THE OVERVIEW OF THE RESEARCH
REASONS OF SELECTING TOPIC OF THESIS
Vietnam's stock market, which began operations in August 2000, has established a vital channel for direct capital mobilization, significantly aiding the country's economic development However, after more than 18 years of operation, it remains relatively young compared to more established markets in Thailand, Singapore, and Malaysia.
During the stock market boom in Vietnam in 2006 and 2007, investors flocked to this investment avenue for profit; however, the economic crisis of 2008 severely impacted the operations of listed companies, resulting in a significant decline in share prices This downturn diminished investor demand for securities, making the stock market a less appealing option compared to previous years Both domestic and foreign investors became increasingly cautious, leading to a decline in interest and many exiting the market due to substantial losses from price volatility Those who remained exhibited a lack of trust in listed companies, compounded by a general deficiency in knowledge, experience, and information necessary for evaluating and selecting quality stocks for investment.
The stock market is currently experiencing a gradual recovery, prompting investors to adopt a more cautious approach, often seeking guidance from expert consultants before entering the market According to the ACBS Financial Report of 2017, ACBS has reestablished itself among the top 10 companies in Vietnam's stock market, boasting total assets valued at VND 2,723 billion On the Hanoi Stock Exchange (HNX), ACBS ranks in the top 5 with a market share of 6.08% in the fourth quarter, while it holds the sixth position overall for the year In the Ho Chi Minh Stock Exchange (HoSE), ACBS commands a market share of 3.13%, securing the seventh position.
In the fourth quarter of 2017, I chose to focus my bachelor thesis on "Measuring the factors affecting stock investment decisions of individual investors at Asia Commercial Joint Stock Bank Securities Limited Company."
BACKGROUND TO THE RESEARCH
Numerous research projects have explored various aspects of the stock market and individual investment strategies, highlighting its evolution as a key modern investment channel in today's integrated economy Notable research topics focus on investor stock options, providing valuable insights into market development and investment trends.
Obenberger and his partner (1994) conducted a study titled "Factors Influencing Individual Investor Behaviour," which examines the financial factors affecting individual investor decisions Their research model includes variables such as personal information, stock details, market conditions, and personal financial needs, emphasizing that investors do not rely on a single integrated approach The study utilized data gathered from a questionnaire distributed to individual investors holding stocks in Fortune 500 companies.
Nofsinger and his partner (2002) explored the "Psychological Biases of Investors" through qualitative research, revealing that investor behavior frequently diverges from logical reasoning due to various psychological factors Their findings identified several biases, including representativeness, regret, disposition, familiarity, worry, anchoring, self-attribution, and trend-chasing, all of which significantly influence investment decisions However, the study primarily focused on theoretical analysis and did not include empirical surveys to validate these claims.
- Sun and his partner (2006) with their research "The influence of investor psychology on disposition effect" using the Exploratory
This article explores Factor Analysis (EFA) and investor psychology, focusing on four key factors: overconfidence, regret aversion, mental accounting, and self-control Conducted within the framework of behavioral finance, the study highlights how psychological variables significantly impact investor behavior, laying the groundwork for future research in this field.
- Rajashekar and his partners (2016) with their research "Factors influencing investment decisions of retail investors" using the
This study utilizes Exploratory Factor Analysis (EFA) and behavioral finance theory to investigate the factors that influence individual investors' decisions The influencing factors are categorized into five groups: demographic, economic, psychological, social, and organizational It provides a comprehensive overview of the determinants of investment, emphasizing both psychological influences and fundamental aspects that impact investor behavior.
Thuy Dung and her colleagues conducted research in 2008 titled “Survey and Measurement of Psychological Factors Influencing Individual Investors' Stock Investment Decisions,” utilizing exploratory factor analysis (EFA) to identify key psychological influences on investment behavior.
The Exploratory Factor Analysis model identifies four key groups of factors impacting expectations, mood, overconfidence, and crowd psychology However, the original authors recognized seven distinct groups, including a risk-averse factor and professional opinions, which may not receive adequate attention from investors during interviews Consequently, the analysis results may not be fully accurate, highlighting the limitations of exploratory research in this context.
4 basis and only concentrated on Ho Chi Minh’s stock exchange, so there are certain limitations
In his 2015 study titled “Analysis of Factors Influencing Individual Investors' Stock Investment Decisions at Stock Exchanges in Da Nang City,” Ngo Duc Chien employed descriptive statistics and empirical models to uncover key insights The research revealed that factors such as return rates, business size, duration of activity, market trends, and investment experience positively influence investment decisions Conversely, investment risk negatively impacts these decisions, with investors in Da Nang City exhibiting heightened caution due to psychological factors that often overshadow other considerations.
In their 2016 study, Duc Hoang and his colleagues explored the factors influencing stock investor decisions through a behavioral finance model, identifying investment efficiency (HIEUQUA) as a dependent variable influenced by five independent variables: anchor (X1), confidence (X2), herd behavior (X3), loss aversion (X4), and reaction (X5) Their findings ranked the positive effects of these influences, with confidence (X2) and anchor (X1) having the most significant impact, while loss aversion (X4) and herd behavior (X3) were found to exert a negative influence The study concluded that investors often act irrationally, swayed by psychological factors rather than purely financial considerations While behavioral finance provides a suitable framework for understanding investor behavior, the research suggests that it should be complemented with insights from corporate finance and the investment industry for a more comprehensive analysis.
In their 2016 study titled "Factors Influencing Investor Decisions in the Stock Market," Hoang Quan and his partner employed an in-depth interview method targeting securities professionals, utilizing the Likert scale to gather and analyze data effectively.
This study employs a five-level correlation regression analysis to identify the factors influencing stock investment demand among investors in Vietnam Utilizing independent variables derived from common behavioral biases identified by Amar Kumar Chaudhary (2013) and Waweru (2008), the analysis focuses on variables X1 (MONEO), X2 (TUTIN), X3 (BAYDAN), X4 (THUALO), and X5 (PHANUNG) The findings provide valuable insights for businesses, enabling them to strategically develop resources and services that align with investor preferences.
Ngoc Thu's 2018 research, "Factors Influencing Individual Investors' Stock Selection at Ho Chi Minh City Securities Corporation," reveals that psychological factors significantly affect stock choices among investors The study employs four primary methodologies: descriptive statistics, scale reliability assessment, exploratory factor analysis (EFA), and regression analysis, to evaluate how these factors impact individual stock decisions at HSC Additionally, it emphasizes the importance of technical and fundamental analysis methods to attract more individual investors and provide them with a comprehensive understanding of their investment strategies, enabling them to make informed decisions about when to exit their positions and improve their stock selection criteria.
OBJECTIVES OF THE RESEARCH
The main objective of this study is to measure the factors affecting stock investment decisions of individual investors at ACBS In addition, the study included the following specific objectives:
- The system of theoretical bases involved in the decision making of individual investors
- General introduction of securities trading and brokerage activities at ACBS
- Analyzing the factors influencing the decision to select stocks of individual investors at ACBS
- Recommending solutions to increase efficiency in making decision stock investment.
SUBJECTS AND SCOPE OF THE RESEARCH
This article explores the various factors that influence individual investors' stock selection decisions at ACBS The author specifically examines how these factors affect the choices made by investors, providing a detailed analysis of their impact on stock selection.
- Scope of study: Individual investors are currently trading at ACBS
- Research content: Focus on the use of the Exploratory Factor Analysis (EFA) method to determine the factors that influence the decision to stock investment of investors at ACBS
METHODOLOGY
- Primary data: Data are collected through questionnaires (online questionnaires via email and paper sheets)
- Secondary data: published research reports, documents, journals, Internet such as banking technology magazine
Data will be collected, analyzed and compared according to the criteria defined in the study
To effectively process survey data, Excel software is utilized to aggregate information based on specific evaluation criteria Subsequently, the SPSS analysis tool is employed to assess and understand the impact of various factors on stock investment decisions made by individual investors at ACBS.
- Analyzing the opinions of experts: By synthesizing experts' opinions according to each research content
A comparative analysis and synthesis of relevant data is conducted to evaluate and inform investment decisions for stock selection at ACBS.
THE PRACTICAL SIGNIFICANCE OF THE RESEARCH
Measure the impact of factors on stock investment decisions of individual investors at Asia Commercial joint stock Bank Securities limited company (ACBS), enabling research and practical application
Serving in advising and recommending to investors of brokerage division at ACBS to help individual investors improve their investment efficiency.
STRUCTURE OF DISSERTATION
Apart from the introduction, conclusions, list of abbreviations, list of figures, list of tables, references and list of appendices, the thesis is composed of five main chapters:
Chapter 1: The overview of the research
Chapter.2:.Theoretical background on stock investment decisions of individual investors and an overview of securities trading activities at Asia Commercial joint stock Bank Securities limited company (ACBS)
Chapter 3: Surveying the impact of factors according to stock investment decisions of individual investors at Asia Commercial joint stock Bank
Chapter.5:.Conclusions and recommendations for improving stock investment decisions of individual investors at Asia Commercial joint stock Bank Securities limited company (ACBS)
Chapter 1 of this thesis gives an overview of the reason of selecting topic as well as a background, objectives, subjects and scope of the research, methodology and the practical significance of the research to provide an overview of the topic
“Measuring factors affecting stock investment decisions of individual investors at Asia Commercial joint stock Bank Securities limited company”
THEORETICAL BACKGROUND ON STOCK INVESTMENT
A summary of stock and stock investment
According to Le Manh Hung and his partners (2015) in the book Corporate Finance, supposes "Stocks are a deed of ownership of a joint stock company"
As stipulated by Vietnamese law, the law on enterprises (2014) states that
"Stocks are certificates issued by a stockholding company, book accounting entries or electronic data certifying the ownership of one or more stocks of that company"
In a joint stock company, the authorized capital is divided into equal parts known as stocks, which are purchased by individuals referred to as stockholders Each stockholder receives a certificate that signifies their ownership of these stocks, with their ownership stake corresponding to the number of stocks they hold Stocks serve as financial instruments with no expiration date, representing a long-term investment in the company.
The Securities Law (2010) defines stocks as securities that certify the legitimate rights and interests of their owners in a portion of the debt capital of the issuing organization.
Stocks can be classified in various ways, but the most prevalent method categorizes them based on stockholder interests into two main types: common stocks and preferred stocks.
Common stocks represent shares in a company that can be volatile, with dividends paid annually to shareholders based on the company's performance Shareholders also have the right to elect members to the company's governing board A key feature of common stock is that ownership increases with the number of shares held.
Preferred stocks are fixed-income securities that offer a consistent interest rate, independent of a company's profitability These stocks have priority over common stockholders for dividend payments and asset distribution during profitable operations or liquidation However, preferred stockholders do not possess voting rights in the company's board of directors.
Stocks represent an investor's ownership stake in a company, signifying their capital contribution and status as a stockholder When an individual purchases shares, they effectively become a part of the issuing enterprise, gaining rights and potential benefits associated with their investment.
Stock investment involves investors allocating their capital to trade and exchange stocks with the aim of achieving specific profit goals Future profits can arise from price fluctuations, annual dividend payments, and increases in share value, all of which are influenced by the company's strong performance.
According to Ho Thi Huyen Trang (2012), the objective of stock investment includes the following types of objectives:
Capital safety management is the primary goal for investors in the market, as it focuses on protecting their investments while also seeking to generate profits.
- Increased capital: the most important target of investors, especially retail investors, is the difference between buying and selling prices
By buying stocks at low prices and selling at higher prices
- Income increase: unlike the increase in capital, increase in income is the annual increase in dividends when the company stocks the profit for stockholders
According to Nguyen Quang Khai (2016), stock investment activity has some following characteristics:
Investment capital in the stock market encompasses both the financial capacity of investors and the broader resources they possess, including human resources, financial assets, and accumulated experience This comprehensive understanding of investment capital is crucial for investors as they analyze and make informed decisions about stock options.
Stock investment influences an investor's economy through two primary channels: growth and income Growth refers to the fluctuations in stock prices driven by market conditions, trading volume, business performance, and investor sentiment In contrast, income is generated through fixed dividends that investors receive, which are contingent on the company's performance.
Venture capital carries significant risks, as investors may lose their capital if a business fails Therefore, it's crucial for investors to clearly define their investment objectives, as these can vary widely in terms of short-term or long-term goals, investment strategies, risk tolerance, and overall financial circumstances Understanding these factors is essential when making stock purchase decisions in the venture capital market.
- Lastly, stock market’s investment is not fixed but will fluctuate through every day that the most important factor affecting the market
- is the supply and demand of investors Sometimes, the market will follow the will of the majority of investors
Stock investment is inherently challenging and involves various risks, as noted by Ho Thi Huyen Trang (2012) Key risks include interest rate fluctuations, issuer default, exchange rate volatility, and political instability.
Theory of investment making decisions and stock investment
Based on studies of fundamental analysis by Fontanilla and his partners
Fundamental analysis is defined as the process of evaluating an enterprise by analyzing various business-related information to determine its intrinsic and market value This analysis incorporates both subjective and objective data, including financial metrics such as sales, profits, and costs, as well as qualitative aspects related to management quality.
Fundamental analysis, as noted by Veliota (2015), operates on the premise that stock market prices often fail to accurately represent a stock's true value; however, over time, the market will align with its fundamentals This approach offers a significant advantage, as it is widely utilized and accessible for novice investors looking to make informed investment decisions.
Fundamental pricing models in the stock market, as noted by Ahmed (2015), are utilized by analysts to evaluate interest rates, risks, and both current and future earnings of a company This analysis helps in determining the intrinsic value of a stock, which is then compared to its market value to guide investment decisions.
Fundamental analysis relies on subjective inputs, leading to varying analytical outcomes for the same stock Unlike other methods, it remains unaffected by investor psychology, ensuring a more objective evaluation of a company's value.
According to Kirkpatrick and his partners (2006), the concept of "Technical analysis is a method of stock analysis that predicts price trends by studying past market data primarily price and volume"
In technical analysis, as outlined by Pring (1991), stock prices align with market trends driven by fluctuations in investor demand and supply This approach focuses solely on price movements, disregarding a stock's intrinsic value, and employs charts and technical tools to identify upward or downward trends Janssen and colleagues (2015) identify three key characteristics of technical analysis: the market's tendency to decline, stock prices fluctuating in accordance with trends, and the historical patterns of stocks that often repeat.
Technical analysis offers the benefit of requiring less input data compared to fundamental analysis, allowing for automated operations that help investors manage market psychology objectively However, its reliance on historical stock price data poses a significant drawback, as past performance does not guarantee future results Additionally, technical indicators can sometimes lead to misunderstandings or inaccuracies in varying market conditions, making this approach subjective for each investor.
Behavioural finance theory
The stock market is a collective of investors aiming to predict market trends for profitable investment decisions This diverse group consists of individual investors who often experience conflicting emotions, such as self-confidence, optimism, and fear, which can influence their decision-making While fundamental analysis assesses a company's status and technical analysis evaluates stock price trends, both methods overlook the critical aspect of investor sentiment in the analysis process.
Private investment plays a crucial role in the overall investment decision-making process, highlighting the limitations of both fundamental and technical analysis, as well as the influence of investor psychology To address these shortcomings, the study incorporates behavioral finance theory as a valuable tool for enhancing investment strategies.
Behavioral economics critically examines the validity of neoclassical assumptions regarding human behavior and aims to identify empirical laws that more accurately describe actual behavior It also explores the implications of these behavioral deviations for economic systems, institutions, and public policy Additionally, the field seeks to provide empirical evidence regarding the utility function or its alternatives, enhancing the predictive power of economic behavior theories.
Prospect theory highlights the challenges of decision making under uncertainty, as Simon (1987a) suggests that individuals face cognitive limitations that hinder their ability to maximize utility as conventional wisdom suggests Consequently, even highly intelligent individuals often resort to alternative decision-making heuristics to navigate their choices effectively.
Bounded rationality refers to the limitations in human knowledge and computational capacity that hinder economic actors from making decisions that align with classical and neoclassical economic theories These limitations include the lack of a comprehensive and consistent utility function to evaluate all possible choices and the inability to consider more than a small subset of relevant alternatives.
16 inability to foresee the consequences of choosing alternatives, including inability to assign consistent and realistic probabilities to uncertain future events”
Conventional wisdom posits that institutions deliver accurate and reliable information transparently, enabling individuals to make rational, utility-maximizing decisions It assumes that these institutions are fundamentally rational and promote both individual and social rational choice behavior Notably, prospect theory does not contest this implicit assumption of conventional wisdom.
A central figure in New Institutional Economics, 360 critiques conventional theory for its flawed assumption that institutions are merely designed for efficiency and can be overlooked in economic analysis, as they supposedly do not influence economic performance This prevailing belief simplistically presumes that institutions fostering efficiency will naturally emerge without deliberate intervention.
Economic history shows that institutions promoting economically inefficient yet individually rational behavior can endure over time Such institutions, which reward utility-maximizing but inefficient actions, can remain stable and significantly influence economic outcomes, even when agents are narrowly self-interested In the context of behavioral economics, Simon (1978) argues that it is essential to model the institutional realities that establish the constraints affecting choice behavior, as these institutions are crucial in incentivizing decision-making.
Prospect theory and behavioral finance highlight that conventional finance may falter not solely due to flaws in SEU theory, but largely because of institutional factors These factors often involve misleading and asymmetric information, along with irrational individual behaviors.
Incentives, such as moral hazard related to principal-agent problems, can lead to inefficient and suboptimal behaviors in financial markets For instance, when fund managers face minimal risk of losing their economic gains from high-risk investments and can conceal their actions from investors, moral hazard emerges, resulting in economically inefficient outcomes It is crucial to recognize that all agents are rational within the constraints they encounter, including the incentive structures in place Thus, incorporating institutional parameters into the discussion of prospect theory is essential for understanding these dynamics.
The Behavioural Finance Theory, introduced by French psychologists Jean Gabriel De Tard and George Charles Selden, highlights the significance of psychological factors in influencing investor reactions to market dynamics Tversky and his colleagues further expanded on this concept, emphasizing the role of cognitive biases in financial decision-making.
Behavioral finance theory, established in 1979, posits that "people are not always right." Nguyen Duc Hien (2013) further emphasizes that this theory is founded on two key principles: the market does not always operate efficiently, and investors often act irrationally.
Behavioral finance theory, as outlined by Ricciardi and his partners (2000), is grounded in three key pillars: psychology, sociology, and finance This interdisciplinary approach seeks to address the intricate challenges associated with investor behavior, emphasizing that understanding these issues requires both individual and collective insights.
Psychology, as defined by Femald (2008), encompasses the study of the mind and behavior, exploring all facets of consciousness and human thought In the realm of investing, psychological principles are crucial for understanding the reasons behind investors' actions in specific situations.
Sociology is the scientific study of how relationships between individuals and society are formed, evolve, and influence one another It examines the impact of societal factors on human behavior and how individual actions can, in turn, shape society Key elements that influence human attitudes and behaviors include social structures, social categories, and social foundations.
AN OVERVIEW OF SECURITIES TRADING ACTIVITIES AT ASIA
2.2.1 The establishment and development of the company
ACBS received its license from the State Securities Commission on June 29, 2000, and was granted Business Registration Certificate No 4104000006 by the Ho Chi Minh City Authority for Planning and Investment on June 30, 2000.
- Authorized capital as of December 31, 2017: VND1,500 billion
- Owner: Asia Commercial Bank (ACB)
- Company type: Limited Liability Company
- Trading Name: ACB Securities Company, Ltd
- Head office: No 41 Mac Dinh Chi, Da Kao Ward, District 1, Ho Chi Minh City
- E.mail: acbs@acbs.com.vn
- Website: www.acbs.com.vn
Basic securities operations at ACBS:
In addition, ACBS also provides other financial products such as:
- Consulting and conducting auction of stocks
- Consulting the organization of the General Assembly of Stockholders
- Consultancy on corporate financial restructuring
- Consultancy of acquisition and merger
In 2016, ACBS reported brokerage revenue of nearly VND 117 billion, an increase of over VND 15 billion from 2015 Despite this growth in revenue, the company's brokerage fees remained advantageous, leading to a significant profit decline of VND 831.6 million compared to the previous year Looking ahead to 2017, the company aims to improve its financial performance.
ACBS will continue to increase revenue from business activities, of which revenue from brokerage activities of ACBS is approximately 153 billion dongs, profit of about 14 billion dongs compared to 2016
With the determination to regain market share, ACBS has returned to the top
In the fourth quarter of 2017, ACBS regained its position among the top five companies on the HNX with a market share of 6.08%, securing the sixth position for the entire year Meanwhile, on the HoSE, ACBS ranked seventh, achieving a market share of 3.145% during the same quarter.
2.2.2 The structure and organization of the company
ACBS is a contemporary financial institution backed by significant shareholders, including both state-owned and foreign entities Based in District 1, Ho Chi Minh City, ACBS operates 9 branches and 2 transaction offices nationwide, enabling it to deliver valuable information to investors and meet their investment needs on stock exchanges.
ACBS is a member of SWIFT, ensuring worldwide customer service 24 hours a day
2.2.3 Security trading activities of the company
Between 2015 and 2017, ACBS experienced sustainable growth in equity, despite facing challenges in 2015-2016 By 2017, ACBS's equity reached nearly VND 1,859 billion, reflecting an increase of VND 44 million from 2016, positioning the company as one of the most financially stable entities in the industry This robust financial structure enables ACBS to effectively pursue its investment and business activities.
1 ACBS financial statement from 2015 to 2017
From 2015 to 2017, ACBS demonstrated significant growth, with net revenue rising from nearly VND 135 billion in 2015 to approximately VND 476 billion in 2017, marking a 1.5-fold increase in 2016 However, operating expenses also increased by VND 40 billion in 2017 compared to 2016, largely due to a VND 12 billion rise in losses from FVTPL financial assets and a VND 134 billion decrease in the revaluation of financial assets Additionally, after-tax profit in 2016 saw a decline of about VND 17 billion compared to 2015.
96 billion dongs) Then continue to increase steadily from 2016 to 2017 and reached the highest of nearly 134 billion dongs
Table 2.1 Results of business operations of ACBS from 2015-2017
Brokerage revenue of securities 101,549 116,856 152,959 Revenue from securities investment and capital contribution 9,449
Source: ACBS Financial Report from 2015 to 2017
In 2017, amidst robust and sustainable stock market growth, ACBS recorded significant achievements, with an EBT increase of 15.4% compared to 2016 The company prioritized the development of brokerage services, particularly for individual investors, leading to a 4.12% rise in revenue from these services Additionally, ACBS undertook a proactive restructuring of its balance sheet, reducing ineffective investments while maintaining a carefully selected portfolio with a low investment level.
Chapter 2 explores key concepts related to stock and stock investment, focusing on fundamental analysis, technical analysis, and behavioral finance theories as foundational research elements It examines the factors that affect individual investors' stock investment decisions and provides an overview of ACBS's securities trading This analysis aims to establish a precise and relevant research model for the topic.
CHAPTER 3: SURVEYING THE IMPACT OF FACTORS ACCORDING TO STOCK INVESTMENT DECISIONS OF INDIVIDUAL INVESTORS AT ASIA COMMERCIAL JOINT STOCK BANK SECURITIES LIMITED
SURVEY MODEL
The proposed model of this study is based on two key projects,
“Fundamental versus Technical Analysis” written by Wiwik Utami and his partners
(2015), to analyze investment decisions from the behavioural finance theories with
“The investment behaviour, decision factors and their effects toward investment performance in the Taiwan stock market” written by Yu-Je Lee and his partners
(2010) Both research use the Exploratory Factor Analysis (EFA) method and the appropriate approach to the author's study.
THE PROCESS OF SURVEY
• Phase 1: Building the raw questionnaire (1 st appendix) based on the information collected in the theoretical model and related behavioural finance studies
In Phase 2, we focused on selecting and refining a questionnaire informed by expert insights and a nominal scale design To assess its clarity, we conducted interviews with 10 random individual investors, gathering their initial opinions on how behavioral factors influence their investment decisions The study utilized analytical methods to explore the decision-making processes of investors regarding stock investments, employing a 5-point Likert scale ranging from "absolutely agree" (1) to "totally disagree" (5).
• Phase 3: Editing and completing the questionnaire, send out an official questionnaire (2 nd appendix)
Step 2: Determining the number of samples needed and the scale for the survey
Determining the appropriate sample size for a study is a complex task that varies based on the estimation methods employed Research indicates that a representative sample size can be as small as 5 for certain estimates, while specific models, such as one with 22 observations, may require a minimum of 110 samples to ensure accuracy In this survey, a total of 174 samples were utilized, thereby ensuring a representative sample size that supports the validity of the findings.
The Likert scale is a widely utilized measurement tool in quantitative research, featuring five levels ranging from 1 to 5 to assess respondents' ratings The questionnaire is structured so that 1 represents "absolutely agree" and 5 signifies "totally disagree."
Step 3: Sending the questionnaire to investors
The questionnaires were distributed to investors through emails and printed sheets, with enthusiastic backing from partners and brokers who are friends and colleagues of the author The investors who received these questionnaires were randomly chosen based on their transactions at ACBS stock exchanges.
The questionnaires were directly sent to the investors, allowing them to promptly complete and return their responses to the author or a colleague.
Step 5: Receiving feedback from investors
There were 200 questionnaires received, with a response rate of 100%, of which 26 were rejected for invalidation Therefore, the remaining sample size for analysis is 174 survey forms
Step 6: Processing data using the SPSS analysis tool
Questionnaire data is designed as follows:
Section 1: Some information about investors:
Q1: Time to participate in the stock market
Q4: The impact of psychological factors on the decision making process of investment
Section 2: Behavioural factors affect the decision-making process
Section 3: The impact of factors on the investment decision-making process
After gathering the necessary sample size, the authors utilized the SPSS software to analyze the data based on the encoded scale outlined in the table below, with detailed results to be presented in the subsequent section.
Table 3.1 Summary of encoded scales
1 T Time to participate in the stock market
4 B The impact of psychological factors on making decision of investment
5 RP1 I think that investing in stocks is hot in the market despite the risks, but that way to invest which is the fastest profitability
6 RP2 Stocks have had good growth results in the past which will usually have good growth in the future
7 OV1 I think I have enough knowledge and experience to self-analyze and forecast the market situation
8 AN1 Reference price to decide whether to buy a stock is the price sold before or the stock price on the first trading day
9 AN2 Stocks of well-known large companies are often more liquid and their growth rates are often higher than small and less-engaged stocks
When you have to make a decision to buy or sell stocks in a short period of time, the trend of the majority is fast and quite sure
11 HB2 Highly reliable references are information from relatives, friends and colleagues
I think that depending on the daily trading situation of foreign investors in the market to make investment decisions is a safe and effective investment measure
I believe that securities specialists are the most knowledgeable people in the market and we should follow their recommendations when deciding to buy or sell stocks
When evaluating profit or loss in investments, the emotional impact of losing money is typically more intense than the joy experienced from earning profits.
The stock has had several sessions of continuous increase, if this stock investment, the safest method is to immediately sell stock when the stock just reached the account
16 LA3 Willing to take more risks when in a negative state
17 RG1 Procrastination, regret selling the loss of the stock when in a loss state
For long-held, loss-making stocks, it's advisable to sell as soon as the stock price rebounds, rather than holding out for potentially higher prices This strategy allows investors to recover their capital and reinvest in more promising stocks.
FACTORS AFFECTING STOCK INVESTMENT DECISIONS
19 P Volatility of stock prices at the moment
20 I Information on domestic and international markets
21 T The trend of stock price trend in the past
22 F Report on profit and performance of listed companies
Chapter 3 mainly presents the survey model and the process of survey to the groups of factors influencing the decision of stock investment of individual investors at ACBS, corresponding to each group of factors, there are hyppotheses for the test The topic was conducted in a qualitative way with the main method which is EFA
RESEARCH RESULTS
DESCRIPTIVE STATISTICS ABOUT INVESTOR INFORMATION
Table 4.1 Statistics of securities market participants' time of individual investors at ACBS
Time to participate Frequency Percentage (%) Accumulative percentage (%)
Source: Analysis results on SPSS 16.0 (Appendix 3.1)
A recent survey of 174 individual investors revealed that 54.6% had participated in the stock market for less than three years, with an average duration of 2.65 years Additionally, a bachelor thesis by Ngoc Thu in 2018 indicated an even higher figure, showing that 90.4% of investors had entered the stock market within the same three-year timeframe.
Picture 4.1 Pie chart of securities market participation time of individual investors at ACBS
Source: Analysis results on SPSS 16.0
Statistics reveal that 90.2% of individual investors have been in the market for one year or longer, while only 9.8% are new entrants this year.
4.1.2 Investment capital in the stock market
Table 4.2 Statistics describing the investment capital of individual investors
Investment capital Frequency Percentage (%) Accumulative percentage (%)
Source: Analysis results on SPSS 16.0 (Appendix 3.2)
The mean value of 2.10 indicates that more than half of the individual investors surveyed participating in the stock market with an investment capital of
The statistics reveal that a significant majority of individual investors, specifically 92%, hold investments of less than 1 billion VND, while only 8% have ventured into the stock market with amounts exceeding 1 billion VND The investment brackets include under 100 million VND, between 100 to 500 million VND, from 500 million to 1 billion VND, and over 1 billion VND, with the majority falling below the 1 billion VND threshold.
Picture 4.2 Pie chart of investment capital in the stock market of investors
Source: Analysis results on SPSS 16.0
Table 4.3 Statistics describing the investment strategy of individual investors
Source: Analysis results on SPSS 16.0 (Appendix 3.3)
Short-term investment (surfing) 140 80.5 80.5 Medium term investment (from 1 year to 2 years) 30 17.2 97.7 Long-term investment (over 2 years) 4 2.3 100.0
Short-term investment (surfing) Medium term investment Long-term investment
Picture 4.3 Pie chart of investment strategies of individual investors
Source: Analysis results on SPSS 16.0
A recent survey revealed that a significant majority of individual investors prefer short-term investment strategies, with a mean value of 1.21 Specifically, 80.5% of respondents favor short-term tactics, while combined short-term and medium-term strategies account for an impressive 97.7% In contrast, only 2.3% of investors opted for long-term investment approaches.
4.1.4 The influence of psychological factors on making decision of investment
Table 4.4 Descriptive statistics of the influence of psychological factors on the investment making decision of individual investors at ACBS
Source: Analysis results on SPSS 16.0 (Appendix 3.4)
Picture 4.4 Pie chart of the influence of psychological factors on the decision- making process of individual investors
Source: Analysis results on SPSS 16.0
A recent survey revealed that 87.9% of respondents, totaling 153 individuals, acknowledged the significant impact of psychological factors on their decision-making processes In contrast, only 12.1% of participants, or 21 out of 174 respondents, underestimated the role of these psychological influences in their choices.
The findings indicate that, alongside well-known analytical methods like fundamental and technical analysis, individual investors at ACBS are also significantly influenced by behavioral finance factors discussed in Chapter 2.
4.1.5 Analysis describes the behavioural factors that influence the decision- making process of individual investors at ACBS
Table 4.5 Descriptive statistics on behavioural factors affecting the decision- making process of individual investors at ACBS
Source: Analysis results on SPSS 16.0 (Appendix 3.5)
With 5 scales corresponding to 1 is "Absolutely agree"; 3 is "Neutral or no opinion" to 5 is "absolutely disagree", we see that the factor is many investors acknowledge the impact of it on their decision-making behaviour is the factor RP1 means "like investing in hot stocks" (average value is 1.65) The most unlikely personal investor was HB4 "The decision was based on the recommendations of experts in the stock-based media" (average value of 2.43).
ASSESSMENT OF SCALE RELIABILITY
Cronbach's Alpha is a statistical measure that assesses the reliability and internal consistency of observational variables It evaluates both the inter-correlation among the variables and the correlation of individual variable scores, ensuring a comprehensive understanding of their relationships.
N Minimum Maximum Mean Std Deviation
HB4 174 1 5 2.43 1.000 HB2 174 1 5 2.16 957 HB3 174 1 5 2.09 854 AN2 174 1 5 1.99 778 LA2 174 1 5 1.97 785 HB1 174 1 5 1.95 809 LA3 174 1 5 1.94 794 LA1 174 1 5 1.88 750 RG1 174 1 5 1.84 754 OV1 174 1 5 1.77 744 RP2 174 1 5 1.75 737 RG2 174 1 5 1.73 736 AN1 174 1 5 1.70 728 RP1 174 1 5 1.65 717 Valid N (listwise) 174
The total score for each respondent's variables is 48, enabling analysts to eliminate unsuitable variables and refine the model This process is essential for accurately assessing variability and error Only variables with a Corrected Item-Total Correlation greater than 0.3 and Alpha coefficients above 0.6 are deemed acceptable for further analysis Many researchers suggest that a Cronbach's Alpha of 0.8 or higher indicates a good scale with strong correlation.
4.2.1 Assessing the reliability of the scale of variables observing the behavioural factors
Appendix 3.6 reveals a Cronbach's alpha coefficient of 0.911, demonstrating high internal consistency among the observed variables, as all Corrected Item-Total Correlation values exceed 0.3 This suggests that investors assess these observational variables with a significant degree of reliability.
Thus, these observational variables are satisfactory and are used in Exploratory Factor Analysis
4.2.2 Assess the reliability of investment decision scale
Table 4.6 Results of the evaluation of the reliability of the investment decision scale
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted Volatility of stock prices at the moment 6.14 3.883 687 880
Information on domestic and international markets
The trend of stock price trend in the past 6.15 3.479 815 833
Report on profit and performance of listed companies 5.90 3.475 756 855
Source: Analysis results on SPSS 16.0 (Appendix 3.7)
The investment decision scale demonstrates a strong reliability with a Cronbach's Alpha of 0.883, indicating high internal consistency Additionally, the correlation coefficients of the observed variables exceed 0.3, validating their suitability for use in Exploratory Factor Analysis in subsequent stages.
The reliability tests for the "financial behavioral factors" and "investment decision" scales indicated that all observed variables met statistical adequacy, resulting in no eliminations The overall correlation among the variables was confirmed.
50 coefficient is less than 0.3, so these variables are sufficiently reliable and are used for further analysis of this research.
EXPLORATORY FACTOR ANALYSIS (EFA)
Exploratory Factor Analysis (EFA) is employed to identify the underlying structure of observation variables, effectively summarizing data on a reduced scale This interdependence technique assesses two crucial aspects of measurement scales: convergent and discriminant values As noted by Nguyen Dinh Tho (2011), convergent values reflect the consistency of a scale in measuring a concept through repeated iterations, indicating that correlations become closer over time Conversely, discriminant values ensure that two measures of distinct concepts yield different correlation coefficients, highlighting their uniqueness.
In factor analysis, Principal Components Analysis (PCA) combined with Varimax rotation is the most widely utilized approach Key conditions for employing Exploratory Factor Analysis (EFA) include examining factor loadings, the Kaiser-Meyer-Olkin (KMO) measure, Bartlett's test, and the percentage of variance explained.
According to Hair and his partners (1989), this is an indicator to ensure the level of practical significance of the EFA
Factor loading levels: o Factor loading > 0.3: viewed to the minimum o Factor loading > 0.4: is considered important o Factor loading > 0.5: considered to be practical
- KMO (Kaiser meyer olkin): is the index used to determine the appropriateness of factor analysis Used to measure the fit of the sample and to
51 indicate the magnitude of the correlation between the observed variables The larger the KMO, the more meaningful it is
Bartlett's test is a statistical tool used to examine the hypothesis that variables have no correlation in overall With sig < 0.05 statistically significant, the observed variables are correlated in the overall
The percentage of variance is a key metric for assessing the adequacy of a hidden factor, reflecting the calculated variance for that factor To determine the appropriate number of factors, analysts typically use an Eigenvalue threshold of 1 or higher, along with a total variance criterion of 60% or more.
The reliability of the tested variables will be assessed using Cronbach's Alpha coefficient and cumulative correlation coefficients, focusing on their correlation within the variable group Factor analysis is applicable when the Kaiser-Meyer-Olkin (KMO) coefficient exceeds 0.5 (Garson, 2003) Variables with factor loadings below 0.4 will be excluded to ensure convergence among variables within a single factor, where the eigenvalue is greater than 1 and the total deviation distribution exceeds 0.5 (Gerbing & Anderson, 1998) This study will employ the Principal Component method with Varimax rotation for conducting factor analysis.
All 14 initial observation variables met the reliability criteria established by the Cronbach's Alpha coefficient and were included in the exploratory factor analysis (EFA) The findings from the EFA revealed significant insights into the underlying factors.
Table 4.7 Results of factor analysis of independent variables
Kaiser-Meyer-Olkin Measure of Sampling Adequacy .851
Bartlett's Test of Sphericity Approx Chi-Square 1512.207 df 91
Source: Analysis results on SPSS 16.0 (Appendix 3.8)
The KMO coefficient of 0.851, which exceeds the threshold of 0.5, indicates that the hypothesis of a homogeneous correlation matrix is rejected Additionally, Bartlett's test reveals a significance level of 0.000, which is less than 0.05, confirming that the observed variables exhibit significant overall correlation.
The analysis results in 3 rd appendix (Appendix 3.9) show the four factors extracted at the eigenvalue 1.065 and the variance of 73.798% > 50% showed that 73.798% of the variance is explained by four factors
In the four factors extracted, we observe:
Group 1 st factor (factor X1): Includes variables RG1, RP1, OV1 and RG2 o The maximum factor loading factors of each observation variable are 0.5, so no variables were excluded from the study model o Four observers RG1, RP1, OV1 and RG2 were re-entered into one with relatively high factor load factor These variables refer to regret, self- confidence, and interest in investing in hot stocks Although theoretically four variables of observation are in three different concepts, but within the scope of research, individual investors at ACBS have agreed to identify these three factors into one constitutes a new observation variable and is called ôOverconfidence andRegretằ (variable X1)
Source: Analysis results on SPSS 16.0 (Appendix 3.9)
Group 2 nd factor (factor X2): Includes variables LA2, LA1, HB2, and LA3 o These observation variables have a factor load factor of 0.5, which is not excluded model o LA2, LA1 and LA3 observation variables refer to aversion to individual investor losses and HB2 to trust in relatives, friends and colleagues These variables are grouped together and are called ôLoss aversionằ (variable X2)
Group 3 rd factor (X3): Includes HB4, HB1 and HB3
In this observational study, only factor loadings exceeding 0.5 will be incorporated into the research model, which is designated as "Herd Behavior" (variable X3).
Group 4 th factor (X4): Include variables AN1, RP2, and AN2 o Observational variables of this group also had coefficients of loading satisfying the requirement (greater than 0.5) o This group of observations refers to the experience and retrospect of the trader of such experiences when it comes to assessing similar problems in the present so called ôBehaviour of past experience-based retrospectằ (variable X4)
Ngoc Thu's bachelor thesis (2018) presents a rotation matrix in Table 4.12, indicating that all factor loadings exceed 0.5 and are categorized into four distinct groups Group 1 consists of variables X11, X12, X13, and X14; Group 2 includes X21, X22, X23, and X24; Group 3 comprises X31, X32, X33, and X34.
Questions X31 to X34 reflect investors' perceptions of stock market conditions, while questions X35 to X38 illustrate their actions and reactions to these situations Consequently, psychological factors influencing investors can be categorized into negative psychological factors (group 3) and positive psychological factors (group 4).
4.3.2 Analysis of dependent variable factor (Y)
The investment decisions (Y) of individual investors are assessed using four observable variables (T, I, F, P) and analyzed through the Principal Component method with Varimax rotation Observations with a factor loading below 0.5 are excluded to maintain adequate convergence with the other variables in the scale The analysis yields significant results that inform the understanding of individual investor behavior.
Table 4.8 Results of the dependent variable EFA analysis
Kaiser-Meyer-Olkin Measure of Sampling Adequacy .827 Bartlett's Test of Sphericity Approx Chi-Square 394.856 df 6
Source: Analysis results on SPSS 16.0 (Appendix 3.10)
The four observations—T, I, F, and P—are consolidated into a single factor, demonstrating a factor loading of 0.5 The variance explained by this factor is 74.905%, significantly exceeding the 50% threshold Additionally, the Bartlett test yields a significance level of 0.000, indicating strong correlations among the observed variables Furthermore, the Kaiser-Meyer-Olkin (KMO) coefficient is 0.827, which is well above the acceptable level of 0.5, affirming the adequacy of the sample for factor analysis.
Initial Eigenvalues Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %
The trend of stock price trend in the past .904
Information on domestic and international markets
Report on profit and performance of listed companies
Volatility of stock prices at the moment
Source: Analysis results on SPSS 16.0 (Appendix 3.10)
The EFA analysis identified four key factors, labeled X1, X2, X3, and X4, that encapsulate the behavioral influences on the decision-making processes of individual investors in Ho Chi Minh City.
Therefore, the former research model is finally adjusted as follows:
Picture 4.5 The relationship between four main factors (X) and dependent variable (Y)
REGRESSION ANALYSIS
Table 4.9 Correlation matrix between factors
Source: Analysis results on SPSS 16.0 (Appendix 3.11)
The analysis of the correlation matrix reveals a significant relationship between financial behavioral factors and the decision-making process of investors (p < 0.05) However, the Pearson correlation coefficients for all four groups of behavioral factors are below 0.5, suggesting that these factors do not play a dominant role in the decision-making process of individual investors at ACBS.
We perform regression analysis to identify the weight of various behavioral factors affecting investor decision-making, utilizing four independent variables (X1, X2, X3, X4) and one dependent variable (Y) The analysis employs normalized values of the observed variables and is conducted using SPSS 16.0 software The results of this total regression analysis provide insights into the influence of these factors on investment decisions.
Table 4.10 The summary of regression model
Model Variables Entered Variables Removed Method
Std Error of the Estimate
Unstandardized Coefficients Standardized Coefficients t Sig
B Std Error Beta Tolerance VIF
Source: Analysis results on SPSS 16.0 (Appendix 3.12) Comments:
A Sig.F change value of less than 0.05 indicates that the included variables are statistically significant at the 5% significance level Consequently, the independent variables (X1, X2, X3, X4) in the model are associated with the dependent variable (Y).
Regression analysis indicates that all four independent variables—Overconfidence and Regret (X1), Loss Aversion (X2), Herd Behavior (X3), and Behavior Based on Past Experiences (X4)—significantly influence individual investors' decision-making processes, as evidenced by their coefficients being below 0.5%.
• The adjusted R2 coefficient in this model is 0.273 This shows that 27.3% of the variation in the decision-making process (Y) is generally explained by the four variables
• The coefficients of VIF of the independent variables in the model are less than 2, so the multi-collinearity of the independent variables has no
Model Sum of Squares df Mean Square F Sig
59 significant effect on the regression model
• The Durbin-Watson statistics value approximately to 2 (1.752) indicates the residual has no correlation
The ANOVA analysis indicates that the F parameter has a significance value of 0.000, confirming that the constructed regression model aligns well with the collected data set Consequently, the derived linear regression model is deemed reliable.
Y: Investment decisions of individual investors at ACBS 0.372: Overconfidence andRegret
0.256: Loss aversion 0.245: Herd behaviour 0.165: Behaviour of past experience-based retrospect
The coefficient β1 (0.372) indicates that a one-unit increase in individual investors' overconfidence and regret leads to a 0.372 unit rise in their stock investment decisions at ACBS, assuming other factors remain constant This demonstrates that both overconfidence and regret significantly influence individual investors' stock selection at ACBS.
The coefficient β2 (0.256) indicates that a one-unit increase in loss aversion among individual investors at ACBS leads to a 0.256 unit rise in their stock investment decisions, assuming all other factors remain constant This demonstrates that loss aversion significantly influences individual investors' choices when selecting stocks at ACBS.
The coefficient β3 (0.245) shows that when the herd behaviour of individual investors is increased by one unit, the level of stock investment decisions of
At ACBS, the number of individual investors is set to rise to 0.245 units, assuming all other factors remain constant Consequently, the influence of herd behavior will continue to significantly affect individual investors' decisions when selecting stocks at ACBS.
The coefficient β4 (0.165) indicates that a one-unit increase in individual investors' behavior based on past experiences leads to a 0.165 unit increase in their stock investment decisions at ACBS, assuming other factors remain constant However, this behavior based on past experiences has an insignificant impact on the individual's stock selection decisions at ACBS.
Research by Lingesiya Kengatharan (2014) indicates that "Overconfidence" negatively affects investment efficiency, while "Anchoring" positively influences it The study highlights that anchoring significantly impacts individual investors, whereas excessive confidence and fear of loss have a moderate effect In contrast, herd behavior shows a minimal influence on individual investors.
Hoang Quan and his partner (2016) identified five key factors influencing individual investors' decisions in Vietnam: overconfidence (X1), herd behavior (X2), loss aversion (X3), anchoring (X4), and overreaction (X5) Their research revealed that overconfidence has the most significant impact on investment choices, while herd behavior and loss aversion exert a lesser influence Overall, individual investors in Vietnam are primarily affected by overconfidence, with herd behavior being the least impactful factor.
According to Ngoc Thu's 2018 bachelor thesis, a multivariate regression analysis revealed four key factors influencing individual investors' stock investment decisions at HSC: X1 (fundamental analysis), X2 (technical analysis), X3 (negative psychological factors), and X4 (positive psychological factors) Among these, three factors were identified as having a positive impact on investment decisions.
The analysis reveals that positive psychological factors (X4) have the most significant impact on investment decisions, with a beta coefficient of 0.442, indicating that investors are more likely to choose stocks when they maintain a positive outlook Following this, technical analysis (X2) shows a beta coefficient of 0.190, while fundamental analysis (X1) has a beta coefficient of 0.162 Conversely, negative psychological factors (X3) negatively affect the dependent variable, with a beta coefficient of -0.133.
4.4.3 Check for breach of regression model
- Check for multi-collinearity of the regression function:
Multi-collinearity occurs when independent variables are highly correlated As indicated in Table 4.11, the regression coefficients exhibit a very small significance value, demonstrating that the model is appropriate and the variables are valid (Tolerance > 0.0001) Furthermore, the collinearity diagnostics show that the Variance Inflation Factor (VIF) for the independent variables is below 2, indicating that the impact of the independent variables is minimal.
This article explores the relationship between the duration of stock market participation and the decision-making behaviors of individual investors at ACBS It examines how different securities investment strategies influence investment choices, highlighting the significance of understanding investor behavior in the context of market engagement Through ANOVA analysis, the study aims to uncover patterns that can guide investors in making informed decisions regarding their investment strategies.
Sum of Squares df Mean
Time to participate Between Groups in the stock market
Source: Analysis results on SPSS 16.0 (Appendix 3.13)