated from the analysis Further details on the Cronbach’s alpha analysis can be found in Appendix 5.5.
Table 4.4: KMO and Bartlett’s Test, total variance explained and rotated component matrix (EFA time 1)
=> The result was accepted Initial Eigenvalues 1.511 (component 7) >1 => acceptable
Rotation sums of squared loadings
Component 1 rep1, rep2, rep3, rep4, rep5 Component 2 herd3, herd2, creat5, herd1 herd1 deleted
Component 3 creat2, creat3, creat4 Component 4 return1, return2, return 3 Component 5 gam1,gam2, gam3, gam4, gam5 gam1 and gam4 deleted
Component 6 avail1, avail2, avail3, creat1 creat1 and avail2 deleted
After removing items such as herd1, gam1, gam4, avail2, and creat1, the analysis yielded a KMO value of 0.544, which is above the 0.5 threshold, and a Bartlett's test significance level of 0.000, indicating strong factorability All factor loadings were above 0.5, and the Rotate Factor matrix showed that each item had loadings exceeding 0.3, confirming their acceptance The total variance explained indicated that seven factors had initial eigenvalues greater than 1, with a rotation sum of squared loadings at 71.362%, surpassing the 50% criterion, thus validating the measurement scales Detailed results are presented in Table 4.5.
Table 4.5: Summary of KMO and Bartlett’s Test, total variance explained and rotated component matrix (EFA time 2)
Initial Eigenvalues 1.401 (component 7) >1 => met requirement Rotation sums of squared loadings
The study utilized SPSS software to categorize data into seven components, as illustrated in Table 4.6 All item loadings exceeded 0.5, confirming their suitability for grouping Following exploratory factor analysis, these components were employed in regression analysis Detailed findings of the Cronbach's alpha analysis for time 2 can be found in Appendix 5.5.
Table 4.6: Rotated component matrix (EFA time 2)
Component Items cluster Factor loadings
Regression analysis
Regression analysis is a statistical method used to examine the relationships between variables, as noted by Leech, Barrett, and Morgan (2005) The author gathered data on various variables and applied regression techniques to estimate how independent variables influence dependent variables To assess the predictive power of the model, researchers utilized adjusted R², which is typically lower than unadjusted R², to indicate the percentage of variance explained by the independent variables, with adjustments influenced by effect size and sample size Additionally, the author employed the F-test to evaluate the significance of the model, presenting hypotheses H0 and H1 for analysis.
H0: β1 = β2 = … = βk = 0 (no linear relationship) H1: at least one βi ≠ 0 (at least one independent variable affects Y)
If p-value
