Heuristics influencing investment decision making and investment performance evidence from individual investors in vietnam

INTRODUCTION

Background

In financial markets, both individual investors and fund managers rely on decision-making tools like fundamental and technical analysis to guide their investment choices It is believed that the information structure and behavioral factors in the market significantly impact these decisions and overall market outcomes (Mutswenje, 2014) However, actual investor behavior is frequently influenced by psychological principles, which help to clarify the reasons behind buying or selling stocks These behavioral factors emphasize how investors perceive and respond to information when making investment decisions.

Economic and financial theories often assume that individuals make rational decisions based on all available information; however, evidence indicates that humans frequently exhibit irrational behavior when faced with uncertainty (Bernstein, 1996) Behavioral finance, which integrates psychological insights into market behavior, helps explain why investors choose to buy, sell, or hold stocks A key challenge for investors lies in their decision-making processes, as their profits or losses largely stem from these choices The 2008 subprime crisis highlighted this issue, demonstrating that even highly educated investors were not immune to the failures of traditional rational market models (Subash, 2012).

In the behavioral finance discipline, heuristics can be defined as the use of experience and practical efforts to answer questions or to improve performance (Fromlet,

2001) Raines & Leathers (2011) argue that when faced with uncertainty, people rely on

1 heuristics or rules of thumb to subjectively assess risks of alternatives, which reduces the complex tasks of assessing probabilities and predicting values to simpler judgmental

It could be seeing that, heuristics are quite useful in investment decision making (Waweru et al., 2008, p.27), however they may lead to biases (Kahneman & Tversky,

1974, p.1124; Ritter, 2003, p.431) For instance, Kahneman & Tversky (1974, page

1124) introduce three factors belongs to heuristics namely representativeness, availability bias, and anchoring, while Waweru et al (2008) also list two factors named Gambler’s fallacy and Overconfidence of heuristic

There are several studies in the literature investigating the relationship between heuristics and decision making and performance of individual investor as well Tversky

Kahneman (1974) explored judgment under uncertainty, focusing on heuristics and biases, while Hassan et al (2013) investigated how affect heuristics, particularly fear and anger, influence individual investors' decision-making Additionally, Grinblatt and Keloharju (2000) analyzed the investment behavior and performance of different investor types in Finland's stock market.

Research problem

Numerous studies in the literature indicate that investors' decision-making processes are significantly influenced by behavioral finance Researchers have explored various psychological and sociological factors, including heuristics, that impact individual investment decisions (Subrahmanyam 2007, Le & Doan 2011, Kengatharan 2014).

In Vietnam, the first official stock exchange, namely the Ho Chi Minh Stock Exchange (known as HOSE) has been launched since mid-2000 and five years later, the

The Ha Noi Stock Exchange (HNX) was established during a period when the Vietnamese stock market was largely unfamiliar to the local population, hindered by a weak legal framework, a basic trading system, a limited number of securities firms, and a narrow range of investment options However, both the HNX and the Ho Chi Minh Stock Exchange (HOSE) have experienced significant development in recent years, enhancing their appeal and functionality within the financial landscape.

Vietnamese stock market has experienced significant development with regards to the market size and market

As of November 2014, the Vietnamese stock market comprised 345 companies on the HOSE with a listed value of nearly 339 trillion VND, and 367 companies on the HNX with a listed value of approximately 92.4 trillion VND, totaling over 145 trillion VND in market value However, when compared to foreign stock markets, Vietnam's stock market is significantly smaller in both market size and capitalization (Le & Doan, 2011).

The Ho Chi Minh Stock Exchange has experienced significant growth in both the number of listed stocks and trading values, yet its VN-Index exhibits unpredictable fluctuations over time Research indicates that various factors, particularly behavioral influences like the herding effect and heuristics, as well as market dynamics, play a crucial role in shaping the investment decisions of individual investors (Waweru, 2008; Hassan et al., 2013).

Several studies in the literature show that individual investors have difficulties making investment decisions due to lack of financial sophistication (Winchester et al.

In 2011, research indicated that individual investors frequently rely on heuristics or rules of thumb when making investment decisions (Shikuku, 2010) This raises the question of whether heuristic factors significantly impact the decision-making processes of individual investors in the Vietnamese stock market Therefore, this study aims to explore the effect of these heuristic influences on the decision-making and performance of individual investors within this specific market context.

Research objective and research questions

The objective of the study is to investigate impacts of heuristic factors on individual investors’ decision-making and their investment performance More specifically, two questions are given as follows:

• Question 1: Do heuristic factors influence individual investors’ decisions in the Vietnamese stock market?

• Question 2: Does a strong tendency of investment decision making have a positive influence on the investment performance?

Scope of the research

Investor behavior in stock markets is significantly influenced by various heuristic factors, particularly representativeness, availability, and overconfidence Research has highlighted the impact of overconfidence bias (Glaser and Weber, 2012) and representativeness bias (Taffler, 2012), while also examining how these behavioral factors affect investment decision-making among unit companies in Kenya (Shikuku, 2010).

A significant concern for individual investors arises from a 2014 report by Wall Street Securities, which reveals that they represent over 60% of trading activity in the Vietnamese stock market.

In addition, due to the time constraint, the research focuses only on the heuristic behaviors of individual investors studying or working at the Ho Chi Minh City only The

Ho Chi Minh City is recognized as Vietnam's largest economic hub, hosting the country's premier stock exchange, the Ho Chi Minh Stock Exchange.

Structure of the thesis

This thesis is organizes in five chapters as follow:

Chapter 1 is an introduction chapter This chapter describes an overview of research background, research problem, and objective Besides, the scope of research, implications, and structure of thesis are also present.

Chapter 2 is about presenting previous research done on the stream of studies related to theoretical foundation regarding to explain prospect theory and heuristics theory as well Besides, heuristics of individual investors also is presented in detail in the research More importantly, investment decision making and investment performance of individual investors is clearly explored as well This chapter is to concentrate on explaining each variable in the model, and reasons for choosing them to be included in the research model.

Chapter 3 is research methodology chapter Firstly, research process is presented in general Then, research design and sampling are also mentioned regarding to qualitative method and quantitative method as well After that, the measurement scales apply for the research factors will be determined clearly and suitably This chapter also defines how to collect data and analyze the data collected to test the research hypotheses proposed in chapter 2 Finally, research method is explained in detail regarding to Cronbach alpha, Exploratory Factor Analysis and Multiple regression analysis.

Chapter 4 is the analysis and discussion chapter In detail, data background is firstly mentioned and measurement reliability of each factor using Cronbach’s alpha is properly presented as well Moreover, scale testing by using Exploratory Factor Analysis and multiple regression analysis is explored in detail in the session. Furthermore, this part also discusses the method for collecting data used to test the hypothesis, and it analyses the data received, its reliability and multiple regression as well.

Chapter 5 presents the research findings and results, offering a comprehensive conclusion along with implications and limitations of the study Additionally, this thesis provides recommendations for future research in the relevant topic area.

LITERATURE REVIEW, HYPOTHESIS AND RESEARCH MODEL

Theoritical foundation

Prospect theory, developed by Daniel Kahneman and Amos Tversky in 1979, significantly influences economic research by explaining how individuals assess losses and gains (Okur & Gurbuz, 2014) This behavioral theory addresses the shortcomings of expected utility theory, particularly in decision-making under uncertainty It highlights that investors perceive gains and losses through an S-shaped utility function, revealing psychological factors such as regret aversion, loss aversion, and mental accounting that impact decision-making processes (Okur & Gurbuz, 2014).

In behavioral finance, individuals establish a reference point for wealth comparison, influencing their risk-taking behavior When wealth falls below this reference point, investors become risk-seeking, willing to engage in riskier investments to regain their preferred wealth level Conversely, when wealth exceeds the reference point, they exhibit risk aversion, aligning with traditional economic theories Kahneman and Tversky highlighted that individuals tend to be risk-seeking in the context of losses, as noted in Finucane et al (2002) The utility function for gains is concave, indicating that while people enjoy gains, the pleasure derived does not double with double the gain In contrast, the utility function for losses is convex, suggesting that although individuals feel pain from losses, the emotional impact does not double with increased loss.

The theory was used to get an overall review about behaviors of individual investors in investment decision making that includes heuristic factors According to Okur & Gurbuz

In 2014, a review of prospect theory in finance highlighted that expected utility theory, along with its rational expectations component, remained the prevailing framework guiding investor and economic decision-making.

Heuristics, as defined by Bramson (2007), serve as a normative decision theory that simplifies decision-making in complex and uncertain environments These rules of thumb help reduce the complexity of evaluating probabilities and predicting outcomes, enabling quicker judgments (Kahneman & Tversky, 1974) While heuristics can be particularly beneficial when time is constrained (Waweru et al., 2008), they may also result in cognitive biases (Kahneman & Tversky).

According to Balota et al (2004), numerous decisions hinge on beliefs about the probabilities of uncertain events, including election outcomes, a defendant's guilt, or the future value of currency These beliefs are often articulated through specific statements.

Beliefs about uncertain events are often expressed through phrases like "I think that" or "it is unlikely that," and can sometimes be quantified as odds or subjective probabilities The formation of these beliefs is influenced by various factors, leading individuals to evaluate the likelihood of uncertain outcomes To simplify the complex process of assessing probabilities and making predictions, people often rely on a limited set of heuristic principles that streamline their judgmental operations.

According to Selden (1912), the fluctuations in stock market prices are significantly influenced by the psychological perspectives of investors and traders Finucane et al (2000) further highlight that there exists a negative correlation between the perception of risk and the perceived benefits, which is attributed to the affect heuristic.

Investors often seek high returns on their investments, but their decisions can be significantly influenced by the affect heuristic, which may lead them to overlook potentially lucrative stocks perceived as high-risk This phenomenon highlights the inverse relationship between affect heuristic and decision-making processes, as individuals rely on preconceived images and symbols to assess risks and benefits in the stock market Additionally, some investors lack the necessary financial literacy and education, which prevents them from conducting thorough financial analyses; instead, they resort to heuristics to simplify their decision-making process (Hassan, 2013).

In summary, the theory is properly applied to explore possible effects of heuristic on the individual investor’s judgments and investment decisions.

Heuristic of individual investors

Heuristics are mental shortcuts that individuals use to make quick judgments and simplify complex problems, often relying on trial and error to develop rules of thumb for investment decisions (Shikuku, 2010) While these strategies can sometimes lead to favorable outcomes, they may also result in poor decisions (Chandra, 2008) One common heuristic is assessing the likelihood of an event based on how easily examples come to mind, highlighting the dual nature of these decision-making tools.

(2000), the affect heuristic refers to the way in which subjective impressions of

“goodness” or “badness” can act as a heuristic capable of producing fast perceptual judgments and also systematic biases For

Research by Ganzach (2001) indicates that individuals assess stocks based on their perceptions, categorizing "good" stocks as low-risk with high returns, while "bad" stocks are seen as high-risk with low returns For unfamiliar stocks, there is a negative correlation between perceived risk and return, aligning with the affect heuristic Conversely, for familiar stocks, this relationship is positive, where higher perceived risks are associated with expectations of greater returns, consistent with traditional economic theory.

Numerous studies have explored heuristics, notably Tversky and Kahneman (1974) who identified three key types: representativeness, availability, and anchoring and adjustment Later research, including work by Hassan et al (2013) and Bramson (2007), expanded on these concepts Tversky and Kahneman (1982a) highlighted the significance of these heuristics in decision-making, while Later et al (2002) introduced the affect heuristic, emphasizing the role of emotional factors Additionally, Gilovich and Griffin (2002) identified six general-purpose heuristics: affect, availability, causality, fluency, similarity, and surprise, further enriching the understanding of human judgment and decision-making processes.

Kahneman and Tversky (1974) were pioneers in the study of heuristics, identifying key factors such as representativeness, availability bias, and anchoring Additionally, Waweru et al (2008) contributed to heuristic theory by introducing Gambler’s fallacy and Overconfidence Schwartz (1998) noted that while there is substantial evidence supporting general heuristics like representativeness, availability, anchoring and adjustment, and affect, there is significantly less research on the specific heuristics applied in decision-making processes.

The rapid dissemination of information has complicated decision-making for financial market participants (Shikuku, 2010) This complexity often leads to an increased reliance on heuristics, which, while sometimes unavoidable, may not always yield positive outcomes (Fromlet, 2001) Heuristic decision-making rules are frequently necessary for interpreting new information (Finucane et al., 2002; Chandra, 2008).

1 9 studied behavioral factors and their impacts on investors’ attitude towards risk and behavioral

The study on decision-making processes reveals that individual investors are significantly affected by cognitive biases, including representativeness, overconfidence, and anchoring Additionally, factors such as cognitive dissonance, greed, fear, regret aversion, and mental accounting shape their perception of risk, ultimately impacting their investment decisions.

Due to the constraint time, this research focuses only on analyzing three of mainly heuristic factors which including representativeness, availability and overconfidence.

Representativeness refers to the way people make subjective probability judgments based on similarity to stereotypes (Barker & Nofsinger, 2012, p 259). However, recognizing the representativeness heuristic is easier than defining it Gilovich

Representativeness is a cognitive heuristic where individuals evaluate the similarity of outcomes, instances, and categories based on noticeable and often superficial characteristics, leading them to make judgments based on these assessments This tendency to equate similar features often results in errors in probability judgments, as it overlooks several important influencing factors (Barker & Nofsinger, 2012).

259) Representativeness may result in some biases such as people put too much weight on recent experience and ignore the average long-term rate (Ritter, 2003, p.432).

Availability is a heuristic that influences how we assess information based on its ease of recall rather than its actual probability or frequency This concept can stem from recent dramatic news events and can be categorized as experience-based, memory-based, or imagination-based, as noted by Warneryd (2001) However, there is no consensus on the varying degrees of availability or the appropriate weight to assign to these differences An example of the significance of availability is seen in the behavior of a successful mutual fund manager, who often avoids stocks that are widely favored by analysts and other managers.

21 celebrating because he was convinced that such “availability” increased the likelihood that the shares of those companies were

Investors have historically favored national stocks over international ones, especially until the mid-1990s, often overlooking lucrative opportunities abroad This tendency is likely influenced by the availability heuristic, which leads to a significant bias due to its insensitivity to sample size Consequently, the information that investors prioritize may stem from a limited and potentially unrepresentative sample.

Overconfidence is a widespread issue that can lead to significant negative outcomes, including wars, strikes, legal disputes, business failures, and stock market bubbles (Camerer & Lovallo, 1999; Glaser & Weber, 2007) As noted by Plous (1993), overconfidence is one of the most common and potentially disastrous problems in judgment and decision-making This phenomenon occurs when individuals overestimate the reliability of their knowledge and skills, highlighting the dangers of misplaced confidence (DeBondt & Thaler, 1995; Hvide, 2002).

Overconfidence is widely regarded as a significant judgment bias, as highlighted by Barker & Nofsinger (2012) Research indicates that this bias can lead to poor decision-making among investors, managers, and politicians, particularly in areas where individuals feel knowledgeable (Evans, 2006, p.20) Despite its drawbacks, overconfidence may also foster persistence, determination, and mental agility, as well as increase risk tolerance These traits can enhance professional performance and positively influence how others perceive one's abilities, potentially resulting in quicker promotions and extended investment horizons (Oberlechner).

Decision making and performance of individual investor

Making informed investment decisions in the stock market is increasingly challenging, requiring a deep understanding and insight Psychological and behavioral factors significantly influence these decisions, highlighting the importance of awareness in the investment process (Evans, 2006; Waweru et al.).

Traditional finance assumes that investors act rationally, whereas behavioral finance posits that stock market investors often behave irrationally When making market decisions, investors process available information, but their emotions, psychological factors, and behavioral biases can result in systematic errors in this information processing.

Investment decisions are characterized by complexity and uncertainty, as highlighted by Macmillan (2000) The complexity arises from the multitude of alternative actions available to decision-makers, while uncertainty is a constant factor, especially in investments where decisions can significantly impact an organization Investors often juggle multiple objectives, necessitating trade-offs between expected returns and associated risks Dean and Sharfman (1996) emphasize that the interplay of these factors does not negate the importance of choice, as it is improbable for all potential options to yield equal success or failure.

Barber and Odean (2008) highlight the significant impact of attention on individual investor purchase decisions Investors often encounter a substantial search problem when selecting stocks, leading many to focus on those that capture their attention, such as trending news stocks or those with notable price fluctuations Consequently, individual investors tend to invest heavily in these attention-grabbing stocks In contrast, since most individual investors typically hold a limited number of stocks and primarily sell those they already own, the selling process presents a lesser search challenge and is less influenced by attention factors.

Personal rate of return reflects an individual's investment performance based on their transaction history and cash flows Research by Schlarbaum, Lewellen, and Lease (1978a) examines the overall performance of common stocks held by investors at a full-service brokerage Further studies by Odean (1999) and Schlarbaum et al (1978b) focus on the profitability of individual stock trades Lin and Swanson (2003) evaluate investment performance through three return criteria—raw, risk-adjusted, and momentum-adjusted—across five time horizons They highlight that while investors can achieve strong short-term performance, it is often influenced by short-term price momentum rather than just risk-taking.

Research by Barber and Odean (2001) reveals that men exhibit higher levels of overconfidence compared to women, particularly in traditionally male-dominated areas, leading to excessive trading that negatively impacts their investment performance Specifically, men have an annual turnover rate of approximately 80%, while women’s is around 50% This excessive trading behavior results in poorer returns for men, as both genders experience low returns, but men’s aggressive trading strategies and associated costs further diminish their performance Ultimately, the lack of stock selection ability is evident in both genders, as their gross returns from trades are similar, highlighting that men’s trading frequency is the primary factor contributing to their underperformance.

Various methods exist to assess investment performance, with many researchers relying on secondary data from investors' outcomes in the securities markets, as noted by Lin et al (2003) In contrast, some studies, including those by Oberlechner & Osler (2004) and Le & Doan (2011), utilize primary data gathered through interviews to evaluate investment performance.

Hypothesis Development

2.4.1 Representativeness and investors’ decision making.

A typical example for representativeness bias is that investors often infer a company’s high long-term growth rate after some quarters of increasing (Waweru et al.,

2008, p.27) Representativeness also leads to the so-called “sample size neglect” which occurs when people try to infer from too few samples (Barberis & Thaler, 2003, p.1065).

In the stock market, investors often gravitate towards "hot" stocks, reflecting the representativeness heuristic, which can lead to overreaction (DeBondt and Thaler, 1995, p.390) This tendency arises from the perception that successful stocks belong to strong companies, creating a false sense of safety However, this belief contradicts traditional finance theory, which asserts that there is a positive correlation between risk and return.

In the stock market, investors often categorize certain stocks as growth stocks due to their consistent earnings history and growth potential However, it is important to recognize that only a limited number of companies are likely to maintain this growth trajectory over time (Finucane et al 2002).

In their 2011 study, researchers highlighted that making numerical predictions about stock values often relies on company descriptions while neglecting their reliability, leading to an overreliance on stereotypes and a failure to adequately consider base rate information Kahneman and Tversky (1974) demonstrated that individuals tend to classify events based on familiar categories, which skews their probability estimates by placing undue emphasis on these classifications and overlooking the actual underlying probabilities.

Representativeness bias plays a significant role in investor behavior, leading many to assume that past price trends will continue, with rising prices perceived as indicative of future gains and falling prices suggesting ongoing losses According to DeBondt & Thaler (1985), this cognitive bias causes investors to become overly optimistic about previous winners while being excessively pessimistic about past losers Consequently, trading influenced by representativeness can distort share prices, preventing them from accurately reflecting all pertinent information.

Behavioral finance has been explored in Vietnam for some time, yet consistent empirical evidence regarding the representativeness bias in investor decision-making remains elusive Therefore, we propose the following hypothesis:

Hypothesis 1: Representativeness has positive impact on the individual investors’ decision-making in Vietnam.

2.4.2 Availability bias and investors’ decision-making

Availability bias, as noted by Shefrin (2002), influences investors' decision-making by causing them to rely heavily on readily available information while neglecting less accessible data This tendency leads investors to prioritize stocks of well-publicized companies, often believing they perform better than less-visible counterparts, despite the possibility that these high-profile firms may have inferior earnings and return potential.

Recent information, particularly from media and corporate announcements, is often top of mind for individuals, influenced by their brokers' or advisors' recommendations However, research by Barber and Odean (2008) indicates that stocks receiving extensive press coverage tend to underperform in the two years following the news.

The availability heuristic leads investors to assess probabilities based on how easily they can recall similar instances, which can create biases when this ease does not align with actual frequency Research by Klibanoff, Lamont, & Wizman (1998) highlights that significant country-specific news can significantly impact the prices of closed-end country funds, causing them to underreact to fundamental changes in a typical week However, when news about a specific country is prominently featured, such as on the front page of the Saigon Times, the market responds much more vigorously to these developments.

Behavioral finance has been recognized in Vietnam for some time, yet empirical studies examining the impact of availability bias on investor decision-making remain lacking Therefore, this article proposes a hypothesis to explore this critical aspect of investor behavior.

Hypothesis 2: Availability has positive impact on the individual investors’ decision- making in Vietnam

2.4.3 Overconfidence and investors’ decision making

Overconfidence models indicate that the presence of overconfident traders leads to increased trading volume in financial markets Individual investors exhibiting higher levels of overconfidence tend to engage in more aggressive trading, resulting in a direct correlation between overconfidence and trading volume Odean identified this phenomenon as “the most robust effect of overconfidence.” Additionally, DeBondt and Thaler highlighted that the elevated trading volume in financial markets poses a significant challenge to traditional finance theories, emphasizing that overconfidence is a crucial behavioral factor in unraveling this trading conundrum.

This research examines the influence of overconfidence on the investment decision-making processes and performance of individual investors at the Ho Chi Minh Stock Exchange.

Hypothesis 3: Overconfidence factor has positive impact on the individual investors’ decision-making in Vietnam

2.4.4 Investment decision making and investment performance

Investor behavior theories, such as those addressing under- and overreaction to news, are grounded in psychological research and concepts like overconfidence and bounded rationality These theories aim to explain return patterns, including long-horizon reversals However, a more comprehensive understanding of actual investor behavior and the variability in individual reactions to the same information is needed to enhance this line of research (Grinblatt and Keloharju, 2000).

Recent studies have revealed notable patterns in the return-based behavior of investors Brennan and Cao (1997) proposed a theoretical model and provided empirical evidence suggesting that foreign investors, due to their lack of information compared to domestic investors, are likely to adopt momentum strategies but ultimately experience inferior performance Additionally, research by Froot et al (2000) and Choe et al (1999) indicates that foreign investors typically engage in momentum investing, with Choe et al specifically examining short-term past-return horizons.

Grinblatt and Keloharju (2000) highlight that due to data limitations, a comprehensive analysis of the investment behavior and performance across all investor categories has not been feasible The challenges arise from varying research methodologies, data frequencies, historical return horizons, and distinct institutional frameworks.

32 blur the comparison of the results and make it difficult to identify general patterns behind the behavior and performance of isolated investor categories.

Investment performance Investment decision making

Research by Anderson, Henker, and Owen (2005) indicates that individual investors who engage in more transactions tend to achieve higher returns compared to those with fewer trades Additionally, Kim and Nofsinger (2003) discovered that stocks with significant increases in individual ownership often yield negative abnormal returns over the year, while those with declines in ownership can produce positive abnormal returns Their study delves into buying and selling behaviors, revealing that stocks purchased during periods of increased individual ownership are typically past winners.

Such results led Papadakis (1998) to hypotheses that performance is positively related to comprehensiveness/rationality and formalization in the investment decision- making process.

Conceptual model

Based on the above arguments, the thesis suggests the research model as follow:

Accordingly, four following hypotheses are suggested:

Hypothesis H1: Representativeness has positive impact on the individual investors’ decision-making in Vietnam.

Hypothesis H2: Availability has positive impact on the individual investors’ decision-making in Vietnam.

Hypothesis H3: Overconfidence factor has positive impact on the individual investors’ decision-making in Vietnam.

Hypothesis H4: The individual investors’ decision-making positive impact on their investment performance in Vietnam.

Chapter summary

In conclusion, the literature review highlights the significant influence of heuristic factors on the investment decisions and performance of individual investors in financial markets, particularly in stock markets This chapter provides an in-depth analysis of the theoretical frameworks, including prospect theory and heuristics theory Additionally, the thesis examines the definition and classification of heuristics used by individual investors Crucially, it investigates the relationship between decision-making processes and the performance of individual investors in the Ho Chi Minh stock market, while also formulating hypotheses and constructing a conceptual model.

RESEARCH METHODOLOGY

Research process

The development and testing of a theory typically involve two primary approaches: induction and deduction The deductive approach begins with an established theory and explores the logical relationships between concepts to gather empirical evidence Conversely, the inductive approach focuses on deriving a theory from observations of empirical reality, allowing researchers to draw inferences that inform the original theory.

This study aims to explore the heuristic factors that influence investor performance and decision-making by employing a deductive approach, rather than building new theories It begins with a review of behavioral finance theories, particularly in the stock market, to establish a theoretical and empirical foundation for the research model and hypotheses Subsequently, interview and questionnaire questions are developed, aligning with the deductive approach that posits researchers can understand how the world operates and test these ideas against empirical data (Neuman & Kreuger, 2003, p.53) The hypotheses are then tested through data collection and analysis, allowing for a comparison of research results with existing theories to identify differences This deductive approach is typically linked to quantitative research, which involves the collection and statistical analysis of quantitative or quantifiable qualitative data, making it suitable for this study's objectives.

Research problem Research objective Research scope

Sampling design Refine measures & measurement scale Refine questionnaire

The research problem was clearly defined, followed by the identification of specific research objectives and questions aimed at addressing this issue A literature review was conducted to explore relevant theories regarding heuristic factors that influence decision-making and the performance of individual investors, with a focus on finding an appropriate model for the Vietnamese stock market This led to the formulation of hypotheses for the study Subsequently, a preliminary questionnaire was developed based on questions from previous studies, setting the stage for the research design, which includes two sub-steps.

A pilot study was conducted involving face-to-face interviews with two managers from a securities company to evaluate the content, quantity, and structure of questions in a preliminary survey This was aimed at testing the survey's effectiveness prior to the main launch Additionally, a draft survey was administered to 94 investors to assess reliability and perform exploratory factor analysis (EFA) for the research.

A comprehensive survey was conducted via mail, social networks, and direct distribution of hard copies to individual investors, facilitated by securities company brokers Data collection was completed one month after the survey distribution.

The collected data underwent a cleaning process before being utilized to assess the reliability of the scale and the validity of the questionnaire, employing Cronbach’s alpha coefficient and Exploratory Factor Analysis (EFA) To evaluate the hypotheses, the multiple regression method was applied, and the implications and findings were documented and reported.

Research design

The questionnaire is divided into four parts: personal information, heuristic factors influencing investment decisions, decision making and investment performance.

In the part of personal information, nominal measurements are used Nominal scales are used to classify objects.

This research explores behavioral finance theories, including heuristic and prospect theories, as well as the effects of behavioral factors on investor decision-making, as discussed by Waweru et al (2008, pp 24-38) and other authors The study aims to synthesize key questions regarding how heuristic factors influence investment choices.

The study examines 38 investment decisions and their performance, utilizing a 5-point Likert scale to gauge individual investors' opinions on the influence of heuristic factors on their investment choices and performance outcomes Respondents rate their agreement on a scale from 1 (extremely disagree) to 5 (extremely agree) Detailed measurements and questions related to these evaluations are outlined in tables 3.1, 3.2, and 3.3.

Previous research has highlighted the influence of behavioral finance on investment decision-making, focusing on three common heuristics: representativeness, availability, and overconfidence Specifically, representativeness was assessed through three observed variables established by DeBondt & Thaler (1995), utilizing a five-point Likert scale and further modified by other researchers Our analysis primarily draws on the studies conducted by Kengatharan.

The research conducted by Hassan et al (2013) and Le & Doan (2011) identifies three observed variables to measure availability factors Additionally, the primary source of overconfidence bias is derived from the work of Kengatharan.

(2013), research of Luu (2014) and Qureshi (2012) with two observed variables.

Table 3.1 Types of measurement for heuristic variables influencing investment decision making

Investors buy "hot" stocks and avoid stock that have performed poorly in the recent past?

Investors forecast the changes in stock prices in the future based on the recent stock prices?

RE3 Investors use trend analysis of some representative stocks to make investment

Variables Items Description Sources decision for all stocks that they invest?

Investors rely on their previous experiences in the market for their next investment?

In investors’ opinion it is safe to invest in local stocks rather than to buy international stocks

Investors consider the information from their close friends and relatives as the reliable reference for their investment decisions?

Investors believe that their skills and knowledge of stock market can help they to outperform the market?

OVER2 Investors use predictive skills for investment decision making

3.2.2 Measure of investors’ decision making

Individual investors' decision-making is influenced by various factors, but this study focuses specifically on their perspectives regarding investments The research evaluates investors' decision-making through three observed variables, as established by Hassan et al (2013) and Qureshi (2012), utilizing a five-point Likert scale for measurement.

Table 3.2 Types of measurement for individual investors’ decision making

Investors’ investment has a lower risk compared to the market in general

DEC2 Investors’ investment in stocks has high degree of safety

DEC3 Investors’ investment has the ability to meet interest payment

This research diverges from previous studies that primarily utilized secondary data on investors' results in security markets to assess stock investment performance (Lin and Swanson, 2003; Kim and Nofsinger, 2003) Instead, it prompts investors to evaluate their own performance, drawing on the investment return rate methodology established by Oberlechner and Osler (2004) The evaluation encompasses both objective and subjective perspectives: investors compare their actual return rates to their expected rates for the subjective assessment, while the objective evaluation involves comparing real return rates to the average market return Additionally, the study introduces the satisfaction level of investment decisions as a key criterion for measuring investment performance Notably, some investors report satisfaction with their performance despite low profits, whereas others remain dissatisfied even with relatively high returns Thus, this research proposes that both the satisfaction level of investment decisions and the investment return rate serve as comprehensive measures of investment performance.

Investors’ performance was measured by three observed variables, developed byKengatharan (2013), used a five-point Likert scale, and modified by the author as follow:

Table 3.3 Type of measure for individual investors’ performance

Investors feel satisfied with their investment decisions in the last year (including selling, buying, choosing stocks and deciding stock volume)

PER2 The return rate of the investors’ recent stock investment meets their expectation?

PER3 Investors’ investment in stocks has demonstrated increased revenue growth in last year

Pilot test

To develop a draft questionnaire for a pilot test, the author conducted interviews with two managers from the Ho Chi Minh Stock Exchange (HOSE) to gain insights into the financial behaviors of Vietnamese individual investors The selection of interviewees was based on convenience sampling due to the managers' limited availability Given their roles in trading surveillance and market information, these managers possess extensive knowledge of the stock market and investor behaviors, making them qualified sources for significant analysis and discussion on the topic.

Before conducting the main survey, a draft survey will be collected to ensure measurement reliability The internal consistency of the measurements will be evaluated using Cronbach’s Alpha Test, with a recommended threshold of 0.7 for reliability, although a score above 0.6 is also deemed acceptable Additionally, it is important to consider corrected item-total correlations, which should be at least 0.3 to indicate a strong relationship among the items This research adopts a Cronbach’s alpha of 0.6 or higher and a corrected item-total correlation of 0.3 or more, as the financial behavior measurements are new to stockholders of the Ho Chi Minh Stock Exchange Furthermore, the acceptable significance level for the F-test in this context is set at 0.05, and the analysis will be conducted using SPSS software.

Then, EFA is used to explore the factors that the heuristic variables, investment decision making and investment performance of the questionnaire (question 13 to question

26) belong to EFA is used to reduce the number of items in the questionnaire that do not meet the criteria of the analysis (O’brien, 2007, p.142) In this case, EFA is utilized to test the hypotheses shown in the research model of Chapter 2.

In this research, the following criteria of the exploratory factor analysis are applied: Factor loadings, KMO, Total variance explained, and Eigen value.

In a pilot study involving 94 samples, the measurement scales demonstrated Cronbach’s alpha coefficients exceeding 0.6, indicating strong reliability Although the Cronbach’s alpha for availability bias was 0.566, which is below 0.6 but above 0.5, its Corrected Item Total Correlation values were all above 0.3, allowing it to be included in the subsequent exploratory factor analysis Overall, the scales developed in this research exhibit statistical significance and adequate reliability.

Through factor analysis using the Varimax method, four key factors have emerged: representativeness bias, overconfidence bias, decision-making, and the performance of individual investors The variable representing availability (AVA2) was excluded due to its double loading, while AVA1 and AVA3 were integrated into the representativeness and overconfidence components Consequently, AVA1 has been renamed to RE4 and AVA3 to OVER3 This leads to a proposed new model suggesting that representativeness and overconfidence biases significantly influence the decision-making processes and performance outcomes of individual investors.

Figure 3.3 New Research Model (revised) Representativeness

Main s urv e y

To effectively explore the heuristic factors at the HOSE, a larger sample size is essential for obtaining reliable results, as a more representative sample enhances the validity of the findings (Saunders et al., 2009, p.219) However, the feasible sample size is influenced by the researchers' available resources, including time, finances, and personnel (Saunders et al., 2009, p.212) According to Hair et al (1998, p.111), a minimum of 100 respondents is recommended for quantitative research to ensure the statistical methods of data analysis are appropriately applied.

Questionnaires were distributed to participants using stratified random sampling to ensure a representative sample of individual investors' financial behaviors Initially, convenience sampling was employed for its ease and efficiency in gathering responses from friends and relatives, but it is a non-probability method that limits the generalizability of results (Bryman & Bell, 2007, p.198) In contrast, stratified random sampling segments the population based on specific criteria, such as brokerage market share, allowing for more accurate and reliable data collection from each subgroup.

Stratified sampling is utilized to ensure that the sample mirrors the population distribution (Bryman & Bell, 2007, p.187) Questionnaires were distributed to brokers from selected companies, who randomly assist their investors Due to time limitations, the focus was narrowed to individual investors from ten leading securities firms For a complete list of these firms, please refer to Appendix 2.

The study utilized a non-probability convenience sampling method To assess the reliability of the measurement scale, Cronbach’s Alpha was employed, while Exploratory Factor Analysis (EFA) was used to test factor loading Hypotheses were evaluated using Multiple Regression According to Hair et al (1998), the data set must meet the requirement for EFA, which stipulates a minimum sample size of five times the number of variables, with a total sample size exceeding 100 Consequently, this study required a minimum sample size of n > 100, given that it involved five variables.

The minimum sample for multiple regression analysis must ensure the formula of n

> 50 + 8m (m: number of independent variables) (Tabachnick and Fidell, 1996)

As there were three independent variables in this study, the minimum sample required to run multiple regression in this research was n > 74

As a result, the minimum sample size in this study was over 100 which would be satisfied both EFA and multiple regression analysis.

As mentioned in pilot test, this research use Cronbach’s alpha to examine the reliability of variables in the questionnaire through following coefficients:

Cronbach’s alpha coefficient: the scale is reliable when this coefficient is 0.6

Corrected Item – Total correlation: variables are acceptable when this coefficient is 1.3 or more.

In the pilot test, the study utilized the Exploratory Factor Analysis (EFA) method with Varimax rotation to assess the heuristic factors affecting decision-making and individual performance Additionally, the Kaiser-Meyer-Olkin (KMO) test and Bartlett's test were employed to evaluate the suitability of the sample for analysis.

The study utilized Multiple Regression Analysis to evaluate its research hypotheses, adhering to the assumptions outlined by Leech et al (2005) Key assumptions include the linear relationship between predictor variables and the dependent variable, as well as the normal distribution and lack of correlation of residuals with predictors A significant concern is multicollinearity, which arises from high intercorrelations among predictor variables, potentially leading to misleading or inaccurate results due to overlapping information among predictors.

A correlation matrix can help identify potential multicollinearity issues among predictor variables, but it may not always reveal the existence of this condition Multicollinearity can arise when multiple predictors are collectively related to other predictors Therefore, it is crucial to test for multicollinearity when conducting multiple regression analysis, which can be performed using SPSS 16.0.

In this study, there are two models of multiple regression as follows:

 The first multiple regression model: Y’ = β1X1 + … + βnXn + ε

- Y: Investment decision making variable (dependent variable)

- X: Heuristic factors (independent variables included representativeness, availability and overconfidence variables)

 The second multiple regression model: Y’’ = β1Y’1 + ε

- Y’’: Investment performance variable (dependent variable)

- Y’: Investment decision making variable (independent variable)

Chapter summary

The collected data underwent a systematic analysis, beginning with a summary of the respondents' demographic profiles Next, the reliability of the measurement items was confirmed using Cronbach’s alpha Following this, the Promax method was employed to determine the correlation between the independent and dependent variables Finally, standard multiple regression analysis was conducted to assess the statistical significance of the model and evaluate the predictive power of each independent variable in relation to the two dependent variables: investment decision-making and investment performance.

FINDINGS AND DISCUSSIONS

Data background

A survey conducted at the Ho Chi Minh Stock Exchange yielded 186 responses from 400 delivered questionnaires, resulting in a 47% response rate, which is considered moderately high for postal surveys The data was collected from securities companies in Ho Chi Minh City, and the sample characteristics include gender, age, marital status, education, years of work experience, income, duration of stock market participation, securities company affiliation, stock course completion, course duration, total investment in the stock market, and total investment from the previous year These demographic statistics are summarized in Table 4.1.

Table 4.1 Descriptive statistic of respondent’s characteristics

Years of working Under 5 years 96 52%

Years of attendant the stock market

Securities company Saigon Securities Incorporation 36 17%

Course of stock market Yes 124 67%

Total amount of money invested in the stock market (USD)

Total amount of money invested in the stock market last year (USD) under 10,000 93 50%

The data analysis comprises a sample size of 186 participants, evenly split between males and females The majority of respondents are aged between 18 and 35 years, representing 34% of the sample Over 55% of participants are single, while married individuals account for 36% and divorced individuals make up 9% In terms of education, 73% hold a bachelor's degree, and 52% of investors have less than five years of work experience Income levels show that 34% of investors earn between 300 USD and 600 USD Most individual investors have been involved in the stock market for 1 to 3 years (28%) and primarily hold accounts with Hochiminh City Securities Corporation (36%) Additionally, 67% of investors have completed a stock market course, with 65% attending a three-month program Notably, 46% of respondents have invested less than 10,000 USD since entering the stock market, and 50% have done so in the past year.

Measurement Reliability using Cronbach’s Alpha

To determine the reliability of measurement scales and assess the cohesion of their items, Cronbach’s alpha coefficient is utilized This statistic evaluates the internal consistency reliability, ensuring that the scale's items effectively measure the same underlying construct (Pallant, 2001) For a scale to be deemed reliable, its Cronbach’s alpha value must exceed 0.6 (George & Mallery, 2003).

Cronbach's alpha is a key indicator of the reliability of items measuring a specific construct, with values closer to 1.0 being preferred A score of 0.9 or above signifies high reliability, while a value below 0.5 indicates that the scale is unreliable and unsuitable for measurement.

This section utilizes Cronbach’s alpha to assess the reliability of items identified in the factor analysis, ensuring that the measurements are dependable for future applications.

Table 4.2 Reliability analysis for each factor

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Cronbach's Alpha if Item Deleted

The Cronbach’s alpha for representativeness (RE) is 0.759, exceeding the acceptable threshold of 0.6 Additionally, the corrected item-total correlations for the individual items are as follows: representativeness 1 (RE1) at 0.703, representativeness 2 (RE2) at 0.655, representativeness 3 (RE3) at 0.711, and representativeness 4 (RE4) at 0.735, all of which are above the minimum requirement of 0.3 Consequently, this variable is deemed suitable for Exploratory Factor Analysis.

The Cronbach’s alpha for overconfidence (OVER) is 0.608, exceeding the 0.6 threshold, with corrected item-total correlations of OVER1 = 0.424, OVER2 = 0.383, and OVER3 = 0.692, all above the acceptable level of 0.3 Consequently, this variable is suitable for Exploratory Factor Analysis Similarly, the Cronbach’s alpha for investment decision making (DEC) is 0.753, also surpassing 0.6, with corrected item-total correlations of DEC1 = 0.629, DEC2 = 0.671, and DEC3 = 0.740, each greater than 0.3 Thus, this variable is also deemed appropriate for Exploratory Factor Analysis.

The Cronbach’s Alpha for investment performance (PER) is 0.840, exceeding the acceptable threshold of 0.6, indicating strong internal consistency The corrected item-total correlations for investment performance measures are as follows: PER1 = 0.813, PER2 = 0.716, and PER3 = 0.797, all of which are above the minimum acceptable value of 0.3 Consequently, this variable is deemed suitable for Exploratory Factor Analysis, leading to the retention of five variables.

Scale testing by using Exploratory Factor Analysis (EFA)

The measurement model was evaluated through item-to-total correlations, with a threshold of greater than 0.5, alongside an assessment of convergent and discriminant validity for the instrument items Convergent validity is established when items exhibit high loadings, exceeding 0.5, on their respective factors.

The analysis of the dependent and independent variables was conducted separately for each factor Principal axis factoring was utilized to extract all variables, employing the Varimax rotation method The factor analysis results indicated that all remaining variables exhibited significant loadings greater than 0.5 on an acceptable factor.

Using the Varimax method for factor analysis on both independent and dependent variables, four key factors emerged: representativeness, overconfidence, investment decision-making, and investment performance.

According to the result of analysis shows in Rotated Component Matrix (a), 13 factors have loading factor bigger than 0.5 that meets the requirement.

The testing of reliability and Exploratory Factor Analysis (EFA) confirms the validity of two independent variables—representativeness, comprising RE1, RE2, RE3, and RE4, and overconfidence, including OVER1, OVER2, and OVER3 Additionally, two dependent variables, investment decision making (DEC1, DEC2, DEC3) and investment performance (PER1, PER2, PER3), are deemed acceptable and possess practical value.

Test hypotheses by multiple regressions

This section employs two linear regressions to analyze the relationship between the dependent variable, investment decision-making, and the independent variables of representativeness and overconfidence The analysis includes two distinct models to evaluate these variables effectively.

Model 1: representativeness and overconfidence that influencing on investment decision making

Model 2: investment decision making impact on investment performance of individual investors.

4.4.1 Regression analysis of variables representativeness, overconfidence and investment decision making (Model 1)

Table 4.4 Correlation among factors Correlations

* Correlation is significant at the 0.05 level (2-tailed).

** Correlation is significant at the 0.01 level (2-tailed).

Table 4.4 reveals a low correlation between Representative bias (RE) and Investment decision making (DEC), indicated by a significance level of 0.238, which is greater than the 0.05 threshold In contrast, there is a strong correlation between Overconfidence bias (OVER) and DEC, with a significance level of 0.000, demonstrating a significant relationship below the 0.05 mark.

Result of assumption of regression

The analysis of the Coefficients table reveals that multicollinearity is not present, as all variables exhibit a Variance Inflation Factor (VIF) of less than 10, with the VIF for RE and OVER specifically recorded at 1.025.

• The samples get the standardized normal distribution, because mean value is nearly zero, and standard deviation is approximately one (Std Dev = 0.995) (see appendix 7)

The Enter method was utilized to simultaneously input all variables for selection based on criteria with a significance level of less than 0.05 The results of the regression analysis are as follows:

Std Error of the Estimate

1 350 a 122 113 697 122 12.750 2 183 000 1.974 a Predictors: (Constant), OVER, RE b Dependent Variable: DEC

Table 4.5 presents the R Square and Adjusted R Square values, which indicate the variance in the dependent variable, investment decision making, explained by the model A high R Square value suggests strong model success; however, it may overestimate real-world applicability The Adjusted R Square offers a more accurate measure, revealing that the model accounts for 11.3% of the variance in investment decisions influenced by representativeness and overconfidence biases With an R Square value of 0.122 and a lower Adjusted R Square, this analysis emphasizes the model's relevance while ensuring a more realistic assessment of its effectiveness.

Table 4.6 - ANOVA ANOVA b a Predictors: (Constant), OVER, RE b Dependent Variable: DEC

The ANOVA analysis yielded an F value of 12.750 with a significance level of 0.00, indicating strong statistical significance Additionally, the Tolerance and VIF values in the Coefficients table confirm the absence of multicollinearity, as all variables have a VIF below 10, with the VIF for RE and OVER both recorded at 1.025.

Squares df Mean Square F Sig.

Table 4.7 Regression analysis of variables Coefficients a a Dependent Variable: DEC

Table 4.7 displays the Sig and Standardized Beta Coefficient values, highlighting the unique contribution of each independent variable to the model while controlling for other predictors A high coefficient indicates a significant impact on the model, with the standardized Beta coefficient for overconfidence bias (OVER) at 0.343 and a significance value of 000 The t and Sig (p) values demonstrate the statistical significance of each independent variable in predicting the dependent variable Specifically, a large absolute t value and a small p value (p < 05) confirm that overconfidence bias significantly influences individual investors' investment decision-making, thereby supporting hypothesis H3.

The results indicated that the RE variable had a standardized Beta of 0.033 and a significance value of 0.635, which is greater than the 0.05 threshold This suggests that, at a 95% confidence level, RE does not significantly contribute to predicting the dependent variable DEC (investment decision making), leading to the conclusion that the hypotheses H1 were not supported.

The significance of the regression coefficients:

The coefficients in the model use to test how importance of independent variables in impacting dependent variable Through the Beta coefficient in the regression analysis as the

63 results presented in table 5.1 below, the level of importance of each factor affecting on investment decision making is shown.

Results of regression analysis show that OVER (overconfidence) has the strongest effect on DEC (investment decision making) with Standardized Coefficients β = 0.343, the second is RE with Standardized Coefficients β = 0.033.

The regression analysis results indicate a strong correlation and statistical significance for both OVER and the variable in question, with significance values of Sig = 0.000, which is less than the 0.05 threshold Conversely, RE shows a weak correlation and lacks statistical significance, as evidenced by its Sig value of 0.635, which exceeds the 0.01 level.

The multiple regression analysis reveals that only overconfidence bias significantly influences the investment decision-making of individual investors, accounting for 34.3% of the variance in the dependent variable Consequently, hypothesis H3 is accepted.

On the other hand, hypothesis H1 is not supported and thus rejected in this study. Overconfidence bias was found to be significant in explaining the variation in the dependent variable.

4.4.2 Regression analysis of variables investors’ decision making and investors’ performance (Model 2)

** Correlation is significant at the 0.01 level (2-tailed).

The table 4.8 showed that correlation between DEC (Investment decision making) and PER (Investment performance) is high because sig of DEC and PER is 0.000 < 0.05

Result of assumption of regression:

• Tolerance and VIF values in the Coefficients table show that do not exist multicollinearity because VIF of variable are less than 10, and only VIF of RE is 1.000

• The samples get the standardized normal distribution, because mean value is nearly zero, and standard deviation is approximately one (Std Dev = 0.997) (see appendix 8)

The Enter method is employed to simultaneously include all variables for selection based on criteria with a significance level of less than 0.05 The outcomes of the regression analysis are presented below.

1 369 a 136 131 803 136 28.928 1 184 000 1.600 a Predictors: (Constant), DEC b Dependent Variable: PER

Table 4.9 illustrates that all variables, including investment decision-making and investment performance, have been incorporated into the evaluation of the multiple linear regression model The coefficient of determination, R², is employed to assess the model's relevance.

65 research model R2 coefficient when assessing the relevance of the model is 0.136, the result also

The adjusted R-squared value of 0.131 reveals that only 13.1% of the variance in individual investors' investment performance can be predicted by the independent variables in the regression model This highlights the model's relevance, as using the adjusted R-squared provides a more accurate assessment of consistency without overstating the model's explanatory power.

Table 4.10 ANOVA ANOVA b a Predictors: (Constant), DEC b Dependent Variable: PER

The ANOVA analysis reveals an F value of 28.928 with a significance level of 0.00, indicating a strong model fit Additionally, the Coefficients table demonstrates that there is no multicollinearity present, as all Variance Inflation Factor (VIF) values are below 10, with the VIF for the RE variable being 1.000 These findings support the assumptions of multiple regressions.

Table 4.11 Regression analysis of variables Coefficients a a Dependent Variable: PER

Table 4.11 showed that, the standardized coefficient Beta of DEC (investment decision making) and PER (investment performance) at the level of 95% confidence The

Model Sum of Squares df

B Std Error Beta Tolerance VIF

.429 080 369 5.378 000 1.000 1.000 standardized coefficient Beta DEC was provided 0.369 and with the sig value of 000 Thus, the hypotheses H4 were supported.

The significance of the regression coefficients:

The coefficients in the model assess the significance of independent variables on the dependent variable As illustrated by the Beta coefficients in the regression analysis presented in Table 5.1, the importance of each factor influencing investment performance is clearly demonstrated.

Results of regression analysis show that DEC has the strong effect on PER with Standardized Coefficients β = 0.369

Results of regression analysis also show that has Sig = 0.000 < 0.05, and DEC has Sig 0.000 < 0.05 Therefore, DEC have strong correlation and statistics significance when included in model analysis.

Chapter summary

This chapter detailed the analysis of measurement scales, the research model, and hypotheses Initially, Cronbach’s alpha and exploratory factor analysis (EFA) were employed to refine the measurement scales for alignment with market data Subsequently, the structural equation model was utilized to evaluate the research model The findings indicated that overconfidence bias significantly influenced both investment decision-making and performance, while the representativeness and availability variables showed no effect Notably, hypothesis 1, which proposed that representativeness affects investors' decision-making, was not supported The upcoming chapter will summarize the discussions, conclusions, implications, and limitations of this study.

CONCLUSION AND RECOMMENDATION

Conclusions

The descriptive analysis of qualitative constructs provides a comprehensive overview of the research sample, highlighting key demographics such as gender, age, educational level, and marital status Additionally, the analysis identifies both independent and dependent factors that influence individual investors in Ho Chi Minh City, shedding light on their perceptions of elements that impact investment decision-making and overall investment performance.

The study addresses two key questions posed in Chapter 1, providing clear conclusions that summarize the main findings essential for answering the research inquiries.

• Question 1: Do heuristic factors influence individual investors’ decisions in the

The reliability analysis using Cronbach Alpha coefficients indicated high reliability for the research model, with the exception of the item AVA2, which was removed to enhance the reliability of the "Availability bias" factor Exploratory Factor Analysis (EFA) revealed four factors from the initial 14 variables, specifically focusing on representativeness bias and overconfidence bias Subsequently, linear regression analysis was conducted to examine the relationship between independent factors (representativeness bias and overconfidence bias) and the dependent factor (investment decision-making of individual investors) The findings demonstrated that overconfidence bias significantly influences the investment decision-making process of individual investors, thereby supporting hypothesis H3.

This research stands out as one of the few studies examining heuristic factors that influence stock investment decisions through the lens of behavioral finance in Vietnam Unlike previous studies, such as those by Nguyen and Vuong (2006) and Tran (2007), which primarily focused on herding effects, this study broadens the scope by evaluating various heuristic factors affecting Vietnamese individual investors It contributes to the understanding of heuristics in emerging and frontier stock markets The study employs a 5-point measurement scale, with Factor Analysis and Cronbach’s Alpha confirming the consistency and reliability of heuristics as applicable to the Vietnamese stock market.

• Question 2: Does a strong tendency of investment decision making have a positive influence on the investment performance?

The relationship between individual investors' decision-making and their investment performance was analyzed using linear regression The findings indicate that the decision-making processes of investors significantly influence their investment returns.

This research introduces a novel approach to measuring investment performance by asking investors to assess their own outcomes based on investment return rates and satisfaction levels Unlike previous studies by Lin and Swanson (2003) and Kim and Nofsinger (2003), which relied on secondary data from security market results, this study employs a 5-point Likert scale to evaluate the application of heuristics in the Stock Exchange.

With all kinds of sectors which include state sectors and other sectors

Hypothesis 1 (H1): Representativeness has impact on the individual investors’ decision- making at HOSE 0.033 0.598 Not supported

Hypothesis 3 (H3): Overconfidence factor has impact on the individual investors’ decision- making at HOSE 0.343 0.000 Supported

Hypothesis 4 (H4): The individual investors’ decision-making impacts on their investment performance at HOSE 0.369 0.000 Supported

The exploratory factor analysis (EFA) revealed four key loading factors: representativeness bias (RE), overconfidence (OVER), decision making (DEC), and investment performance (PER) Notably, the availability variable was associated with the representativeness factor, leading to the conclusion that hypothesis 2 is excluded from this study.

The multiple regression analysis revealed that overconfidence bias significantly influences investment decision-making among individual investors, accounting for 34.3% of the variance, leading to the acceptance of hypothesis H3 Conversely, hypothesis H1 was rejected, as overconfidence bias was found to be insignificant in explaining variations in the dependent variable Additionally, investment decision-making significantly impacts investment performance, explaining 36.9% of the variance, which supports hypothesis H4 In summary, while overconfidence bias plays a notable role in decision-making, it does not significantly affect investment performance.

Implication of the study

This thesis highlights the significance of individual investors in the Vietnamese stock market, as they represent over 60% of trading activity, according to a 2014 Wall Street Securities report Understanding the impact of heuristics on investment decision-making will enable government officials to effectively monitor and manage stock market operations Additionally, this research can assist in formulating appropriate policies to enhance the stock market in Vietnam.

The research equips securities firms with valuable insights to enhance their predictions of future stock market trends, enabling them to offer more reliable consulting services to a diverse range of individual investors For individual investors, the study highlights the significance of heuristic factors in investment decision-making, empowering them to make more informed choices and improve their overall investment performance.

Investors are advised to exercise caution and avoid overconfidence based on past successes when making investment decisions, as this can lead to biased judgments and poor performance Additionally, it is important for investors not to segregate their investment portfolios into separate accounts, as each component is often interconnected; treating them as independent can negatively impact overall investment outcomes.

Limitation and direction for further research

While the sample size of 188 is adequate for statistical analysis, future research should aim for a larger sample to better represent the Vietnamese stock market Furthermore, although respondents were selected from ten leading securities firms and random sampling was employed, the findings may not fully generalize to the entire population.

Behavioral finance and its measurements are very new to investors in Vietnam.There is very limited number of references for applications of behavioral finance in

Investment performance in Vietnam is often evaluated through the perceptions of investors, but this subjective assessment has its limitations Many investors may lack awareness of their expected return rates or the average stock market returns To improve the accuracy of investment performance measurements, it is essential to combine investor assessments with secondary market data.

This study explores heuristic factors among volunteers in Vietnam using a 5-point Likert scale To validate these findings, additional research is needed with a larger and more diverse sample size.

It is also suggested to conduct the further researches to improve the measurements of heuristics as well as adjust them to fit the case of Vietnam security market.

The further researches are also suggested to apply heuristic factors to explore the behaviors influencing the decisions of institutional investors at the Stock

Exchanges of Vietnam These researches can help to test the suitability of applying heuristics for all kinds of security markets with all components of investors.

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As a graduate student specializing in financial administration at HCMC University of Economics, I am conducting a thesis on the heuristic factors that affect decision-making and investment performance among individual investors in Vietnam I kindly request your participation in the attached questionnaire to share your insights as an individual investor Your input is invaluable to my research.

I kindly ask you to take 5-10 minutes to complete the questionnaire Your responses will aid in finalizing our thesis and provide valuable insights for investors in making informed decisions Rest assured, all collected data will remain confidential and will be presented in the synthesis report without disclosing any personal information.

I sincerely appreciate your assistance and wish you continued success as investors in the Vietnamese stock market Should you require it, we are prepared to provide a comprehensive report of this survey for your reference.

For further information please contact:

Nguyen Thi Thanh Thuy, Email: thuy.nguyentt90@gmail.com

6 Please estimate your average monthly income

High school and lower Under-graduate Bachelor

7 How long have you attended the stock market under 1 year 1-3 year 3-5 year

8 Please name the security company that you are holding an account for stock investment

MB Securities Joint Stock Company

Maybank Kim Eng Securities Joint Stock Company

Rong Viet Security Joint Stock Company

Sao Viet Securities Joint Stock Company

9 Have you attended any course of stock investment

If yes, please answer question 10, if not yet please jump to question11

10 How long does the course of stock investment that you attended take?

11 The total amount of money (USD) that you have invested at the Hochiminh Stock Market (too many levels)

12 The total amount of money (USD) that you have invested at the Hochiminh security market during last year (too many levels)

II HEURISTIC FACTORS INFLUENCING YOUR INVESTMENT DECISION

Please evaluate the degree of your agreement with the impacts of heuristic factors on your investment decision making:

Disagree Neutral Agree Extremely agree

13 You buy "hot" stocks and avoid stock that have performed poorly in the recent past

14 You forecast the changes in stock prices in the future based on the recent stock prices

15.You use trend analysis of some representative stocks to make investment decision for all stocks that you invest

16 You rely on your previous experiences in the market for your next investment

17 In your opinion, it is safe to invest in local stocks rather than to buy international stocks

18 You consider the information from your close friends and relatives as the reliable reference for your investment decisions

19 You uses predictive skills for investment decision making

20 You believe that your skills and knowledge of stock market can help you to outperform the market

21 You are sure you can make correct investment decision

Please give your opinion about the levels of agreement for the following statements:

Disagree Neutral Agree Extremely agree

21 Your investment has a lower risk compared to the market in general

22 Your investment in stocks has high degree of safety

23 Your investment has the ability to meet interest payments

Please give your opinions about the levels of agreement for the following statements:

Disagree Neutral Agree Extremely agree

24 You feel satisfied with your investment decisions in the last year (including selling, buying, choosing stocks and deciding stock volume)

25 The return rate of your recent stock investment meets your expectation

26 Your investment in stocks has demonstrated increased revenue growth in last year

BẢNG CÂU HỎI (Vietnamese version)

Tôi là sinh viên cao học ngành quản trị tài chính tại ĐH Kinh Tế TPHCM, hiện đang thực hiện luận văn tốt nghiệp về đề tài “Nghiên cứu các nhân tố phỏng đoán tác động đến quyết định và kết quả đầu tư chứng khoán của nhà đầu tư cá nhân tại Việt Nam” Tôi rất mong nhận được ý kiến của Ông/Bà, với tư cách là nhà đầu tư cá nhân, thông qua bảng câu hỏi đính kèm.

Kính mong Ông/Bà dành 5-10 phút để hoàn thành bảng câu hỏi, giúp chúng tôi hoàn thiện luận văn và cung cấp thông tin hữu ích cho các nhà đầu tư trong quyết định đầu tư Tất cả dữ liệu thu thập sẽ được bảo mật và chỉ được trình bày trong báo cáo tổng hợp, đảm bảo không tiết lộ thông tin cá nhân.

Chúng tôi xin chân thành cảm ơn sự hỗ trợ của quý Ông/Bà và chúc quý vị luôn đạt được thành công trong đầu tư trên thị trường chứng khoán Việt Nam Nếu quý Ông/Bà có nhu cầu, chúng tôi rất sẵn lòng cung cấp báo cáo tổng hợp từ cuộc khảo sát này để quý vị tham khảo.

Mọi chi tiết xin liên hệ:

Nguyễn Thị Thanh Thúy, Email: thuy.nguyentt90@gmail.com

3 Tình trạng hôn nhân: Single Married Divorced

5 Số nămkinh nghi ệm làm việcDưới 5 năm

6 Xin vui lòng cho biết thu nhập bình quân tháng của ông/bà

Dưới 6 triệu Từ 6-12 triệu 12-20 triệu

7 Ông/bà tham gia vào thị trường chứng khoán được bao lâu?

8 Ông/bà vui lòng cho biết tài khoản đầu tư của ông/bà được mở tại công ty nào?

Công Ty CP Chứn Khoán TP.HCM

Công Ty TNHH Chứng Khoán ACB Công Ty CP Chứng

Công Ty CP Chứng Khoán VNDirect

Công Ty CP Chứng Khoán MB Công Ty CP Chứng

Công ty TNHH Chứng khoán Ngân hàng TMCP Ngoại thương Việt Nam

Công Ty CP Chứng Khoán Maybank Kim Eng

Công Ty CP Chứng Khoán Rồng Việt

Công Ty CP Chứng Khoán Sao Việt

9 Ông/bà đã từng tham gia khóa đào tạo nào về đầu tư chứng khoán chưa? CóChưa

Nếu “Có”, vui lòng trả lời câu hỏi 10, nếu “Chưa” vui lòng chuyển sang câu hỏi 11

10 Khóa học về đầu tư chứng khoán mà ông/bà đã tham gia là bao lâu? 3 tháng 6 tháng trên 6 tháng

11 Ông/bà vui lòng cho biết tổng số tiền đầu tư của ông/bà trên sàn Hồ Chí Minh từ khi tham gia vào thị trường chứng khoán

Dưới 200 triệu Từ 200 triệu đến 1 tỷ Trên 1 tỷ

12 Tổng số tiền ông/bà đầu tư vào sàn Hồ Chí Minh trong năm vừa qua Dưới 200 triệu Từ 200 triệu đến 1 tỷ Trên 1 tỷ

II CÁC YẾU TỐ PHỎNG ĐOÁN ẢNH HƯỞNG ĐẾN QUYẾT ĐỊNH ĐẦU TƯ g

Xin ông/bà vui lòng đánh giá mức độ đồng tình của ông/bà đối với các phát biểu sau:

Các yếu tố Hoàn toàn không đồng ý

Trung lập Đòng ý Hoàn toàn đồng ý

13 Ông/bà mua các loại chứng khoán “nóng” và tránh mua những loại chứng ít khoán sinh lợi trong những năm gần đây

14 Ông/bà dựa trên giá hiện tại để dự đoán những thay đổi về giá trong tương lai

15 Ông/bà phân tích xu hướng của 1 vài cổ phiếu đại diện để ra quyết định đầu tư cho các cổ phiếu khác?

16 Ông/bà ra quyết định đầu tư dựa vào kinh nghiệm bản thân?

17 Ông/bà ưu tiên đầu tư vào cổ phiếu trong nước hơn cổ phiếu nước ngoài vì các thông tin về cổ phiếu trong nước đầy đủ hơn

18 Theo ông/bà, thông tin từ người thân và bạn bè là nguồn tin đáng tin cậy cho quá trình ra quyết định đầu tư

19 Ông/bà tin rằng kỹ năng và kiến thức của ông/bà có thể giúp ông/bà đạt mức sinh lợi cao hơn mức trung bình ca thị trường

20 Ông/bà tin chắc rằng các quyết định đầu tư của mình là đúng đắn

III QUYẾT ĐỊNH ĐẦU TƯ CỦA ÔNG/BÀ

Xin ông/bà vui lòng lựa chọn mức độ đồng tình của ông/bà đối với những phát biểu sau đây:

Các nhận định Hoàn toàn không đồng ý

Trung lập Đòng ý Hoàn toàn đồng ý

21 Nhìn chung, rủi ro của việc đầu tư của ông/bà thấp hơn thị trường

22 Quyết định đầu tư của ông/bà khá an toàn

23 Quyết định đầu tư của ông/bà có khả năng đáp ứng các khoản thanh toán lãi suất

IV KẾT QUẢ ĐẦU TƯ CỦA ÔNG/BÀ

Xin ông/bà vui lòng lựa chọn mức độ đồng tình của ông/bà đối với những phát biểu sau đây:

Các nhận định Hoàn toàn không đồng ý

Trung lập Đòng ý Hoàn toàn đồng ý

24 Ông/bà cảm thấy hài lòng với quyết định đầu tư của ông/bà trong năm vừa qua

(bao gồm quyết định chọn, mua, bán cũng như số lượng cổ phiếu được lựa chọn, mua và bán)

25 Tỷ suất sinh lời của việc đầu tư chứng khoán đáp ứng mong đợi của ông/bà

26 Ông/bà đạt được mức tăng trưởng thu nhập trong năm qua

94 100.0 a Listwise deletion based on all variables in the procedure.

Reliability analysis for each factor

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Cronbach's Alpha if Item Deleted

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .804

Extraction Method: Principal Component Analysis

Rotation Method: Varimax with Kaiser Normalization. a Rotation converged in 5 iterations.

APPENDIX 2 LIST OF SECURITIES FIRM SELECTED

No Name of securities company

4 Viet Capital Securities Joint Stock Company

6 MB Securities Joint Stock Company

7 FPT Securities Joint Stock Company

9 Maybank Kim Eng Securities Joint Stock Company

10 BIDV Securities Joint Stock Company

11 Rong Viet Security Joint Stock Company

12 Sao Viet Securities Joint Stock Company

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .764 Bartlett's Test of Sphericity Approx Chi-Square

Extraction Method: Principal Axis Factoring.

Rotation Method: Varimax with Kaiser Normalization. a Rotation converged in 5 iterations.

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .753 Bartlett's Test of Sphericity Approx Chi-Square

Extraction Method: Principal Axis Factoring.

Rotation Method: Varimax with Kaiser Normalization. a Rotation converged in 5 iterations.

1.3Research objective and research questions 3

CHAPTER 2: LITERATURE REVIEW, HYPOTHESIS AND RESEARCH MODEL 6

2.3 Decision making and performance of individual investor 11

2.4.1 Representativeness and investors’ de cision making 13

2.4.2 Availability bias and investors’ decision -making 15

2.4.3 Overconfidence and investors’ decision making 15

2.4.4 Investment decision making and investment performance 16

3.2.2Measure of invest ors’ decision making 23