INTRODUCTION
Research background
Established in 1998, the Vietnamese stock market comprises the Ho Chi Minh City Stock Exchange (HOSE) and the Ha Noi Stock Market (HAS) Initially, in 2000, it featured just 2 listed companies and 4 securities firms, but over the past decade, the number of member securities companies has surged to 682, highlighting significant growth The VN-Index, which started at 100 points in July 2000, skyrocketed to 571 points by June 2001, fueled by investor enthusiasm for quick profits despite the limited number of listed stocks However, a lack of investor knowledge and inadequate regulatory support led to a dramatic decline, with the VN-Index plummeting to a low of 139 points by March.
Investors who entered the market in 2003 faced significant financial challenges due to substantial asset losses, as noted by Huy (2010) The stock market entered a dormant phase until 2005, eventually experiencing a resurgence in 2006 The market saw a remarkable boom in the latter half of 2006, reaching 1170 points by March 2007, and maintained fluctuations around the 1000 point mark until October 2007.
The VN-Index faced a significant decline after reaching its peak, marking a challenging year for the Ho Chi Minh stock market in 2008 The downward trend continued until February 2009, when the index finally stabilized at 245 points.
Economic experts attribute the significant drop in the VN-Index to multiple factors, particularly the tightening of monetary policies.
The VN-Index experienced a significant decline due to several factors, including lending for stock investments, elevated deposit interest rates, high inflation, and a recession in the U.S economy Additionally, the lack of timely intervention by authorities contributed to this dramatic fall (Vo and Pham, 2008, p.15).
Between 2009 and early 2011, the VN-Index experienced significant volatility, peaking at 542 points in May 2010 and dropping to a low of 351 points in November 2010 Throughout 2012, the index fluctuated within a range of 400 to 500 points, showing no notable changes in amplitude.
Table 1: The highest and lowest VN-index from 2000 to 2012
Year Highest prices Lowest prices
Problem of statement
Over the past twelve years, the HOSE and the Vietnamese Stock Exchange have experienced various phases marked by significant price fluctuations, making it challenging for investors to make informed investment decisions.
The Vietnam Stock Market, particularly during the VN-Index boom in 2007, faced significant criticism from foreign media The Financial Times labeled the HOSE as a "too hot place," suggesting an overvaluation Additionally, the American Chamber of Commerce (AMCHAM) characterized the market's growth as unrealistic, referring to it as a "bubble market."
The Efficient Markets Hypothesis (EMH) fails to account for the causes of bubble markets, as it posits that stock prices reflect all available information and that capital markets are informationally efficient In contrast, behavioral finance suggests that financial markets can be informationally inefficient under certain conditions, indicating that behavioral factors may play a significant role in the dynamics of bubble markets.
Behavioral finance can be helpful in the bubble market because it is based on the psychology to explain why people buy or sell stocks (Waweru et al., 2008, p.25)
Behavioral factors such as overconfidence, representativeness, availability, herding, loss aversion, regret aversion, gambler's fallacy, and over-underreaction significantly influence investment decision-making (Ritter, 2003, p 437) Researchers view behavioral finance as a valuable framework for understanding the emotional and cognitive biases that impact investors (Waweru et al., 2008, p 25) Proponents argue that insights from social sciences, particularly psychology, can illuminate stock market behaviors, including market bubbles and crashes.
In Sweden, a study by Johnsson, Andrén, Lindblom & Platan (2002) revealed that behavioral factors significantly influence the investment decisions of both individual and institutional investors Notably, 67% of individual investors are affected by loss aversion bias, while 33% are influenced by representative bias, and 32% are impacted by regret aversion bias.
Besides European countries, behavioral finance is also discovered in Asia
Chandra and Kumar (2011) investigate the factors influencing individual graduation rates, focusing on the latest trends and methodologies in educational research Their study provides valuable insights into the determinants that affect academic success and completion.
Investor behavior in the Indian stock market significantly influences their investment decisions and outcomes Key biases include representativeness bias, which is strongly prevalent among investors, and overconfidence bias, where individuals often overestimate the accuracy of their judgments Additionally, 53.2% of investors are affected by anchoring bias, while 35.5% fall prey to gambler's fallacy in their decision-making Availability bias impacts 57.7% of investors, and loss aversion bias influences 55% of them, highlighting the various psychological factors at play in investment choices.
Vuong & Dao (2012) identify significant behavioral biases among individual investors in the Vietnamese stock market, revealing that 51.2% are affected by regret aversion bias, while 47.7% experience loss aversion bias Additionally, 43% of investors are influenced by anchoring bias, and 42.4% by both overconfidence and self-control biases Other notable biases include illusion of control (41.3%), confirmation bias (41.3%), framing bias (34.9%), conservatism bias (30.3%), representative bias (18%), and availability bias, which affects 17.4% of investors.
Behavioral finance is particularly significant in the context of the Vietnamese stock market (HOSE) for two main reasons Firstly, it remains a relatively new area of study, providing a valuable framework for understanding how investors make decisions and how these choices impact financial markets (Kim and Nofsinger, 2008, p.1) Secondly, research indicates that Asian investors, including those in Vietnam, are more prone to cognitive biases compared to individuals from other cultural backgrounds (Kim and Nofsinger, 2008, p.1).
Understanding the factors that influence Vietnamese investors' decision-making processes must include behavioral elements This research aims to enhance the body of knowledge on how behavioral finance impacts investment decisions among Vietnamese stock market investors.
Research questions
This study explores the behavioral factors that affect the investment decisions of individual investors at the HOSE, while also assessing the extent of these factors' impact on their decision-making processes The research is guided by specific questions aimed at uncovering these influences.
1/ What are the behavioral factors influencing individual investors‟ decisions at the HOSE?
2/ How strong is impact of behavioral factors on investment decision making of individual investors at the HOSE?
Research scope
Vietnam has two stock exchanges, located in Hanoi and Ho Chi Minh City, with this study focusing on the Ho Chi Minh Stock Exchange (HOSE) due to the city's status as the largest and most rapidly developing in the country The HOSE serves as a benchmark for the economy's wealth, facilitating capital raising for businesses and investors Additionally, selecting HOSE for this research allows for a more efficient approach, enabling the author to gather accurate survey information and effective interview data within time constraints.
Research method
This research utilizes both qualitative and quantitative methods, beginning with a pilot study that employs qualitative techniques and interviews to identify behavioral factors influencing investor decision-making The pilot interview, conducted with 10 experienced investors, refines the questionnaire for the official research Subsequently, a quantitative survey is distributed to 220 investors in the HOSE, starting with a pilot survey of 52 participants to test the reliability of the measurement scales After refining the questionnaire by removing irrelevant questions, the final survey is sent to an additional 170 investors.
This study formulates hypotheses grounded in established behavioral finance theories, employing a quantitative method for initial testing followed by a qualitative analysis for deeper insights To ensure the reliability of the measurement scales, SPSS software is utilized to calculate Cronbach's Alpha based on standardized items Subsequently, the validity of the measurement scales is assessed through Exploratory Factor Analysis (EFA) Finally, regression analysis is conducted to evaluate the assumptions and hypotheses.
Significance of the research
• Help investors or organizations consider and analyze these behavioral factors before making decisions for investment
To enhance business success, it is crucial to effectively utilize behavioral factors.
Structure of the study
Chapter 1, Introduction, is divided into two key sections The first section provides an overview of the Hanoi Stock Exchange (HOSE), tracing its development from inception to the current state The second section outlines the problem statement and explains the rationale behind the selection of this topic.
Chapter 2 of the Literature Review examines the behavioral factors influencing investment decision-making, as explored by previous researchers Key factors include representativeness, availability bias, gambler's fallacy, herding, regret aversion, and loss aversion The paper emphasizes the need to investigate six specific behavioral factors: representativeness, availability bias, herding, gambler's fallacy, mental accounting bias, and over-underreaction Additionally, it presents hypotheses and research models to further explore these influences.
Chapter 3, Methodology, outlines the research methods utilized, including both qualitative and quantitative approaches The qualitative method involves interviewing 10 investors to explore their behavioral factors and gather insights on the questionnaire content Following the development of reliable questionnaires, the quantitative method is applied to survey 220 investors using a five-point Likert scale ranging from "extremely disagree" to "extremely agree." The chapter also discusses the research population, sampling techniques, and the investment decision-making process analyzed through SPSS software.
Chapter 4 presents the findings from data collection and analysis, divided into three key parts Part 1 discusses the results of interviews, highlighting valuable feedback from investors regarding the questionnaires and their insights on behavioral factors Part 2 outlines the results of the pilot survey, focusing on the measurement reliability and validity through Exploratory Factor Analysis (EFA) Part 3 details the results of the official survey, which further tests measurement reliability, validity (EFA), and regression analysis, while also examining six key assumptions.
The article discusses key assumptions in regression analysis, including the independence of residuals, the presence of a linear relationship, homoscedasticity of residuals, the absence of multicollinearity, and the lack of significant outliers or influential points, as well as the normality of the residuals Following this, the findings are presented alongside a hypothesis.
Chapter 5 presents recommendations and conclusions, highlighting the impact of behavioral factors on investor decisions, such as representativeness, gambler's fallacy, and over- or under-reaction It also notes that certain biases, including availability bias, herd behavior, and mental accounting bias, do not significantly influence the investment choices of individual investors The chapter concludes with recommendations for individual investors and acknowledges the limitations of the study.
LITERATURE REVIEW
Classical finance theory versus behavioral finance
The Efficient Market Hypothesis (EMH), proposed by Fama (1998), posits that capital markets are informationally efficient, meaning they reflect all available information in asset prices In contrast, behavioral finance, as noted by Ritter (2003), argues that financial markets can be informationally inefficient under certain conditions While EMH suggests that markets are rational and capable of making unbiased forecasts despite the presence of irrational investors, behavioral finance highlights instances where market inefficiencies occur.
Stock market efficiency, as defined by Statman (1999), encompasses two key aspects: the inability to consistently outperform the market and the notion that stock prices accurately reflect fundamental characteristics, such as risk, rather than psychological factors like sentiment In contrast, behavioral finance examines the psychological processes influencing decision-making in financial markets (Talangi, 2004) Capital markets can be categorized into three types of efficiency: weak form, semi-strong form, and strong form.
The Efficient Market Hypothesis (EMH) posits that weak form efficiency indicates that a financial asset's current price incorporates all historical market information In contrast, semi-strong form efficiency suggests that asset prices also reflect all publicly available information.
Share prices adjust quickly and impartially to publicly available new information, ensuring that no excess returns can be gained from trading on this information Strong form efficiency encompasses both semi-strong and weak form efficiency, indicating that share prices incorporate all information, both public and private, yet none can yield excess returns.
Behavioral finance, pioneered by Tversky and Kahneman (1979), is characterized by its exploration of the psychological factors influencing financial decision-making Olsen (1998) describes it as a new paradigm aimed at replacing the traditional finance theories, which often overlook behavioral aspects Fromlet (2001) emphasizes that behavioral finance integrates individual behavior with market phenomena, drawing insights from both psychology and financial theory Additionally, Olsen highlights that this field focuses on applying psychological and economic principles to enhance financial decision-making.
In short, behavioral finance represents a revolution in financial theory It highlights the psychological edge of investment decision making process, in strong contradiction to the EMH.
Review some behavioral factors impacting on the process of investors’ decision
Numerous studies have explored the behavioral factors that impact investment decision-making, including Representativeness, Availability bias, Anchoring, and Overconfidence This literature review focuses on how these behavioral influences affect the investment choices of individual investors.
Representativeness refers to the extent to which a sample resembles its parent population, as noted by DeBondt and Thaler (1995) and Kahneman and Tversky (1974) This concept can lead to biases, particularly among investors who may overemphasize recent experiences while neglecting the average long-term rates, as highlighted by Ritter (2003).
Investors often infer a company's long-term growth potential based on a few positive quarters, which can significantly influence their decisions (Waweru et al., 2008, p.27) This phenomenon, known as representativeness, also includes "sample size neglect," where investors make conclusions from insufficient data (Barberis & Thaler, 2003, p.1065) In the stock market, this leads investors to favor "hot" stocks over those with poor performance, illustrating the application of representativeness in their decision-making process.
2003, p.1065) Particularly, in theVietnamese stock market, representative bias of individual investors was positively 18% in 2012 (Vuong & Quan, 2012, p.9).
Availability bias, as described by Jahanzeb, Muneer, and Rehman (2012), is a cognitive bias that leads individuals to overestimate the likelihood of events associated with memorable or vivid experiences This bias causes investors to give disproportionate importance to readily available information when making decisions.
Availability bias in stock exchanges leads investors to favor local stocks due to their familiarity and easier access to information This preference contradicts the fundamental principles of optimal portfolio diversification (Waweru et al., 2003, p.28) Furthermore, a study by Chandra, Abhiject, and Kumar, Ravinder (2011) reveals that 57.7% of investors exhibit this bias.
12 positively impacted in the Indian stock market by this factor (p.16) Meanwhile, the percentage of investors in the Vietnamese stock market who were influenced was positively only 17.4% (Vuong & Quan, 2012, p.9)
Many people mistakenly believe that a random event is less likely to occur after a previous event, a misconception linked to the "Law of Small Numbers" (Rabin, 2002; Statman, 1999) that can lead to the Gambler's Fallacy (Barberis & Thaler, 2003) In stock exchanges, this fallacy causes investors to misjudge reversal points, thinking they signal the end of favorable or unfavorable market returns (Waweru et al., 2008) Consequently, investors affected by this bias often make suboptimal choices based on prior selections (Kempf and Ruenzi, 2006) Research by Chandra, Abhiject, and Kumar, Ravinder (2011) found that 35.5% of investors in the Indian stock exchange were positively influenced by this bias.
2.2.4 Herd behavior: buying and selling decisions
Herding behavior, as defined by Chaudhary (2013), involves the actions of a large group, which can be either rational or irrational (p 88) Investors often rely on selective information rather than individual insights when making investment decisions While herding can lead to successful investments, it can also result in significant losses Tan et al (2008) highlight that during stock price fluctuations, the collective opinions of a large group can help investors maximize profits or mitigate risks (p 61) In the stock market, purchasing stocks frequently hinges on 'price momentum,' often disregarding fundamental analysis.
Herd behavior in supply and demand can lead to poor decision-making among investors (Chaudhary, 2013, p 88) Waweru et al (2008) highlight that factors such as buying, selling, investment timing, and the number of stocks positively influence investors They also note that an investor's decisions are often swayed by the actions of others Furthermore, Goodfellow, Bohl, and Gebka (2009) reveal that individual investors tend to follow the crowd more than institutional investors in their decision-making processes (p 213).
Mental accounting is defined as the process through which individuals assess and evaluate their financial transactions (Barberis & Huang, 2001, p.1248) This concept allows individuals to categorize and manage their investment portfolios into distinct items (Barberis & Thaler, 2003, p.1108; Ritter, 2003, p.431).
Rockenbach (2004) empirically examines the relationship between separate investment opportunities, highlighting its significance for arbitrage-free pricing In the Indian stock market, this relationship positively influenced 64.2% of investors' decisions in 2011 (Chandra, Abhiject, and Kumar, Ravinder, 2011) Similarly, in the Vietnamese stock exchange, this factor had a positive impact of 11% on investment decision-making in 2012 (Vuong & Quan, 2012).
Market fluctuations, along with the fundamental principles of underlying stocks and their prices, can lead to both overreactions and underreactions This behavioral aspect significantly impacts investors' decision-making processes.
Researchers DeBondt and Thaler (1985) examined the phenomenon of overreaction in financial markets, highlighting its implications for investor behavior and market efficiency.
Research by Lai (2001) indicates that news significantly impacts investors' strategies and positively influences their investment decisions Additionally, Barberis, Shleifer, and Vishny (1998) highlight that stock prices tend to under-react to news like earnings announcements while over-reacting to consecutive positive or negative news.
Vishny (1994) discovered that investors tend to overreact to stocks with strong past performance, leading to lower returns compared to those with poor past performance that are expected to continue underperforming Additionally, DeBondt and Thaler (1995) highlighted that investors often exhibit over- or under-reaction in response to stock price fluctuations and news events.
Waweru et al (2008) identify several behavioral factors that positively influence investors' decision-making, such as price fluctuations, market information, historical stock trends, customer preferences, overreactions to price changes, and the fundamentals of the underlying stocks.
Suggested research model
The paper studies behavioral factors of investors in HOSE, Vietnam
Therefore it is mostly based on combining the framework of researches in Asia such as Vuong & Dao (2012) in the HOSE and results of research by Chandra and Kumar
The paper examines the Indian Stock Exchange, highlighting the relevance of comparing behavioral factors between India and Vietnam, as both countries are located in Asia This geographical context enhances the validity of the findings The comparison aids in identifying appropriate behavioral factors for study Notably, as shown in Table 2, 53.2% of Indian investors exhibit the gambler's fallacy, a behavioral factor that has not yet been addressed by Vietnamese researchers.
The representativeness factor in the Indian stock market has not been thoroughly researched, unlike in the Vietnamese stock exchange This paper aims to explore these factors further Additionally, while some behavioral factors have been studied in both markets, there are significant differences in their impact levels For instance, availability bias affects 57.7% of Indian investors, highlighting the contrasting influences in the two markets.
In the 17 market, the prevalence of mental accounting bias was notably different between Vietnamese and Indian investors, with only 11% of Vietnamese investors affected compared to 64.2% of their Indian counterparts This disparity highlights the need for further research on these factors in this paper.
Table 2.1: Results of behavioral factors affecting investment decision making of individual investors
Behavioral factors Percentage of affected
Percentage of affected Vietnamese investors (Vuong & Dao, 2012)
Regret aversion bias Some evidences 51,2
Herd behavior: buying and selling decisions
The article discusses the topic of reactions, emphasizing the importance of understanding both over and under reactions It highlights the significance of not being overly concerned with minor issues while focusing on the main objectives Additionally, it mentions the availability of resources for downloading the latest thesis materials and provides contact information for further inquiries.
Vuong and Dao (2012) identify several factors influencing individual investors' decision-making, yet additional factors warrant further investigation Vietnamese experts, including Ngo (2010) and Ho (2007), highlight four key behavioral factors prevalent in the Vietnamese stock exchange: overconfidence, availability bias, herding behavior, and loss aversion bias While Vuong and Dao (2012) discuss these factors, they omit herding behavior Therefore, drawing on Ngo (2010), this paper aims to explore the behavioral factor of herding, proposing the following hypothesis:
H1: Herding behavior positively impacts the investment decisions of individual investors at the HOSE
Ho (2007) identified several behavioral factors influencing the Vietnamese market, such as availability bias, representativeness, anchoring, gambler's fallacy, overconfidence, mental accounting, and herding behavior in buying and selling decisions Similarly, Vuong and Dao (2012) highlighted these behavioral factors, although they did not include gambler's fallacy and over and under reaction.
The paper hence suggests studying two more factors including over and under- reaction and gambler‟s fallacy Hypothesis 2 and 3 are proposed below:
H2: Over and Under-reaction positively affects the investment decisions of individual investors at the HOSE
H3: Gambler’s fallacy positively influences the investment decisions of individual investors at the HOSE
Table 2 highlights significant behavioral factors, including mental accounting and representativeness, that influence investment decision-making Notably, there is a substantial difference in these factors between Vietnamese and Indian investors, with 64.2% of Vietnamese investors exhibiting distinct investment behaviors.
A total of 19% of Indian investors are influenced by the concept of mental accounting, compared to only 11% of Vietnamese investors This significant disparity prompts a reevaluation of two key factors, leading to the examination of hypothesis 4.
H4: Mental accounting bias positively impacts the investment decisions of individual investors at the HOSE
H5: Representativeness bias positively influences the investment decisions of individual investors at the HOSE
Research by Vuong and Dao (2012) indicates that availability bias accounts for only 17.4% of investor behavior, suggesting its minimal impact on behavioral finance However, Vietnamese experts Ngo (2010) and Ho (2007) highlight that this bias significantly influences investor psychology in the Vietnamese stock market.
Therefore the paper will study this factor more to consider whether or not this factor has much effect on investment decision-making And hypothesis 6 is presented below:
H6: Availability bias positively influences the investment decisions of individual investors at the HOSE
This paper examines the Ho Chi Minh Stock Exchange by integrating behavioral factors from the research of Vuong and Dao (2012) alongside insights from Vietnamese experts and studies by Waweru et al (2008) and Chaudhary (2013).
This study, based on the works of Ho (2007) and Ngo (2010), examines the key behavioral factors influencing individual investors' decision-making on the Ho Chi Minh Stock Exchange (HOSE) The factors analyzed include herding behavior, overreaction and underreaction, gambler's fallacy, mental accounting bias, representativeness, and availability bias.
Research model
Through presented above, the research mode is proposed as follows:
A chart of research model is proposed by the author
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RESEARCH METHODOLOGY
Research design
The study utilized both qualitative and quantitative methods, beginning with a pilot study that focused on qualitative research through in-depth interviews This approach enabled the author to identify behavioral factors influencing investors' decision-making, which informed the subsequent official research The pilot interviews, conducted with 10 experienced investors in stock investment, revealed errors and highlighted unsuitable questions, leading to the refinement of the questionnaire for the official study The finalized questionnaires, available in both English and Vietnamese, are detailed in Appendix 5.1, which outlines the qualitative research design and interview questions.
The official research employed quantitative methods, utilizing selected questionnaires to assess the agreement levels of 220 investors in the HOSE Initially, a pilot survey was conducted with 52 investors to evaluate the reliability of the measurement scales The study aimed to ensure the accuracy and consistency of the scales used in the research.
SPSS software to test Cronbach‟s Alpha based on standardized items After deleting some questionnaires that were not reliable, the author distributed the questionnaire to
170 more investors After collecting 220 questionnaires from investors, the paper tested scales measurement reliability and validity: factor analysis – EFA (N"0)
Finally, the paper used SPSS software for regression to test six assumptions
The five Ho Chi Minh Stock exchanges with the highest trading volumes are Dong A (DAS), Ho Chi Minh (HSC), Saigon (SSI), FPT (FPTS), and Viet Capital (BVSC) For insights, two individuals from each of these stock exchanges will be randomly selected for interviews.
Define reseach problem literature review research model qualitative research
(in- depth interview, n) proposed research model and adjusted questionnaires pilot survey
(formal questionnaires, nP) test of scales measurement reliability, NP test of Cronbach's alpha survey questionnaries, (N"0) test of scales measurement reliability (N"0)
This article discusses the testing of Cronbach's alpha to evaluate the reliability of measurement scales, emphasizing the importance of validity through factor analysis and exploratory factor analysis (EFA) It also covers regression analysis and the examination of six key assumptions The discussion includes recommendations and concludes with insights relevant to the field For further details, readers can download the latest thesis document.
According to Hatcher (1994), the minimum sample size for exploratory factor analysis (EFA) should be either five times the number of variables or at least 100, which in this case requires a sample size of 205 for the 41 questions in the study With a sample size of 220, this condition is satisfied Additionally, Tabachnick & Fidell (2011) state that for regression analysis, the sample size must exceed \(N > R + 8k\), where \(k\) is the number of independent variables In this study, the requirement is \(N > 52 + 8 \times 7 = 108\) Consequently, 220 questionnaires were distributed to private investors across various stock exchanges to gather their responses, fulfilling the necessary sample size criteria.
Adjusted research model
Qualitative research indicates that behavioral factors significantly influence investor decisions, particularly representative bias, availability bias, gambler's fallacy, and over- and under-reaction Investors tend to favor blue chip stocks due to representative bias, especially in the context of the current financial difficulties faced by the global economy and Vietnam This cautious approach leads them to avoid poorly performing stocks Availability bias reveals a preference for local stocks over foreign ones, largely due to regulatory challenges and a lack of confidence in navigating foreign trading rules Investors also prefer stocks they are familiar with, avoiding unfamiliar options The gambler's fallacy further complicates their decision-making process, as they may misinterpret past performance trends when making investment choices.
With a decade of stock investment experience, many investors have developed the ability to identify reversal points that inform their decisions Their predictions have often proven accurate, contributing to their success Furthermore, they commonly believe that when a stock's price declines over several sessions, it presents a compelling buying opportunity, as they feel it is unlikely to drop further.
Investors were fortunate in their decisions, as they emphasized the importance of carefully analyzing stock price fluctuations before making investments In the current economic climate, it is crucial to monitor these price changes closely Additionally, they acknowledged the significance of market information, particularly in the sensitive Vietnamese market, which requires frequent reviews Ignoring such information could lead to significant challenges, including potential investment losses.
This paper identifies six behavioral factors for research: representative bias, availability bias, gambler's fallacy, over-underreaction, herd behavior, and mental accounting bias Most investors expressed a desire for further exploration of these factors, noting that herd behavior and mental accounting bias were observed at a moderate level.
It means they do not support but they do not reject them either As a result, the research model after qualitative research and hypotheses do not change against at first
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Scales measurement design
The questionnaires are developed from the practical research of Waweru et al (2008) and tailored to fit the Vietnamese stock market Six behavioral factors are examined, including representative bias, availability bias, herd behavior, mental accounting bias, gambler's fallacy, and over- and underreaction A summary of the adjustments made to the measured questions based on interviews is provided in Appendix 5.2.
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3.3.1 Scales measurement of Representative bias
The variable of Representative bias, denoted as REP, is measured through three key items, as outlined by Waweru et al (2008) One significant item states, "You buy 'hot stocks' and avoid stocks that have performed poorly in the recent past." However, many investors found this question challenging to comprehend clearly.
The term "hot stock" has become too vague for the current stock markets in Vietnam To enhance clarity, it has been replaced with more specific categories such as "blue chip stocks," "midcap stocks," and "penny stocks." Consequently, the initial question will be removed and revised to include three new inquiries: Question 1: Do you prefer buying "blue chip stocks"? Question 2:
You prefer buying „midcap stocks‟ Question 3: „You prefer buying „penny stocks‟
Additionally, it should have question 4 that was separated from the first question:
Investors tend to avoid purchasing stocks that have demonstrated poor performance in the recent past However, analyzing a select group of representative stocks is essential for making informed investment decisions across all desired stocks The term "all" was deemed insufficient, prompting a revision to specify "all other stocks in the same industrial field." Consequently, the question was rephrased to state, "You analyze the effective operation of some representative companies to make investment decisions for all other stocks in the same industrial field." This highlights the importance of representative bias in investment analysis.
Table 3.1 outlines various aspects of representativeness in stock preferences Investors exhibit representative bias by favoring "blue chip stocks," "midcap stocks," or "penny stocks." They tend to avoid stocks with poor recent performance and often analyze the effective operations of representative companies to inform their investment decisions across the market Additionally, there is a tendency to select stocks that are representative of the VN30 index within the stock market.
3.3.2 Scales measurement of mental accounting bias
Mental accounting bias, as identified by Waweru et al (2008), is characterized by the tendency of investors to treat each component of their investment portfolio independently This behavior arises from the distinct features, business types, and industry sectors of each stock Consequently, when making buy or sell decisions, investors often overlook the interconnections between various investment options, focusing instead on isolated factors rather than considering the overall portfolio.
Table 3.2: Items of mental accounting bias
Men often exhibit mental accounting bias, treating each component of their investment portfolio as a separate entity This leads to a disregard for the interconnections among various investment opportunities Consequently, they tend to evaluate individual factors without considering the overall performance of the entire portfolio.
3.3.3 Scales measurement of gambler’s fallacy
The gambler's fallacy, represented by the variable GAM, is measured through four key items as identified by Waweru et al (2008) Investors with over a decade of experience in the HOSE stock market often believe they can predict reversal points in stock prices They tend to buy stocks that have decreased in value, convinced that prices will not fall further, and similarly, they sell stocks that have risen significantly, believing they will not increase any more This behavior aligns with the fluctuation rule and the technical analysis pattern known as "shoulder-head-shoulder."
Most participants disagreed with the question regarding investing in stocks without both fundamental and technical analysis, as the use of "or" created confusion They emphasized that modern investors must possess knowledge of both analyses to succeed Consequently, they deemed the original question less significant and recommended replacing it with a more relevant one: "Do you focus entirely on economic factors such as GDP and CPI before making investment decisions?"
The prevalence of rumors in the stock market significantly influences investors' decisions, especially in a sensitive market environment This raises the critical question: do investors rely on rumors when deciding to buy or sell stocks? This phenomenon is closely related to the gambler's fallacy, which is rooted in belief and chance Consequently, investors are prompted to consider additional questions regarding the impact of these rumors on their investment strategies.
„You decide to buy or sell stocks based on your forecast‟ and „You believe you are often lucky to invest in stocks‟ Items of gambler‟s fallacy were shown below:
Table 3.3: Items of gambler’s fallacy
The Gambler's Fallacy can significantly influence investment decisions Investors may choose to buy a stock after its price has fallen for several sessions, believing it cannot decline further Conversely, they might sell a stock that has risen multiple times, thinking it is unlikely to increase any more Many investors also prioritize economic indicators like GDP and CPI before making investment choices Additionally, some rely on rumors to guide their buying or selling actions, while others may feel a sense of luck in their stock investments Ultimately, decisions are often based on personal forecasts rather than objective analysis.
3.3.4 Scales measurement of availability bias
Variable of availability bias is signed as AVAIL According to Waweru et al
In 2008, three key items were identified to measure this variable The interviewees, each with over 10 years of experience in stock investment, provided valuable insights into the subject matter.
Many investors possess a solid understanding of the stock market, yet they remain hesitant to invest in foreign stock exchanges Instead, they tend to favor local stocks due to the greater availability of information regarding these investments.
Individuals often view information from close friends and relatives as a trustworthy source for their investment decisions, especially when they are busy This reliance on personal connections provides valuable insights that enhance their confidence in investing, particularly in stocks of companies they are already familiar with Consequently, the effects of availability bias remain evident in their decision-making process.
Table 3.4: Items of availability bias
Availability bias influences your investment choices, leading you to favor local stocks over international ones due to the greater accessibility of information You often rely on insights from close friends and relatives as trustworthy references for your investment decisions Additionally, you are inclined to invest in stocks of companies with which you have personal experience, either through previous employment or familiarity.
3.3.5 Scales measurement of herd behavior
Variable of herd behavior is signed as HERD According to Waweru et al
Data analysis approach
3.4.1 Test of scales measurement reliability
Cronbach’s Alpha Test is utilized to assess the reliability of measurement scales using 5-point Likert scales This test evaluates the consistency of responses from a specific sample of participants across various questions or items Furthermore, it aids in predicting the reliability of respondents' answers to the measurements (Helms, Henze, Sass &).
Cronbach's alpha is a widely used measure of reliability in behavioral research, as noted by Liu, Wu, and Zumbo (2010) This study effectively employs Cronbach's alpha due to its use of a 5-point Likert scale in the questionnaire, making it particularly relevant to the field of behavioral finance.
This study utilizes Cronbach's alpha to assess the reliability of measurement scales, including factors derived from factor analysis.
Cronbach's alpha should ideally be at least 0.7 to ensure reliable measurements, although some statisticians accept a value over 0.6 (Shelby, 2011) It is also crucial to consider corrected item-total correlations, which should be 0.3 or higher, as they indicate the correlation of individual items with the total score (Shelby, 2011) This research adopts a Cronbach's alpha range of 0.7 to 0.8 and requires corrected item-total correlations to be above 0.3, given that the measurements of financial behavior are new to stockholders of the Ho Chi Minh Stock Exchange Additionally, the accepted significance level for the F-test in the Cronbach's alpha technique is set at 0.05 or lower.
Cronbach‟s alpha test is finished by SPSS software
This study employs Exploratory Factor Analysis (EFA) to identify the underlying factors related to the behavioral finance variables in the questionnaire (questions 12 to 43) EFA is instrumental in eliminating items that do not meet analytical criteria (O'Brien, 2007, p.142) and is used to test the hypotheses outlined in the research model from Chapter 3 Key criteria for the EFA include factor loadings, the Kaiser-Meyer-Olkin (KMO) measure, total variance explained, and eigenvalues Factor loadings, which represent the correlation of each item with its corresponding factor, should exceed 0.5 for a sample size of 100 to ensure practical significance (Hair et al., 1998, p.111) The KMO statistic assesses the appropriateness of EFA for the data, with acceptable values ranging from 0.5 to 1.0.
0.005) to make sure that factor analysis is suitable for the data (Ali, Zairi & Mahat,
Total variance explained is crucial for determining the number of factors to retain in analysis, with a recommendation that it exceeds 50% (Hair et al., 1998) Factors can be retained until the last one accounts for a minimal portion of the explained variance Additionally, the eigenvalue represents the variance attributed to each factor, indicating how much variance in all items (variables) is explained by that specific factor.
Eigen-value should be greater than 1 because Eigen-value is less than 1 means that information explained by the factor is less than by a single item (Leech, Barrett &
The Exploratory Factor Analysis (EFA) is conducted using SPSS software, as noted by Morgan (2005, p.82).
DATA ANALYSIS AND FINDINGS
Data description
After collecting the data, the paper reported data background; It was initially concerned with gender: most investors were male, 75.46% with females at 24.54%
Secondly, it related to age: most investors were between 26 and 45 years old
A detailed analysis of the data reveals that 57.27% of respondents were aged between 26 and 35, while 42.73% fell within the 36 to 45 age range Additionally, the majority of investors reported having less than 5 years of work experience, accounting for 10.09% of the total respondents.
10 years accounting for 47.27%, and over 10 years work experience with 33.64%
The average monthly income of investors shows a significant distribution, with 48.18% earning between 12 to 20 million VND, 41.82% earning between 6 to 12 million VND, and around 10% earning over 20 million VND.
A significant portion of investors, 35.45%, have been investing in stocks for 5 to 10 years, while 39.09% have done so for 3 to 5 years, and only 11.82% for 1 to 3 years Notably, 85.45% of these investors have received formal training in stock market investment, leaving just 14.55% without any training Regarding their investments in the HOSE last year, 30.91% of investors allocated between 60 to 120 million VND in stocks.
A total of 36 investors contributed amounts ranging from 120 to 300 million VND Among them, 14.55% invested between 600 and 900 million VND, while 11.82% of investors allocated over 900 million VND For a detailed summary of the data background, please refer to Appendix 5.4.
Factor analysis of behavioral variables influencing the individual investment
A pilot survey was conducted to assess the reliability of measurement scales, aiming for Cronbach's alpha values between 0.7 and 0.8 to ensure measurement reliability The independent variables were represented by behavioral factor questions ranging from X12 to X38, while the dependent variables consisted of questions Y39 to Y41, designed to evaluate investors' perceptions of their own investment decisions.
Table 4.1: Idependent variables, dependent variables and items
1.Representativeness rep1, rep2, rep3, rep4, rep5, rep6 2.Mental accounting bias men1, men2, men3
3.Gambler‟s fallacy gam1, gam2, gam3, gam4, gam5, gam6 and gam7
4.Availability bias avail1, avail2 and avail3
5.Herding behavior herd1, herd2 and herd3
6.Over and under creation creat1, creat2 and creat3, creat4, creat5 and creat6
Investment decision return1, return2, return3
4.2.2 Results of Cronbach’s alpha analysis of pilot survey (NR)
The study evaluated the reliability of measurement scales, revealing that the first independent variable, representativeness, had a low Cronbach's alpha of rep3 (1 => acceptable Rotation sums of squared loadings
Component 1 rep1, rep2, rep3, rep4, rep5 Component 2 herd3, herd2, creat5, herd1 herd1 deleted
Component 3 creat2, creat3, creat4 Component 4 return1, return2, return 3 Component 5 gam1,gam2, gam3, gam4, gam5 gam1 and gam4 deleted Component 6 avail1, avail2, avail3, creat1 creat1 and avail2 deleted Component 7 men1, men2, men3 tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
After deleting items including herd1, gam1, gam4, avail2 and creat1, results presented that KMO = 0.544>0.5, Bartlett – Significant level was 0.000 (< 0.05);
The factor loadings were all greater than or equal to 0.5, indicating strong relationships among the items The Rotate Factor matrix revealed that each item had loadings exceeding 0.3, leading to their acceptance The total variance explained table illustrated the distribution of variance across 22 potential factors, with seven factors showing initial eigenvalues of 1.401 or higher, meeting the common criterion for usefulness Additionally, the rotation sums of squared loadings reached 71.362%, surpassing the 50% threshold, thus validating the scale measurement Details are presented in Table 4.5 below.
Table 4.5: Summary of KMO and Bartlett’s Test, total variance explained and rotated component matrix (EFA time 2)
Initial Eigenvalues 1.401 (component 7) >1 => met requirement Rotation sums of squared loadings
The study utilized SPSS software to identify seven components, as illustrated in Table 4.6 All item loadings exceeded 0.5, confirming their suitability for grouping into these components Following the exploratory factor analysis, the components were employed in regression analysis Additional details regarding the Cronbach's alpha analysis (time 2) can be found in Appendix 5.5.
Table 4.6: Rotated component matrix (EFA time 2)
Component Items cluster Factor loadings
Regression analysis
Regression analysis, as described by Leech, Barrett, and Morgan (2005), is a statistical method used to examine the relationships between variables The authors gathered data on various variables and applied regression techniques to assess how independent variables influence dependent variables They utilized adjusted R², which is typically lower than unadjusted R², to indicate the percentage of variance in the dependent variable that can be predicted from the independent variables, with adjustments influenced by effect size and sample size Additionally, the authors employed the F-test to evaluate the significance of the model, presenting hypotheses H0 and H1 accordingly.
H0: β1 = β2 = … = βk = 0 (no linear relationship) H1: at least one βi ≠ 0 (at least one independent variable affects Y)
If the p-value is less than 0.05, we reject the null hypothesis (H0) and accept the alternative hypothesis (H1), concluding that the research model is suitable.
The author discusses the standardized beta coefficient, which is interpreted similarly to the correlation coefficient or factor weights A significant beta is indicated when the t value is less than 0.05, allowing for the assessment of the impact of independent variables on dependent variables, whether positive or negative Additionally, the author highlights the importance of collinearity statistics, specifically VIF and Tolerance Tolerance, calculated as 1/VIF, helps identify multicollinearity issues; a low Tolerance value (less than 1 - R²) suggests potential multicollinearity problems.
The author categorized items with similar components and assigned them new names Component 1, which includes rep1, rep2, rep3, rep4, and rep5, was renamed REP Component 2 grouped creat2, creat3, and creat4 under the name CREAT Additionally, items herd3, herd2, and creat5 were classified as HERD, while return1, return2, and return3 were grouped as RETURN The items gam2, gam3, and gam5 were designated as GAM, and men1, men2, and men3 were categorized as MEN Finally, the items avail1 and avail3 were grouped together as AVAIL.
Table 4.7: summary of grouped items
Component 1 rep1, rep2, rep3, rep4, rep5 REP Component 2 creat2, creat3, creat4 CREAT
Component 3 herd3, herd2, creat5 HERD
Component 4 return1, return2, return3 RETURN
Component 5 gam2, gam3, gam5 GAM
Component 6 men1, men2, men3 MEN
This study examines the relationship between several independent variables—CREAT, MEN, AVAIL, GAM, REP, and HERD—and the dependent variable RETURN Utilizing SPSS software for analysis, the descriptive statistics revealed the following means: REP at 15.55, CREAT at 22.28, HERD at 10.37, MEN at 9.51, GAM at 9.28, RETURN at 9.27, and AVAIL at 6.99 Detailed results are presented in Table 4.8.
44 descriptive statistics including Means, Standard Deviations, and Intercorrelations of RETURN and predictor variables as follows:
Table 4.8: Means, Standard Deviations, and Intercorrelations of RETURN and
As mentioned above, Table 4.8 presented that pearson correlation between the dependent variable (RETURN) and independent variables (REP, MEN, GAM,
AVAIL, HERD and CREAT) In general, there were low relationships between the dependent variable and independent variables, and among the predictor variables
Independent variables were positively correlated with RETURN including REP (22.8%), MEN (6.9%), CREAT (19.4%) Especially, AVAIL and HERD had too low correlations with RETURN GAM (10.8%) was negatively correlated with RETURN
The paper continued to analyze significant levels of independent variables
The analysis revealed significant correlations among the variables, with REP showing a strong relationship with RETURN (p
