(Luận văn thạc sĩ) heuristics influencing investment decision making and investment performance, evidence from individual investors in vietnam

INTRODUCTION

Background

In the financial markets, both individual investors and fund managers rely on decision-making tools like fundamental and technical analysis to guide their investment choices Research indicates that the structure of information and behavioral factors significantly impact these decisions and overall market outcomes However, actual investor behavior is often influenced by psychological principles, which help explain the motivations behind buying or selling stocks These behavioral factors highlight how investors process 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 reveals consistent patterns of irrationality in decision-making under uncertainty (Bernstein, 1996) Behavioral finance, which integrates psychological insights, helps explain why investors buy or sell stocks, or choose not to engage at all A significant challenge for investors lies in making sound investment decisions, as their profits or losses largely stem from their decision-making skills The 2008 subprime crisis, which impacted even the most educated investors, highlighted fundamental flaws in traditional models of rational market behavior (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,

In times of uncertainty, individuals often depend on heuristics or rules of thumb to evaluate the risks associated with different options This approach simplifies the complex process of assessing probabilities and predicting outcomes, enabling more straightforward decision-making.

Heuristics play a significant role in investment decision-making, as noted by Waweru et al (2008), but they can also introduce biases, as highlighted by Kahneman and Tversky (1974) and Ritter (2003) Kahneman and Tversky identify three key heuristic factors: representativeness, availability bias, and anchoring Additionally, Waweru et al mention two other important factors: Gambler’s fallacy and overconfidence in heuristics.

Numerous studies have explored the connection between heuristics, decision-making, and the performance of individual investors Tversky and Kahneman (1974) pioneered research on judgment under uncertainty, focusing on heuristics and biases Additionally, Hassan et al (2013) examined how affect heuristics, particularly fear and anger, influence individual investor decision-making Furthermore, Grinblatt and Keloharju (2000) investigated the investment behavior and performance of different investor types within Finland's stock market.

Research problem

Numerous studies in the literature indicate that behavioral finance significantly influences investors' decision-making processes Researchers are increasingly exploring psychological and sociological factors, including heuristics, that impact individual investment choices (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 amid a nascent Vietnamese stock market that faced challenges, including a lack of legal infrastructure, a rudimentary trading system, a limited number of securities firms, and few types of securities However, recent developments have led to substantial growth in both market size and overall functionality, transforming the landscape of Vietnam's financial markets.

As of November 2014, the Vietnam stock market comprised 345 companies listed on the Ho Chi Minh Stock Exchange (HOSE) with a total listed value of nearly 339 trillion VND, and 367 companies on the Hanoi Stock Exchange (HNX) valued at approximately 92.4 trillion VND This brings the total market value to over 145 trillion VND However, when compared to foreign stock markets, the Vietnam stock market is significantly smaller in both market size and capitalization.

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

Numerous studies indicate that individual investors struggle with investment decisions due to a lack of financial sophistication (Winchester et al., 2011) As a result, they often rely on heuristics or rules of thumb in their decision-making processes (Shikuku, 2010) This raises concerns about the extent to which heuristics influence the investment decisions of individual investors in the Vietnamese stock market Therefore, this research aims to explore the impact of heuristic factors on individual investors' decision-making and performance 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 h

4 positive influence on the investment performance?

Scope of the research

Investor behavior in stock markets is significantly influenced by various heuristic factors This study focuses on three key heuristics: representativeness, availability, and overconfidence Previous research has highlighted the impact of these biases, including Glaser and Weber's (2012) exploration of overconfidence bias, Taffler's (2012) investigation into representativeness bias, and Shikuku's analysis of behavioral factors affecting investment decisions among unit companies in Kenya.

According to a 2014 report by Wall Street Securities, individual investors represent over 60% of trading activity in the Vietnamese stock market, highlighting their significant role and concern within this financial landscape.

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, housing the prominent Ho Chi Minh Stock Exchange, which is the country's leading stock market.

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 h

5 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 results and research findings, offering a comprehensive conclusion along with implications and limitations of the study Additionally, this thesis provides valuable suggestions 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 under uncertainty This behavioral theory addresses the shortcomings of expected utility theory in decision-making and has applications in social psychology Kahneman and Tversky proposed that investors perceive gains and losses through an S-shaped utility function, highlighting key psychological factors such as regret aversion, loss aversion, and mental accounting that impact decision-making processes.

In the theory of reference points, individuals establish a benchmark for comparison regarding their wealth When their wealth falls below this reference point, investors tend to become risk-seeking, willing to take on riskier investments to regain or exceed their desired wealth level Conversely, when their wealth is above this reference point, their risk preferences shift, influencing their investment strategies.

According to conventional theories, investors exhibit risk aversion, leading to a downward-sloping utility function However, Kahneman and Tversky highlight that individuals tend to be risk-seeking when facing potential losses, as noted in Finucane et al.'s (2002) study This results in a concave utility function for gains, indicating that while people enjoy gains, the satisfaction does not double with twice the gain Conversely, the utility function is convex for losses, meaning that although individuals feel pain from losses, experiencing double the loss does not equate to experiencing double the pain.

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 derivative, remained the prevailing framework guiding investor and economic decision-making.

Heuristics, as described by Bramson (2007), function as a normative decision theory that simplifies decision-making in complex and uncertain situations Defined as rules of thumb, heuristics help reduce the complexity of evaluating probabilities and predicting outcomes, enabling individuals to make simpler judgments (Kahneman & Tversky, 1974, p.1124) These cognitive shortcuts are especially valuable when time is constrained (Waweru et al.).

2008, p.27), but sometimes they lead to biases (Kahneman & Tversky, 1974, p.1124; Ritter,

According to Balota et al (2004), decisions often hinge on beliefs about uncertain events, such as election outcomes or a defendant's guilt These beliefs manifest in phrases like "I think that " or "chances are ," and sometimes in numerical odds or subjective probabilities The formation of these beliefs raises questions about their determinants and how individuals evaluate uncertain probabilities People typically utilize a limited set of heuristic principles that simplify the complex process of probability assessment and value prediction.

Selden (1912) said that psychology of the stock market that the ups and downs of h

Stock prices in exchanges are significantly influenced by the psychological perspectives of investors and traders Research by Finucane et al (2000) indicates that there is an inverse relationship between perceived risk and perceived benefits, largely due to the affect heuristic.

Investors often seek high returns on their investments, but their decisions can be significantly influenced by the affect heuristic This cognitive bias leads them to overlook potentially lucrative "hot stocks" perceived as high-risk, highlighting the inverse relationship between affect heuristic and judgment in decision-making Many investors rely on pre-existing mental images and symbols to assess risks and benefits, which ultimately guide their financial choices in the stock market Additionally, some investors lack financial literacy and education, making it difficult for them to conduct thorough financial analyses Consequently, 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, simplifying complex problems while limiting the amount of explanatory information (Shikuku, 2010) Individual investors often rely on trial and error, developing rules of thumb to navigate investment decisions While these heuristics can sometimes lead to favorable outcomes, they may also result in poor decision-making (Chandra, 2008) One common heuristic involves assessing the frequency or likelihood of an event based on how easily examples come to mind Additionally, the affect heuristic illustrates how subjective feelings of "goodness" or "badness" can influence rapid judgments and introduce systematic biases (Finucane et al., 2000).

According to Ganzach (2001), investors tend to view stocks they consider "good" as having low risk and high returns, while "bad" stocks are perceived as having high risk and low returns For unfamiliar stocks, there is a negative correlation between perceived risk and perceived return, aligning with the affect heuristic Conversely, for familiar stocks, this relationship is positive; investors expect riskier stocks to yield higher returns, consistent with traditional economic theory.

Numerous studies have explored heuristics, with Tversky and Kahneman (1974) identifying three key types: representativeness, availability, and anchoring and adjustment Later research, including that by Hassan et al (2013) and Bramson (2007), expanded on these concepts, introducing the affect heuristic, which incorporates emotional factors into decision-making Additionally, Gilovich and Griffin (2002) identified six general-purpose heuristics: affect, availability, causality, fluency, similarity, and surprise, highlighting the diverse ways in which heuristics influence human thought processes.

Kahneman and Tversky (1974) pioneered the study of heuristics, identifying three key factors: representativeness, availability bias, and anchoring Waweru et al (2008) expanded this framework by adding Gambler’s fallacy and Overconfidence to the heuristic theory Schwartz (1998) highlighted the substantial evidence supporting general heuristics, particularly representativeness, availability, anchoring and adjustment, and affect, while noting a lack of research on the specific heuristics applied in decision-making processes.

The rapid spread of information has complicated decision-making for financial market participants, leading to a greater reliance on heuristics, which can be both unavoidable and potentially detrimental (Shikuku, 2010; Fromlet, 2001) The interpretation of new information often necessitates heuristic decision-making rules (Finucane et al., 2002) Additionally, Chandra (2008) explored how behavioral factors influence investors' attitudes toward risk.

The study on the decision-making process of individual investors reveals that cognitive biases, including representativeness, overconfidence, and anchoring, significantly impact their perception of risk Additionally, factors such as cognitive dissonance, greed, fear, regret aversion, and mental accounting further influence 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 that leads individuals to evaluate outcomes and categories based on prominent and often superficial characteristics, resulting in a tendency to assume that similar cases will yield similar results (1991, p.18) This bias can lead to errors in judgment, as it often disregards essential factors that should influence probability assessments (Barker & Nofsinger, 2012, p 259) Additionally, representativeness can cause individuals to overemphasize recent experiences while neglecting the average long-term rates, further skewing their decision-making process (Ritter, 2003, p.432).

Availability is a cognitive heuristic that emphasizes the importance of information based on its ease of recall rather than its actual probability or frequency This phenomenon often arises from recent, impactful news events that influence how we perceive and weigh information.

Availability can be categorized as experience-based, memory-based, or imagination-based, as noted in 2001 However, there remains a lack of consensus on the varying degrees of availability and the significance of these differences A notable example of this is seen in the behavior of a successful mutual fund manager, who consciously avoided stocks that were popular among analysts and managers, believing that such heightened "availability" increased the risk of those companies' shares being overvalued.

Investors often overvalue national stocks while overlooking international opportunities, a trend that persisted until the mid-1990s This behavior may stem from the availability heuristic, which leads to a bias influenced by the prominence of certain information The availability bias is particularly problematic as it lacks sensitivity to sample size, meaning that readily available information may not accurately represent broader market trends.

Overconfidence is a widespread issue that can lead to significant negative outcomes, including wars, strikes, legal disputes, entrepreneurial failures, and stock market bubbles Researchers have highlighted its role in various critical situations (Camerer & Lovallo, 1999; Glaser & Weber, 2007; Howard, 1983; Johnson, 2004; Malmendier & Tate, 2005; Neale & Bazerman, 1985; Odean, 1999) As noted by Plous (1993), overconfidence poses a major challenge in judgment and decision-making, often resulting from an inflated belief in the reliability of one’s knowledge and abilities (DeBondt & Thaler, 1995; Hvide, 2002).

Overconfidence is widely regarded as the most common judgment bias, as highlighted by Barker & Nofsinger (2012) Numerous studies indicate that this bias can result in poor decision-making among investors, managers, and politicians For instance, both investors and analysts frequently exhibit overconfidence in their areas of expertise, which can adversely affect their choices and outcomes.

Overconfidence can enhance persistence, determination, mental agility, and risk tolerance, ultimately leading to improved professional performance This heightened self-assurance may positively influence others' perceptions of one's abilities, facilitating quicker promotions and longer investment durations.

Decision making and performance of individual investor

Making investment decisions in the stock market is increasingly complex and requires deeper insight and understanding These decisions can be significantly influenced by psychological and behavioral factors, as noted by Evans (2006) and Waweru et al (2008) While traditional finance assumes that investors act rationally, behavioral finance challenges this notion, highlighting the impact of cognitive biases and emotions on investment choices.

Many stock market investors often act irrationally, as their decision-making is influenced by emotions, psychology, and behavioral biases These factors can lead to systematic errors in how they interpret and process available information (Pavabutr, 2002).

Investment decisions are characterized by complexity and uncertainty, as highlighted by Macmillan (2000) Complexity arises from the multitude of alternative actions available to decision-makers, while uncertainty is a fundamental aspect of all decision-making, particularly for investors whose choices significantly impact their organizations Investors often face the challenge of balancing multiple objectives, necessitating trade-offs between expected returns and associated risks Dean and Sharfman (1996) emphasize that despite these complexities, the influence of choice remains crucial to decision effectiveness, as it is improbable for all potential options to yield equal success or failure.

Barber and Odean (2008) highlight that attention significantly impacts individual investors' purchasing decisions, as they often struggle with the complex task of selecting stocks Instead of conducting thorough searches, many investors tend to focus on stocks that initially attract their attention, such as those featured in the news or experiencing substantial price fluctuations This tendency results in a disproportionate investment in these attention-grabbing stocks Additionally, since most individual investors maintain a limited portfolio and primarily sell stocks they already own, the selling process is less affected by attention, making it a simpler decision.

Personal rate of return reflects an individual's investment performance derived from their transaction history and cash flows This article explores the investment outcomes of common stocks directly owned by individual investors Research by Schlarbaum, Lewellen, and Lease (1978a) assesses the overall performance of common stocks at a full-service brokerage firm, while Odean (1999) and Schlarbaum, Lewellen, and Lease (1978b) focus on the profitability of individual stock trades Additionally, Lin and Swanson (2003) evaluate investment performance through three key criteria: raw returns, risk-adjusted returns, and momentum-adjusted returns.

Investors often experience strong performance across various time horizons—daily, weekly, monthly, quarterly, and annually This short-term success is largely influenced by price momentum rather than by taking on additional risks.

Research by Barber and Odean (2001) indicates that men exhibit greater overconfidence than women in traditionally male-dominated areas, leading to excessive trading that negatively impacts their investment performance Their findings reveal that men have an annual turnover rate of approximately 80%, compared to 50% for women, resulting in poorer returns for male investors Despite both genders earning subpar returns, men’s aggressive trading strategies contribute significantly to their underperformance, as neither men nor women demonstrate superior stock selection abilities, with gross returns on trades being similar Ultimately, the gender disparity in investment outcomes is largely attributed to men's tendency to trade more frequently, incurring higher trading costs that diminish their overall returns.

Various methods exist to assess investment performance, with some researchers, like Lin et al (2003), utilizing secondary data from investors' results in the securities markets In contrast, others, including Oberlechner & Osler (2004) and Le & Doan (2011), rely on 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.,

Representativeness can lead to "sample size neglect," where individuals draw conclusions from insufficient data (Barberis & Thaler, 2003) In the stock market, investors often gravitate toward "hot" stocks rather than underperforming ones, illustrating the application of representativeness This tendency contributes to investor overreaction, as highlighted by DeBondt and Thaler (1995) Consequently, well-performing stocks are perceived more favorably, influencing investment decisions.

Investors often perceive stocks of reputable companies as safe investments, reflecting the representativeness heuristic However, this perspective 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 history of consistent earnings and growth However, it's important to recognize that very few companies can maintain this level of growth over time (Finucane et al 2002; Raines & Leather).

In 2011, researchers highlighted that relying on numerical predictions of stock values based on company descriptions can lead to an overreliance on stereotypes, neglecting the importance of base rate information Kahneman and Tversky (1974) demonstrated that individuals often categorize events as typical representatives of familiar classes, which can skew probability estimates by placing undue emphasis on these categories while ignoring the actual underlying probabilities.

Representativeness plays a crucial role in investor behavior, as many tend to extrapolate price movements based on recent trends Investors often assume that rising prices indicate a continuation of success, while falling prices suggest ongoing decline This bias leads to an over-optimism about past winners and excessive pessimism about past losers, as noted by DeBondt & Thaler (1985) Consequently, trading influenced by representativeness can distort share prices, causing them to deviate from levels that accurately reflect all pertinent information.

Behavioral finance has been emerging in Vietnam, yet empirical evidence regarding the representativeness bias in investor decision-making remains inconsistent Therefore, we propose the following hypothesis:

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

2.4.2 Availability bias and investors’ decision-making

Availability bias, as noted by Shefrin (2002), affects investors' decision-making by leading them to rely on easily accessible information while undervaluing less frequently encountered data This tendency causes investors to favor stocks of well-publicized companies, which may appear to perform better despite potentially having inferior earnings and return prospects compared to less-prominent firms.

Recent information, particularly from media and corporate announcements, is often easily remembered by individuals, especially when influenced by their broker’s or advisor’s suggestions 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 influences investors' probability assessments based on how easily they can recall similar instances This can lead to biases when the perceived availability diverges from actual frequency Research by Klibanoff, Lamont, and Wizman (1998) illustrates that significant country-specific news can dramatically impact closed-end country fund prices relative to asset values Typically, prices tend to underreact to fundamental changes; however, when a country is prominently featured in the Saigon Times, the market reacts much more vigorously.

In general, the behavioral finance is suggested for some period in Vietnam, however, there is no empirical study on the availability bias towards decision making of investors

As such, one hypothesis is suggested as follows

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

2.4.3 Overconfidence and investors’ decision making

Overconfidence models suggest that the presence of overconfident traders leads to increased trading volume in the market Additionally, individual overconfident investors tend to engage in more aggressive trading, with their trading volume rising in correlation to their level of overconfidence This phenomenon has been identified by Odean as a particularly significant effect in trading behavior.

DeBondt and Thaler (1995) highlighted that the significant trading volume in financial markets poses a challenge to traditional finance theories, identifying overconfidence as a crucial behavioral factor in understanding this trading phenomenon.

This research examines the influence of overconfidence on investment decision-making and performance among individual investors at the Ho Chi Minh Stock Exchange The study aims to assess the levels of impact that overconfidence has on these financial behaviors.

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 overconfidence and bounded rationality, aim to explain return patterns like long-horizon reversals These theories are grounded in psychological research and common sense, but they would gain from a deeper understanding of actual investor behavior and the variations in individual reactions to the same information (Grinblatt and Keloharju, 2000).

Recent studies have highlighted notable patterns in the behavior of investors based on past returns Brennan and Cao (1997) proposed a theoretical model and provided empirical evidence suggesting that foreign investors are likely to adopt momentum strategies but may underperform due to their lack of information compared to domestic investors Additionally, research by Froot et al (2000) and Choe et al (1999) indicates that foreign investors often act as momentum investors, with the latter study concentrating on shorter past-return periods.

Grinblatt and Keloharju (2000) highlight that data limitations have historically hindered a comprehensive analysis of the investment behavior and performance across all investor categories Variations in research methods, data frequencies, past return horizons, and institutional arrangements complicate the comparison of results, making it challenging to discern general patterns in the behavior and performance of distinct investor groups.

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

Heuristic factors significantly influence individual investors' decision-making and performance in financial markets, particularly in stock markets This chapter provides a detailed analysis of the theoretical foundations, including prospect theory and heuristics theory Additionally, the thesis defines and classifies the heuristics utilized by individual investors Crucially, it examines the relationship between decision-making processes and the performance of individual investors in the Ho Chi Minh stock market, while also developing hypotheses and constructing a conceptual model.

RESEARCH METHODOLOGY

Research process

A theory is developed and tested through two primary approaches: induction and deduction The deductive approach begins with an existing theory and logical relationships among concepts, leading researchers to seek empirical evidence Conversely, the inductive approach involves developing a theory based on observations of empirical reality, allowing researchers to infer implications for the initial theory that inspired the research (Ghauri & Gronhaug, 2010; Saunder et al., 2009; Blumberg et al., 2005; Bryman & Bell, 2007).

This study aims to explore the heuristic factors that influence investor performance and decision-making, utilizing a deductive approach rather than building theory from scratch It begins by reviewing behavioral finance theories, particularly in the stock market, to establish a theoretical and empirical foundation for the proposed research model and hypotheses Subsequently, interview and questionnaire questions are developed in line with the deductive approach, which posits that researchers can understand how the world operates and can test these ideas against empirical data The hypotheses are then tested through data collection and analysis, allowing for comparisons between research results and existing theories to identify discrepancies This deductive approach is closely associated with quantitative research, involving the collection and statistical analysis of quantitative or quantifiable qualitative data, thus aligning with established quantitative research strategies.

Research problem Research objective Research scope

The research problem was clearly defined, followed by the identification of research objectives and questions aimed at addressing this issue A literature review was then conducted to explore relevant theories on heuristic factors that influence decision-making and the performance of individual investors, ultimately selecting a suitable model for the Vietnamese stock market and formulating hypotheses for the study Based on prior studies, a preliminary questionnaire was developed, leading to the next phase of research design, which consists of two sub-steps.

A pilot study was conducted through face-to-face interviews with two managers from a securities company to evaluate the content, quantity, and structure of questions in a preliminary survey aimed at testing its effectiveness before the main survey 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 using various methods, including mail, social networks, and direct distribution of hard copies to individual investors, facilitated by securities company brokers Data collection took place one month after the survey was initiated.

Data was meticulously cleaned and analyzed to assess the reliability of the scale and the validity of the questionnaire using Cronbach’s alpha coefficient and Exploratory Factor Analysis (EFA) To evaluate the hypotheses, a multiple regression method was employed, with the implications and findings clearly stated 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, particularly heuristic theory and prospect theory, as outlined by Waweru et al (2008, p.24-38) It examines how behavioral factors influence investors' decision-making processes, synthesizing key questions regarding the impact of heuristic factors on investment choices.

The study employs 5-point Likert scales, a common tool for gauging respondents' opinions and attitudes (Fisher, 2010, p.214), to assess individual investors' agreement regarding the influence of heuristic factors on their investment decisions and performance The scale ranges from 1 (extremely disagree) to 5 (extremely agree) Detailed measurements and questions related to these aspects are outlined in tables 3.1, 3.2, and 3.3.

This article examines the influence of behavioral finance on investment decision-making, focusing on three key heuristics: representativeness, availability, and overconfidence Representativeness is assessed through three observed variables, utilizing a five-point Likert scale as developed by DeBondt & Thaler (1995) and modified by subsequent researchers, particularly Kengatharan (2013) and Le & Doan (2011) Availability factors are measured using three observed variables informed by the studies of Hassan et al (2013) and Le & Doan (2011) Lastly, the source of overconfidence bias is primarily derived from Kengatharan (2013), Luu (2014), and Qureshi's research.

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?

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?

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, with this study focusing on their perspectives regarding investments The research evaluated investors' decision-making using three observed variables, as established by Hassan et al (2013) and Qureshi (2012), employing 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

Investors’ investment in stocks has high degree of safety

Investors’ investment has the ability to meet interest payment

Previous studies primarily relied on secondary data from investors' results in the securities markets to assess stock investment performance (Lin and Swanson, 2003; Kim and Nofsinger, 2003) In contrast, this research invites investors to evaluate their own investment performance, drawing on the methodology of Oberlechner and Osler (2004) to determine investment return rates Specifically, the study examines stock investment returns from both objective and subjective perspectives Investors subjectively assess their performance by comparing actual return rates to their expected rates, while objective evaluations involve comparing real return rates to the average market return Additionally, this research introduces satisfaction levels regarding investment decisions as a performance metric, acknowledging that some investors may feel satisfied with low profits, while others may be dissatisfied despite higher returns Thus, both satisfaction levels and investment return rates are proposed as key indicators of investment performance.

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

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)

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

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

Pilot test

To develop a pilot test questionnaire, 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, considering the managers' limited availability These managers, responsible for trading surveillance and market information at HOSE, possess extensive knowledge of the stock market and investor behavior, making them qualified participants for this study Their expertise is expected to contribute valuable analysis and discussions on the topic.

Before conducting the main survey, a draft survey will be collected To ensure the reliability of the measurements, Cronbach’s Alpha Test is employed, with a recommended threshold of at least 0.7 for acceptable internal consistency, as suggested by Nunnally (1978) However, some statisticians, like Shelby (2011), argue that a Cronbach’s alpha above 0.6 may also be deemed acceptable Additionally, it is crucial to consider corrected item-total correlations when utilizing the Cronbach’s alpha index, as these correlations provide insights into the reliability of individual items within the measurement scale.

In this research, a Cronbach's alpha threshold of 0.6 or higher is established, with a corrected item-total correlation index of 0.3 or more, reflecting the newness of financial behavior measurements for stockholders on the Ho Chi Minh Stock Exchange Additionally, the acceptable significance level for the F-test in the Cronbach’s alpha technique is set at 0.05 or less The Cronbach’s alpha test is 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 a Cronbach’s alpha coefficient exceeding 0.6 for most variables While the availability bias scale recorded a Cronbach’s alpha of 0.566, which is below 0.6 yet above 0.5, its Corrected Item Total Correlation values were above 0.3, confirming its acceptance for exploratory factor analysis Overall, the scales developed in this research exhibit statistical significance and necessary reliability.

Following the application of factor analysis using the Varimax method, four distinct factors emerged: representativeness bias, overconfidence bias, decision-making, and the performance of individual investors The variable for availability (AVA2) was excluded due to double loading, while AVA1 and AVA3 were consolidated within the same components of representativeness and overconfidence Consequently, AVA1 has been renamed to RE4, and AVA3 to OVER3 The proposed model suggests that representativeness and overconfidence biases significantly influence the decision-making processes and performance outcomes of individual investors.

Main survey

To effectively explore the heuristic factors at the HOSE, a larger sample size is recommended, as it enhances representativeness and reliability of results (Saunders et al., 2009) However, the optimal sample size is contingent upon the researchers' available resources, such as time, finances, and personnel (Saunders et al., 2009) According to Hair et al (1998), a minimum of 100 respondents is necessary for quantitative research to ensure the applicability of statistical data analysis methods.

Questionnaires were distributed to respondents through stratified random sampling to ensure a representative sample of individual investors' financial behaviors Initially, convenience sampling was employed for its efficiency in achieving a high response rate among friends and relatives, which also helped save time and resources However, as convenience sampling is a non-probability method, it cannot yield results that are generalizable to the entire population (Bryman & Bell, 2007, p.198) In contrast, stratified random sampling enables the population to be divided based on specific criteria, such as brokerage market share, allowing for the selection of random or systematic samples from each group, thereby enhancing the validity of the findings.

Stratified sampling guarantees that the sample reflects the population's distribution accurately (Bryman & Bell, 2007, p.187) A series of questionnaires were distributed to brokers from these companies, who randomly assist their investors This approach was adopted to optimize time efficiency in data collection.

H4 (+) Figure 3.3 New Research Model (revised) h

28 constraint, only individual investors from ten leading securities companies have been chosen See appendix 2 for the list of securities firms selected

The study utilized a non-probability convenience sampling method to collect data 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 through Multiple Regression analysis Following the guidelines of Hair et al (1998), the sample size for EFA was determined to be at least five times the number of variables, requiring a minimum of 100 participants for the five variables included in the study.

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 0.3 or more

In the pilot test, the exploratory factor analysis (EFA) method with Varimax rotation was employed to examine the observed variables related to heuristic factors that impact decision-making and individual performance Additionally, the Kaiser-Meyer-Olkin (KMO) test and Bartlett's test were utilized to assess the suitability of the sample for analysis.

This study employed Multiple Regression Analysis to evaluate the 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 of residuals that are uncorrelated with the predictors A significant concern is multicollinearity, which arises from high intercorrelations among predictor variables, potentially leading to misleading or inaccurate results.

A correlation matrix can help identify potential multicollinearity among predictor variables, but it may not always reveal its presence Multicollinearity can arise when multiple predictors are collectively related to other predictors or sets of predictors Therefore, it is crucial to test for multicollinearity when conducting multiple regression analysis This analysis 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 multi-stage analysis, beginning with a summary and examination of the respondents' demographic profiles Next, the reliability of the measurement items was confirmed using Cronbach’s alpha The correlation between the independent and dependent variables was assessed through the Promax method Finally, standard multiple regression analysis was conducted to determine the statistical significance of the model and 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 total of 400 questionnaires were distributed to individual investors at the Ho Chi Minh Stock Exchange, yielding 186 responses and resulting in a response rate of 47%, which is considered moderately high for a postal survey The data was collected from a securities company in Ho Chi Minh City The sample of 186 respondents was analyzed based on various demographic characteristics, including gender, age, marital status, education, years of work experience, income, duration of stock market participation, affiliated securities company, stock courses taken, course duration, total investment in the stock market, and last year's investment amount These details are summarized in Table 4.1, providing a comprehensive overview of the respondents' demographic statistics.

Table 4.1 Descriptive statistic of respondent’s characteristics

Years of attendant the stock market

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 involved a sample size of 186 participants, evenly split between genders The majority of respondents, 34%, were aged between 18 and 35 years Marital status revealed that over 55% were single, while 36% were married and 9% divorced Educationally, 73% held a bachelor's degree Most investors had less than five years of work experience (52%), and 34% reported an income between $300 and $600 A significant portion of individual investors had been participating in the stock market for 1 to 3 years (28%), with 36% holding accounts at Hochiminh City Securities Corporation Additionally, 67% of investors had completed a stock market course, with 65% attending a three-month program Notably, 46% of respondents invested less than $10,000 since entering the stock market, and 50% maintained this investment level over the past year.

Measurement Reliability using Cronbach’s Alpha

To evaluate the reliability of measurement scales and assess the cohesion of their items, the Cronbach’s alpha coefficient is utilized This statistical measure determines the internal consistency reliability of scales, ensuring that the items effectively measure the same underlying construct (Pallant).

2001) For scale to be reliable, its Cronbach’s alpha value should be above 0.6 (George & Mallery, 2003)

The above guideline indicates that the higher the Cronbach’s alpha value is, the more reliable are the items measuring a give construct Cronbach’s alpha closer to 1.0 is preferred

A Cronbach’s alpha value of 0.9 or higher indicates a highly reliable scale, while a value below 0.5 signifies an unreliable scale that is unsuitable for measuring a specific construct.

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

Table 4.2 Reliability analysis for each factor

Scale Variance if Item Deleted

Cronbach's Alpha if Item Deleted

The Cronbach’s alpha of representativeness (RE) is 0.759, which is larger than 0.6, and the corrected item-total correlation of representativeness 1 (RE1) = 0.703, representativeness 2 (RE2) = 0.655, representativeness 3 (RE3) = 0.711, representativeness

4 (RE4) = 0.735 all of those are larger than 0.3 Therefore, this variable is accepted for Exploratory Factor Analysis later h

The Cronbach’s alpha of overconfidence (OVER) is 0.608, which is larger than 0.6, and the corrected item-total correlation of overconfidence 1 (OVER1) = 0.424, overconfidence

2 (OVER2) = 0.383, overconfidence 3 (OVER3) = 0.692 all of those are larger than 0.3 Therefore, this variable is accepted for Exploratory Factor Analysis later

The Cronbach’s Alpha for investment decision making (DEC) is 0.753, exceeding the acceptable threshold of 0.6 Additionally, the corrected item-total correlations for investment decision making are DEC1 = 0.629, DEC2 = 0.671, and DEC3 = 0.740, all of which are above the minimum requirement of 0.3 Consequently, this variable is deemed suitable for further Exploratory Factor Analysis.

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

Scale testing by using Exploratory Factor Analysis (EFA)

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

The analysis of the dependent and independent variables was conducted separately for each factor Utilizing Principal Axis Factoring with Varimax rotation, all extracted variables demonstrated significant loadings greater than 0.5 on acceptable factors, confirming their relevance in the study.

Using the Varimax method for factor analysis, four key factors emerged from the independent and dependent variables: 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) revealed two independent variables—representativeness (RE1, RE2, RE3, RE4) and overconfidence (OVER1, OVER2, OVER3)—alongside two dependent variables: investment decision making (DEC1, DEC2, DEC3) and investment performance (PER1, PER2, PER3) These findings are deemed acceptable and hold practical significance for understanding investment behaviors.

Test hypotheses by multiple regressions

In this section, two linear regressions were used to analyze and evaluate these variables in order to find out relationship between dependent variable – investment decision h

38 making with independent variables (representativeness and overconfidence) Specifically, there are two models included:

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)

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

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

Table 4.4 indicates a low correlation between Representative bias (RE) and Investment decision making (DEC), with a significance level of 0.238, which exceeds the 0.05 threshold In contrast, the correlation between Overconfidence bias (OVER) and DEC is significantly high, as evidenced by a significance level of 0.000, which is below 0.05.

Result of assumption of regression h

The analysis of tolerance and Variance Inflation Factor (VIF) values in the Coefficients table indicates the absence of multicollinearity, as all variables exhibit VIF values below 10 Notably, the VIF for RE and OVER is 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

The Enter method is 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 presented 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, indicating the variance in investment decision-making explained by the model A high R Square value suggests effective model performance; however, it may exaggerate success in real-world applications The Adjusted R Square offers a more accurate assessment of model efficacy The analysis includes variables such as representativeness and overconfidence, with the R Square value of 0.122 reflecting the model's relevance, while the Adjusted R Square indicates a lower value, suggesting room for improvement in the model's explanatory power.

The assessment of model relevance using this system is both safer and more accurate, as it avoids overstating the consistency with the model The adjusted R² value of 0.113 reveals that only 11.3% of the variance in individual investors' decision-making can be predicted by the independent variables This indicates that the regression model, which includes representativeness and overconfidence bias, explains just 11.3% of the total variation in investment decisions.

Squares df Mean Square F Sig

Total 101.403 185 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 Variance Inflation Factor (VIF) values in the Coefficients table demonstrate the absence of multicollinearity, as all variables have VIF values below 10, with the VIF for RE and OVER being 1.025.

Table 4.7 Regression analysis of variables

Table 4.7 displays the Sig and Standardized Beta Coefficient values, indicating the unique contribution of each independent variable to the model while controlling for other predictors A higher value suggests a significant contribution to the model Notably, the standardized Beta coefficient for overconfidence bias (RE) and decision-making (DEC) was 0.343, with a significance value of 000 The t and p values further emphasize the statistical significance of each independent variable in predicting the dependent variable A large absolute t value combined with a small p value (p < 05) confirms that overconfidence bias significantly influences individual investors' investment decision-making, thereby supporting hypothesis H3.

The RE variable demonstrated a standardized Beta of 0.033 and a significance value of 0.635, indicating that at a 95% confidence level, RE does not significantly influence the dependent variable DEC (investment decision making) Consequently, 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 h

42 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 In contrast, RE exhibits a weak correlation and statistical significance, as evidenced by its significance value of Sig = 0.635, exceeding the 0.01 level.

The multiple regression analysis revealed that overconfidence bias is the only significant factor, accounting for 34.3% of the variance in individual investors' investment decision-making Consequently, hypothesis H3 is accepted, while hypothesis H1 is rejected This indicates that overconfidence bias plays a crucial role in influencing investment decisions.

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) h

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 input all variables for selection based on criteria with a significance level of less than 0.05 The outcomes of the regression analysis are presented as follows:

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 the inclusion of all variables related to investment decision-making and performance in the analysis The evaluation of the multiple linear regression model reveals a coefficient of determination (R²) of 0.136, indicating the relevance of the research model.

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 metric, being smaller than the regular R-squared, provides a safer and more accurate assessment of model relevance, as it avoids overstating the model's consistency with the data.

Model Sum of Squares df

Total 137.367 185 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 the Variance Inflation Factor (VIF) values are all below 10, with the VIF for the RE variable being 1.000 This confirms the assumptions of multiple regressions are satisfied.

Table 4.11 Regression analysis of variables

B Std Error Beta Tolerance VIF

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 h

45 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 data analysis results concerning measurement scales, the research model, and hypotheses Initially, Cronbach’s alpha and Exploratory Factor Analysis (EFA) were utilized to refine the measurement scales for alignment with market data Subsequently, the research model was evaluated using structural equation modeling, revealing that overconfidence bias significantly influenced both investment decision-making and performance In contrast, the variables of representativeness and availability did not affect decision-making or investment outcomes, with hypothesis 1, which posited that representativeness impacts investors' decisions, being unsupported 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, detailing factors such as gender, age, educational level, and marital status Additionally, the analysis highlights both independent and dependent variables, enabling individual investors in Ho Chi Minh City to understand the key factors influencing their investment decision-making and overall investment performance.

The study addresses two key questions outlined in Chapter 1, providing conclusive insights through a detailed analysis of the main findings The subsequent section summarizes these results, effectively answering the research questions posed at the beginning of the study.

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

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 Subsequent exploratory factor analysis (EFA) revealed four factors from the initial 14 variables, focusing on representativeness bias and overconfidence bias Linear regression analysis was conducted to examine the relationship between the 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 investment decision-making, thereby supporting hypothesis H3.

This research is one of very few studies of heuristic factors impacting the stock h

This study explores 47 investment decisions influenced by behavioral finance among individual investors in Vietnam, expanding upon previous research that primarily focused on limited behavioral dimensions, such as herding effects noted by Nguyen and Vuong (2006) and Tran (2007) By incorporating a broader range of heuristic factors, this research enhances the understanding of behavioral influences in frontier and emerging stock markets The consistency and reliability of the 5-point measurement scale are validated through Factor Analysis and Cronbach’s Alpha, confirming the applicability of heuristics in the Vietnamese stock market.

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

A linear regression analysis was conducted to explore the relationship between individual investors' decision-making processes and their investment performance The findings indicate that the decision-making factors significantly influence the returns achieved by individual investors.

This research introduces a unique approach to measuring investment performance by having investors self-evaluate 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 assess 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

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

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

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

The multiple regression analysis revealed that overconfidence bias significantly influences investment decision-making, accounting for 34.3% of the variance in this area, leading to the acceptance of hypothesis H3 Conversely, hypothesis H1 was rejected, as overconfidence bias was found to be insignificant in explaining variations in investment performance Additionally, investment decision-making itself was shown to significantly explain 36.9% of the variance in individual investors' performance, resulting in the acceptance of hypothesis H4 Overall, the findings indicate that while overconfidence bias does impact 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 influence of heuristics on their investment decisions will enable government officials to effectively monitor and manage stock market operations Furthermore, this knowledge will assist in formulating appropriate policies to enhance the stock market landscape in Vietnam.

Securities firms can enhance their predictions of future stock market trends by utilizing this research, which offers valuable insights This information enables brokers to provide more reliable consulting services tailored to the needs of various individual investors.

Individual investors can enhance their investment decision-making and performance by understanding the significant role of heuristics in their choices This research empowers them to make more informed decisions moving forward.

Investors are advised to approach investment decisions with caution and avoid overconfidence based on past successes, as this can introduce bias into their decision-making process Additionally, it is crucial for investors to maintain a cohesive investment portfolio rather than separating assets into independent accounts, since each component may significantly impact the others Treating investments as isolated can lead to poor overall performance.

Limitation and direction for further research

While the sample size of 188 is relatively large and meets statistical requirements, future research should aim for an even larger sample to better represent the Vietnamese stock market Furthermore, although respondents were selected from ten leading securities companies 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 h

Vietnam's investment performance evaluation relies heavily on investor perceptions, which can be subjective and limited Many investors may lack awareness of their expected return rates and the average stock market returns To improve the accuracy of investment performance measurements, it is essential to integrate investors' assessments with secondary market data.

This study explores heuristic factors in Vietnam through a 5-point Likert scale among volunteers To validate these findings, further 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

Further research is recommended to examine heuristic factors that impact the decision-making behaviors of institutional investors in Vietnam's Stock Exchanges Such studies can assess the effectiveness of applying heuristics across various types of securities markets and among diverse investor groups.

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

I kindly ask you to take 5-10 minutes to complete the questionnaire Your participation will not only assist in finalizing our thesis but also provide valuable insights for investors in making informed decisions Rest assured, all collected data will remain confidential and will only be presented in the synthesis report, ensuring that personal information is protected.

I sincerely appreciate your assistance and wish you continued success as investors in the Vietnamese stock market If needed, we are prepared to provide a comprehensive report based on this survey for your reference.

For further information please contact:

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

3 Marital Status: Single Married Divorced

High school and lower Under-graduate Bachelor

Under 5 years < 10 years over 10 years

6 Please estimate your average monthly income (USD) h

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 "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 từ Ông/Bà, với tư cách là nhà đầu tư cá nhân, thông qua bảng câu hỏi đính kèm dưới đây.

Kính mong Ông/Bà dành 5-10 phút để hoàn tất bảng câu hỏi, giúp chúng tôi hoàn thành 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 lĩnh vực đầu tư trên sàn chứng khoán Việt Nam Nếu quý Ông/Bà có nhu cầu, chúng tôi 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

Cấp 3 trở xuống Cao đẳng trung học chuyên nghiệp Đại học

Thạc sĩ Tiến sĩ Khác

5 Số năm kinh nghiệm làm việc

Dưới 5 năm 5-10 năm Trên 10 năm h

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 Chứng

Công Ty CP Chứng 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?

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?

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 h

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

Total 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 h

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .764 Bartlett's Test of Sphericity Approx Chi-Square 949.809

Extraction Method: Principal Axis Factoring

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

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .753 Bartlett's Test of Sphericity Approx Chi-Square 832.674

Extraction Method: Principal Axis Factoring

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

1.3 Research 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’ decision 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

3.2.2 Measure of investors’ decision making 23