Abstract
Purpose
Determining the impact of behavioral influences on the stock market has significant implications for investment analysis and portfolio management. Behavioral biases are parameters that need to be considered in investment decision-making. The purpose of this study is to inform Bangladeshi investors about behavioral biases that they may encounter when making investment decisions in the prevailing frontier environment.
Design/methodology/approach
Through the chi-square test, one-way ANOVA, paired-samples t-test and descriptive analysis based on the facts collected from 281 respondents of the Dhaka Stock Exchange (DSE), the study has found that individual investors of Bangladesh often make investment decisions emotionally rather than based on theories.
Findings
The result shows that risk aversion and risk perception are the two most influential emotional dimensions that impact investors' decisions. The findings are consistent with the other researchers and highlight the fact that investors hardly act according to the norms recommended in the financial theories.
Research limitations/implications
The findings are grounded on a small portion of investors at DSE on some particular days, which is not sufficient to study individual investors' entire complex decision-making behavior from various angles. Many respondents were reluctant and even confused to disclose their behavioral aspects. These, along with biased and careless answers, may impede the identification of the actual scenario of the behavioral responses in decision-making that demand further study.
Originality/value
The novelty of this study is unique in that it examined investors of the DSE, who are considered to be a representative in a frontier market like Bangladesh. Since this market is not very resilient, small investors need to be aware of the biases of behavioral factors to survive.
Keywords
Citation
Hossain, T. and Siddiqua, P. (2022), "Exploring the influence of behavioral aspects on stock investment decision-making: a study on Bangladeshi individual investors", PSU Research Review, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/PRR-10-2021-0054
Publisher
:Emerald Publishing Limited
Copyright © 2022, Tanzina Hossain and Pallabi Siddiqua
License
Published in PSU Research Review: An International Interdisciplinary Journal. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode.
1. Introduction
Over the past several decades, finance scholars and researchers have conducted several studies and research for establishing different theories to explain the financial markets environment considering investors as rational. One such hypothesis is the efficient market hypothesis (EMH), which claims that capital markets are informationally efficient and that investors may make the best investment decisions assuming information symmetry. By examining a number of share prices in the market, Fama (1991) discovered that the market is efficient when all of the necessary information is held by market participants for investment decision-making. However, Raiffa and Raiffa (1968), Kahneman and Tversky (1979) observed that an individual investor's behavior, in theory, diverges from that in practice. They found traditional financial models are unable to clarify and predict all financial decisions and fail to explain some phenomena that impact an investor's stock-picking choice. Some emotional issues move investors in making investment decisions which is the evidence of irrational market behavior or inefficient markets. Here, the importance of behavioral finance is apparent. Individuals may not always be coherent, according to behavioral finance; instead, they are human beings who discover the irrationality of investors in general, leading them to make irrational investing decisions.
Numerous empirical studies carried over many financial markets to prove that investment choices are not constantly built on the traditional finance theories; they also depend on behavioral financial factors (Baker and Wurgler, 2007; Banerjee, 1992; Caparrelli et al., 2004; Chaudhary, 2013; Fama, 1965; Fogel and Berry, 2006; Jokar and Daneshi, 2018; Olsen, 1998; Shleifer, 2000; Waweru et al., 2008; De Bondt and Thaler, 1985). However, resourceful and effective data and testable estimates are necessary to determine the success and occurrence of behavioral finance (Sent, 2004).
With few developments in the Bangladeshi stock market, the investors' irrational behavior is visible. This irrational behavior will have an enduring, extensive upshot on the whole Bangladeshi financial market. This study's key driver is to understand how the investors' psychological actions can elucidate Bangladeshi capital markets' deficiencies. Though several studies (Bakar and Yi, 2016; Bashar et al., 2017; Caparrelli et al., 2004; Chaudhary, 2013; Mouna and Anis, 2014; Trehan and Sinha, 2018) have been conducted on stock market, this study examines the consequences of four well-known behavioral biases (loss aversion, overconfidence, herding, and risk perception) on investment choices for 281 Bangladeshi individual investors in Dhaka Stock Exchange (DSE).
It is expected that this study will assist the existing and potential investors by providing a way to make investment decisions by combining the fundamental and the technical aspect with the psychological factors to improve their risk-adjusted performance. The study will also assist Bangladesh Securities and Exchange Commission (BSEC), DSE, Chittagong Stock Exchange (CSE) to formulate policy and regulation considering the distortions caused by investors' psychological factors while making investment decisions. The findings will form a foundation for more learning in this area as very little research has been published on frontier markets like Bangladesh. However, the sample is too low compared to the total population. Conducting this study on this sample is supposed to give investors a technique to identify behavioral elements that can be used to hand-pick a healthy stock investment decision-making strategy.
The objectives of this research are to (1) determine if behavioral factors influence individual investors' stock investment decisions at the DSE and (2) determine the relative significance of behavioral factors in influencing individual investors' stock investment decision-making at the DSE. The factor of investment behavior was examined by applying the thought of behavioral finance among the individual investors of the DSE, Bangladesh.
2. Literature review and hypotheses development
2.1 Literature review
Behavioral analysis of investors in the stock market is very central in any developing country because most investors are behaviorally biased. Behavioral influences on human behavior in stock markets sometimes compel them to make irrational decisions. So, it is essential to address those influences. Many anomalies in the financial market cannot explain market inefficiency and irrationality. Extensive studies and research by behavioral psychologists and finance theorists have been conducted to address this irrationality. Recurrent forms of irrationality, inconsistency and incompetence in people's decision-making are found when faced with uncertainty in these studies. By merging behavioral and cognitive-emotional concepts with conventional economics and finance, behavioral finance aids in describing why financial markets might be inefficient due to this irrationality.
Daniel Kahneman and Amos Tversky are considered the fathers of behavioral finance. They have published a considerable number of papers related to behavioral finance concepts since the 1960s where the foundations of behavioral finance were established with the concept of “Prospect Theory.” The prospect theory asserts that people make decisions based not just on utility decision-making processes but also on the probable value of gains and losses. The work of Tversky and Kahneman (1974) revealed that people do not use statistical approaches in their decision-making; instead, they rely on an insufficient number of investigative philosophies. According to them, people, contrary to expected utility theory, place different weights on gains and losses, as well as a different series of probabilities. They found that individual investors are affected much more by potential losses than equivalent gains. Richard Thaler, a renowned finance theorist, emphasizes applying the prospect theory to financial markets.
According to Shleifer (2000), market information has a significant impact on the stock market and, hence, on individual investors' investment behavior. Waweru et al. (2008) showed that, to some extent, investors' investment behavior is affected by the changes in the price of stocks. Stocks that have had a significant price movement for two years in row attract investors who choose to purchase rather than sell (Odean, 1999).
Motivated by them, researchers nowadays are trying to explore how investors' biases affect the efficiency of capital markets. Studies conducted by Hilbert (2012) and Chaudhary (2013) supported the effect of behavioral factors on investing outcomes such as greed, fear, cognitive dissonance, mental accounting, heuristics and anchoring of investors' thinking. Hilbert (2012) showed how behavioral bias such as herding, overconfidence and reinforcement bias influence individual investors more as compared to their institutional counterparts, whereas Chaudhary (2013) discovered that behavioral finance explains investors' irrational financial decisions and anchoring, overconfidence, herd behavior, over and underreaction, and loss aversions lead to irrational financial decisions. Different financial traits and biases such as loss aversion, hindsight bias, anchoring, endowment effect, disposition effect and mental accounting help individual investors in making sound financial decisions. Furthermore, according to Caparrelli et al. (2004), the herding effect impacts stockholders, causing them to move in unison with the rest of the herd if there are changes.
Investors, according to Barber and Odean (2000), sometimes place too much confidence in their previous gains and investment skills, leading them to overestimate their knowledge while underestimating risks. Overconfidence in predicting stock prices along with unnecessary transactions can ultimately lead to poor investment choices (Barber and Odean, 2000). However, some studies did not find any significant impact of overconfidence bias on the investment decision. This suggests that overconfidence is not common among individual investors around the world.
Loss aversion is not an unusual behavior of investors. A number of studies on loss aversion have been conducted and found that people are more distressed at the view of losses than they are happy by equivalent gains (Barberis, 2001). The possibility of losing money is, on an average, twice as motivating as the possibility of making the same amount of money (Barberis, 2001). Loss aversion, according to Barberis (2001), has a crucial impact in determining how people evaluate risky gambles. To them, loss aversion is the tendency of an individual to be more affected by losses than corresponding gains.
2.2 Theoretical framework and hypotheses development
The influence of behavioral aspects on stock investment decision-making can be related with the cognitive dissonance theory which says that the reactions of humans shoot from the view of themselves as “smart, nice people” and people tend to ignore or reject the information that conflicts with this smartness (Aronson, 1979). The conceptual framework for our analysis can be based on cognitive dissonance as labeled by psychologists rather than rational behavior under Bayesian decision theory. Interestingly we found that most of the investors take their investment decision based on their estimate of the state of the world as influenced by their preferences over their state of belief as suggested in “The Economic Consequences of Cognitive Dissonance” (Akerlof and Dickens, 1982). The theoretical framework is depicted in Figure 1.
Though several kinds of research have been conducted on behavioral finance variables, most of them have been carried out in developed markets (Odean, 1999; Caparrelli et al., 2004; Fogel and Berry, 2006) with relatively little in emerging and frontier economies (Sochi, 2018; Akhter and Ahmed, 2013). Akhter and Ahmed (2013) in their study found different factors such as advice of brokers, friends and family, past performance, news of media, etc. influence investment decision. Sochi (2018) has found the significant presence of representativeness, overconfidence, anchoring, gambler's fallacy, loss aversion, regret aversion and mental accounting in investment decision. However, the statistical significance of such findings is absent in her study. Given the paucity of behavioral finance research in Bangladesh's emerging market, this study proposes testing the following hypothesis to evaluate the behavioral influences on investors' stock investment decisions.
There is no effect of behavioral financial issues on stock investment choice-making at DSE.
To determine which of the factors (loss aversion, overconfidence, herding and risk perception) most contributes to stock investment choice-making, the main hypothesis is subdivided into subsequent sub-hypotheses:
There is no effect of loss aversion on stock investment decision-making at DSE.
There is no effect of overconfidence on stock investment decision-making at DSE.
There is no effect of herding on stock investment decision-making at DSE.
There is no effect of risk perception on stock investment decision-making at DSE.
There is no significant difference in the effect among the four behavioral traits on stock investment decision-making at DSE.
The proposed conceptual framework based on the hypothesis has been depicted in Figure 2.
3. Data and methodology
3.1 Research method
The main goal of this study is to explore the presence of behavioral characters impacting individual Bangladeshi individual investors while taking any investment conclusion. For building the knowledge, several studies have been reviewed (Andrade, 2005; Bouwman, 2014; Choi and Skiba, 2015; De Bondt and Thaler, 1985, 1995; Dickinson and Muragu, 1994; Durham, 2002; Fama, 1965, 1991; Grinblatt and Keloharju, 2001; Ritter, 2003; Statman, 1999). Using the deduction approach of exploratory factor analysis (EFA), four psychological traits are found to be more effective in influencing the investment decisions of investors of the DSE. A well-structured closed-end questionnaire was designed and around 320 questionnaires were distributed. Only 281 useable forms were used in the subsequent statistical investigation, resulting in a participation rate of 88%.
The survey used a five-point Likert scale ranging from 1 (strongly disagree) to five (strongly agree) which contains 16 statements under four dimensions after the exclusion, inclusion and rephrasing. Table 1 illustrates the statements for each factor in the questionnaire. To evaluate individual investor's loss aversion, four items adapted from Chun and Ming (2009) have been used in this study. Four items adapted from Areiqat et al. (2019) are employed to assess the risk perception. Overconfidence is examined using four items in a study by Ngoc (2014). Based on Tan et al. (2008), four items measuring herding are adapted. The survey consists of two segments. The first segment is used to collect information about the demographic context of individual investors. The second segment focuses on psychological factors that influence investors' decision-making, wherein investors were asked to evaluate each statement based on their perceptions and thoughts. The score of the answers to the questionnaire has been used to capture the investment decisions. The mean values of these choices are used to examine the influences of the behavioral dimension of the investment decision according to the measurement scale mentioned in Table 2.
To test the internal reliability of multi-element scales, Cronbach's alpha is used. Descriptive statistics, chi-square test, one-way ANOVA are used to test the four null sub-hypotheses ((H0)1-1, (H0)1-2, (H0)1-3, (H0)1-4) under the main null hypothesis ((H0)1) for assessing the impact and the relative importance of behavioral traits on investment decision-making. In addition to these, paired-samples t-test has been conducted to test the fifth sub-hypothesis ((H0)1-5) for testing the significance of statistical differences among the behavioral factors. Different tables and graphs are used to precisely calculate data percentages and frequencies.
3.2 Reliability test
Cronbach's alpha is typically used in societal and behavioral studies to gauge consistency (Liu et al., 2010). Hence, Cronbach's alpha is used in this study to test the reliability of items included as the factors where the questionnaire consists of five-point Likert measurements. Nunnally (1978) asserted that measurements with at least Cronbach's alpha 0.7 are reliable. However, according to others, to be acceptable, Cronbach's alpha should be over 0.6, and the corrected item-total correlations should be 0.3 or higher (Shelby, 2011). The survey yielded a Cronbach's alpha of 0.732, indicating that the scale has a good level of internal reliability. Cronbach's alpha of all factors is more than 0.7, where the corrected item-total correlation of all items is greater than 0.30. Besides, Cronbach's alpha of each factor, if deleted, is less than the factor's Cronbach's alpha.
3.3 Demographic background
Due to various demographic factors such as age, education level, gender, race, social and economic context, every individual is different. The condition is the same with the individual investors while making any investment decision. They are usually affected by their emotional biases, which may vary according to their demographic traits. Table 3 shows the demographic background of the respondents of this study.
Considering gender biases, we have collected and analyzed information on gender. The figure shows that the number of female investors is very low compared to that of male investors in the sample, which further supports the study by Barber and Odean (2001) in the US. Men are more active in investment and have overconfidence in terms of excessive trading and higher-risk trading than women. Only 4.6% of the participants were female, indicating that women are not very interested in participating in share business. However, data obtained to a limited extent cannot accurately represent the genuine situation. Also, there is a possibility that women may be share trading online.
The study discovered that 54.4% of the total sample are investors less than 40 years, while 25.3% of the respondents are in the age group of 40–50, and only 20.3% of sample investors are more than 50 years. It is exposed from the study that most of the individual investors at the DSE are young, and this research may vastly replicate the investment behaviors of these young individual investors.
According to the data, 48.4% of the investors were post-graduates, indicating that the majority of DSE investors are well-educated. Of the total, 19.9% of those surveyed were graduates, with 6% having completed primary school, 13.2% have completed secondary school, 8.5% having completed a diploma and 3.9% having completed other courses.
The survey further found that the majority of investors (71.5%) of the sample did not attend any specialized training for stock trading. Only 28.5% of those surveyed have taken a course in this field. As a result, the majority of investors may be influenced by their behavior.
According to the survey, 71.2% of the sample has less than 10 years of experience, implying that most individual investors have just recently begun to pay attention to the stock market. This higher percentage of individual investors with low experience in the surveyed sample makes investors behaviorally biased. Only 28.5% of investors have spent more than ten years in the stock market.
4. Results of hypothesis testing
4.1 Descriptive statistics
Prospect theory is also known as the loss-aversion theory. The prospect theory says that investors value gains and losses differently, placing more weight on perceived gains versus perceived losses. The prospect theory is a behavioral model that shows how people decide between alternatives that involve risk and uncertainty (e.g. % likelihood of gains or losses). It demonstrates that people think in terms of expected utility relative to a reference point (e.g. current wealth) rather than absolute outcomes (Hirshleifer, 2001). Investors' loss aversion tendency can result in excess fluctuation in stock prices (Barberis, 2001). Moreover, this loss aversion psychology of investors is sometimes responsible for creating momentum effects on stock market trading (Grinblatt and Bing, 2005). In our study, we also tried to determine whether there is any contribution of loss aversion trait on Bangladeshi investors' investment decision-making. Table 4 shows the result of loss aversion which establishes that to some level, after a gain, investors at DSE turn out to be more risk lovers, whereas, after a loss, they lean towards more risk-averse. This behavior is not surprising as any failure on the investment surely slows down the investors a lot though gain motivates them so much.
Furthermore, the concept of risk perception can be used to explain investor behavior in the securities market. Researchers have used several indicators in their studies to measure the individual investors' risk perception while trading at a stock exchange. Four questions were used in our research to describe individual investors' risk perception, which was found to be negative at the DSE (Table 5).
Considering the principle of finance, “higher the risk–higher the return,” loss aversion and negative risk perception are not good investment strategies. Loss aversion can lead to poor decisions, which can have a negative influence on an investor's wealth (Odean, 1999).
Overconfidence bias is the tendency for a person to overestimate their abilities. Overconfidence bias can be defined as an unwarranted and often illogical faith that an investor has in their ability to predict the market. Some investors believe that they are somehow gifted and have special intuition and reasoning skills that help them predict the outcome of the market. This could be because they believe that they have some special skills. Alternatively, they might also falsely think that they have access to superior information, which is why their decisions will always be better. In simpler words, overconfidence bias is a belief amongst investors that they are smarter than everyone else (Nevins, 2004). According to Odean (1999), overconfidence bias often leads people to overestimate their understanding of financial markets or specific investments and disregard data and expert advice. This often results in ill-advised attempts to time the market or build concentrations in risky investments they may consider a sure thing.
Four questions were posed to investors in this study to assess their overconfidence bias. The result is shown in Table 6. The table shows that individual stockholders at DSE have a moderate level of confidence. The reason behind this moderate level of confidence might be the status of the security market. Bangladeshi security market is a frontier market with many fluctuations in its security prices irrespective of the acts of the listed stock issuing firms. Therefore, investors are unable to predict market trends and, hence, have less confidence in their decisions.
People generally trust their friends, colleagues, family members and relatives, and they take opinions from them while making a decision. This reflects the herding behavior of human beings. In stock markets, this herding behavior is apparent as individual investors usually follow a mass without understanding the company fundamentals. Sometimes, they might follow the recommendations provided by famous security analysts. Several studies have found that investors trading in the stock market have a herding behavior (Hilbert, 2012). However, in our analysis, we have found a low level of herding behavior among the individual investors at DSE, which is evident in Table 7. This low effect of herding variables can be clarified by the fact the DSE has been operating for many years while being significantly impacted by share market gambling. Thus, individual investors at the DSE might have grown a tendency to depend on their skills and knowledge of the stock market and anticipation of market returns. Investors with more experience, maturity and expertise may now make proper use of different information from diverse sources when making investing decisions. Hence, from the survey, we found a low effect of herding behavior.
4.2 Chi-square test
The Pearson chi-square test was used to see if there was a relationship between investor behavioral traits and stock investment decisions at the DSE. The results of the Pearson chi-square test have shown at 5% significance level, p < 0.05, which indicates that there is a significant relationship between the four behavioral traits of investors and the stock investment decision-making at DSE (Tables 8–11). So, the four null sub-hypotheses ((H0)1-1, (H0)1-2, (H0)1-3, (H0)1-4) under the main null hypothesis ((H0)1) are rejected. Tables 12 and 13 show that the main hypothesis is rejected at 5% significance level where p < 0.05.
4.3 ANOVA test
A one-way ANOVA was conducted to test whether the stock investment decision that individual investors at DSE make is significantly impacted by their behavioral traits like loss aversion (LA), overconfidence (OC), herding (HR) and risk perception (RP). Table 14 shows the results which indicate that behavioral traits of investors have a statistically significant impact on the investment decision-making at DSE at the p < 0.05 level for the three conditions [F(3, 4,492) = 456.042, p = 0.000]. So, again four null sub-hypotheses ((H0)1-1, (H0)1-2, (H0)1-3, (H0)1-4) under the main null hypothesis ((H0)1) are rejected.
4.4 Paired-samples t-test
A paired-samples t-test was conducted to test the significance of the mean differences among the four behavioral factors on stock investment decision-making at DSE. Table 15 shows the results of paired-samples t-test which indicate that there is significant difference among the four behavioral traits on stock investment decision-making at DSE at the p < 0.05 level for all six pairs. So, it is clear from the test that the fifth sub-hypothesis ((H0)1-5) is rejected.
Table 16 summarizes the descriptive statistics about behavioral factors. The table shows that investor behavioral traits have a significant impact on investment decision-making where both loss aversion and risk perception have a high impact on investment decision-making with overconfidence having a moderate impact and herding having a low impact on investment decision-making.
5. Discussion
The objective of this study was to see if behavioral factors influence individual DSE investors' stock investment decisions, as well as to determine the relative importance of behavioral factors in influencing individual investors' stock investment decisions. The factors that influence investment behavior were investigated using behavioral finance theory among 281 individual investors on the DSE in Bangladesh.
By applying the chi-square test, one-way ANOVA and descriptive analysis, loss aversion, overconfidence, herding and risk perception are identified as psychological biases that influence investors to make irrational decisions. With scores of 4.31 and 4.14, loss aversion and risk perception are found to have the highest impact on individual investor decision-making. The other two biases (overconfidence and herding) have a smaller impact. With the result of paired-samples t-test, it was found that there is significant difference among the four behavioral traits on stock investment decision-making at DSE.
From the above analysis, it can be concluded that Bangladeshi security investors' investment decisions at the DSE are not always rational as traditional finance theory, rather affected by their behavioral biases, resulting in irrational decisions. Therefore, it could be asserted that most investors are risk-averse and prefer to invest in well-known companies that generate consistent profits. The findings are consistent with other researchers and highlight the fact that investors hardly act according to the norms recommended in the financial theories. Gächter et al. (2010) focused on behavioral finance to better understand how human, social, cognitive and emotional biases affect investment decisions and market prices.
The study has found that:
Individual investors of Bangladesh often make investment decisions emotionally.
Risk aversion and risk perception are the two most influential emotional dimensions that impact investors' investment decisions.
6. Conclusion and limitations
Behavioral finance helps to study the psychological variables that can affect financial decision-making. Gächter et al. (2010) focused on behavioral finance to better understand how human, social, cognitive and emotional biases affect investment decisions and market prices. So, before making any investment decision to maximize wealth, it is always a good idea to understand the features of securities markets using a combination of psychology and finance. The evidence of behavioral persuasions suggests that individual investors need to control their behavioral emotions in making investment decisions. Identifying the relative importance of biases in influencing a decision may help them gain a better understanding of behavioral psychology, which will help investors develop a better investment strategy. This study will be closer to reality in terms of behavioral finance theory, and it is expected to provide additional important perceptions in terms of selecting investment strategy and psychological factors to explain market irregularities.
The findings are grounded on a small portion of investors at DSE on some particular days, which is not sufficient to study individual investors' entire complex decision-making behavior from various angles. Many respondents were reluctant and even confused to disclose their behavioral aspects. These, along with biased and careless answers, may impede the identification of the actual scenario of the behavioral responses in decision-making that demand further study.
Figures
Statements for each factor in the questionnaire
| Variables | Author(s) |
|---|---|
| Loss aversion | |
| A large loss in my investment is more important to me than missing a substantial gain (profits) | Chun and Ming (2009) |
| Large price drops in my invested stocks make me nervous | |
| I will avoid increasing my investment when the market performs poorly | |
| I will not sell shares that have observed a decline in value whereas sell shares that have a rise in value | |
| Risk perception | |
| I generally do not have a fear of capitalizing on stocks with a certain gain | Areiqat et al. (2019) |
| I am careful about stocks that show unexpected fluctuations in price or transaction | |
| I generally have concerns about investing in stocks with a historical adverse performance in trading | |
| I don't consider the idea of trading in the stock market attractive | |
| Overconfidence | |
| I sense more assurance in my own investment views over others | Ngoc (2014) |
| I don't look up to others in case of making investment decisions | |
| I am certain of my expertise and experience in outpacing the stock market | |
| I am successful in an environment where others fail | |
| Herding | |
| My investment choices are affected by the choices of choosing stocks of other investors | Tan et al. (2008) |
| My investment choices are affected by the choices of the stock volume of other investors | |
| My investment decisions are affected by the decisions of buying and selling stocks of other investors | |
| I generally respond fast to the fluctuations of other investors' choices and track their responses to the stock market | |
Measurement scale
| Mean values | Impacts |
|---|---|
| <2.00 | Very low |
| 2.00–2.80 | Low |
| 2.81–3.60 | Moderate |
| 3.61–4.40 | High |
| >4.41 | Very high |
Source(s): Authors' assumption
Demographic data
| Area | Grouping | Occurrence | Percentage |
|---|---|---|---|
| Gender | Male | 268 | 95.4 |
| Female | 13 | 4.6 | |
| Age | Less than 40 years | 153 | 54.4 |
| 40–50 years | 71 | 25.3 | |
| Above 50 years | 57 | 20.3 | |
| Academic qualification | SSC or equivalent | 17 | 6.0 |
| HSC or equivalent | 37 | 13.2 | |
| Diploma or equivalent | 24 | 8.5 | |
| Honors or equivalent | 56 | 19.9 | |
| Masters or equivalent | 136 | 48.4 | |
| Others | 11 | 3.9 | |
| Stock investment training | Yes | 80 | 28.5 |
| No | 201 | 71.5 | |
| Experience in the stock market | Less than 1 year | 15 | 5.3 |
| 1–3 years | 47 | 16.7 | |
| 3–5 years | 76 | 27.0 | |
| 5–10 years | 62 | 22.1 | |
| Over 10 years | 81 | 28.8 |
Loss aversion
| SL. | Loss aversion | Mean | SD | Meaning |
|---|---|---|---|---|
| 1 | A large loss in my investment is more important to me than missing a substantial gain (profits) | 4.36 | 0.97 | High impact |
| 2 | Large price drops in my invested stocks make me nervous | 4.40 | 0.86 | High impact |
| 3 | I will avoid increasing my investment when the market performs poorly | 4.32 | 0.84 | High impact |
| 4 | I will not sell shares that have observed a decline in value whereas sell shares that have a rise in value | 4.17 | 0.94 | High impact |
| Average | 4.31 | 0.91 | High impact | |
Risk perception
| SL. | Risk perception | Mean | S.D. | Meaning |
|---|---|---|---|---|
| 1 | I generally do not have a fear of capitalizing on stocks with a certain gain | 4.20 | 0.82 | High impact |
| 2 | I am careful about stocks that show unexpected fluctuations in price or transaction | 4.20 | 0.86 | High impact |
| 3 | I generally have concerns about investing in stocks with a historical adverse performance in trading | 4.05 | 0.88 | High impact |
| 4 | I do not consider the idea of trading in the stock market attractive | 4.10 | 0.74 | High impact |
| Average | 4.14 | 0.828 | High impact | |
Overconfidence
| SL. | Overconfidence | Mean | S.D. | Meaning |
|---|---|---|---|---|
| 1 | I sense more assurance in my own investment views over others | 3.70 | 1.37 | High impact |
| 2 | I do not look up to others in case of making investment decisions | 3.35 | 1.22 | Moderate impact |
| 3 | I am certain of my expertise and experience in outpacing the stock market | 3.55 | 1.23 | Moderate impact |
| 4 | I am successful in an environment where others fail | 3.34 | 1.20 | Moderate impact |
| Average | 3.48 | 1.265 | Moderate impact | |
Herding
| SL | Herding | Mean | S.D. | Meaning |
|---|---|---|---|---|
| 1 | My investment choices are affected by the choices of choosing stocks of other investors | 2.82 | 1.32 | Moderate impact |
| 2 | My investment choices are affected by the choices of the stock volume of other investors | 2.74 | 1.26 | Low impact |
| 3 | My investment decisions are affected by the decisions of buying and selling stocks of other investors | 2080 | 1.27 | Low impact |
| 4 | I generally respond fast to the fluctuations of other investors' choices and track their responses to the stock market | 2.77 | 1.31 | Low impact |
| Average | 2.78 | 1.289 | Low impact | |
Chi-square test of loss aversion
| Value | df | Asymptotic significance (2-sided) | |
|---|---|---|---|
| Pearson chi-square | 4496.000a | 16 | 0.000 |
| Likelihood ratio | 2442.195 | 16 | 0.000 |
| Linear-by-linear association | 1056.541 | 1 | 0.000 |
| N of valid cases | 1124 |
Note(s): a9 cells (36.0%) have expected count less than five. The minimum expected count is 0.23
Source(s): Calculation by authors based on survey
Chi-square test of overconfidence
| Value | df | Asymptotic significance (2-sided) | |
|---|---|---|---|
| Pearson chi-square | 4496.000a | 16 | 0.000 |
| Likelihood ratio | 3341.978 | 16 | 0.000 |
| Linear-by-linear association | 392.090 | 1 | 0.000 |
| N of valid cases | 1124 |
Note(s): a0 cells (0.0%) have expected count less than five. The minimum expected count is 9.08
Source(s): Calculation by authors based on survey
Chi-square test of herding
| Value | df | Asymptotic significance (2-sided) | |
|---|---|---|---|
| Pearson chi-square | 4496.000a | 16 | 0.000 |
| Likelihood ratio | 3419.563 | 16 | 0.000 |
| Linear-by-linear association | 125.672 | 1 | 0.000 |
| N of valid cases | 1124 |
Note(s): a0 cells (0.0%) have expected count less than five. The minimum expected count is 13.24
Source(s): Calculation by authors based on survey
Chi-square test of risk perception
| Value | df | Asymptotic significance (2-sided) | |
|---|---|---|---|
| Pearson chi-square | 4496.000a | 16 | 0.000 |
| Likelihood ratio | 2571.372 | 16 | 0.000 |
| Linear-by-linear association | 353.034 | 1 | 0.000 |
| N of valid cases | 1124 |
Note(s): a10 cells (40.0%) have expected count less than five. The minimum expected count is 0.04
Source(s): Calculation by authors based on survey
Chi-square test
| Value | DF | Asymp. Sig. (2-Sided) | |
|---|---|---|---|
| Pearson chi-square | 1.172E3a | 12 | 0.000 |
| Likelihood ratio | 1.214E3 | 12 | 0.000 |
| Linear-by-linear association | 54.956 | 1 | 0.000 |
| N of valid cases | 4,496 |
Source(s): Calculation by authors based on survey
Symmetric measures
| Value | Approx. Sig | ||
|---|---|---|---|
| Nominal by nominal | Phi | 0.511 | 0.000 |
| Cramer's V | 0.295 | 0.000 | |
| Contingency coefficient | 0.455 | 0.000 | |
| N of valid cases | 4,496 | ||
Source(s): Calculation by authors based on survey
ANOVA test decision-making
| Sum of squares | DF | Mean square | F | Sig | |
|---|---|---|---|---|---|
| Between groups | 1631.794 | 3 | 543.931 | 456.042 | 0.000 |
| Within groups | 5357.714 | 4,492 | 1.193 | ||
| Total | 6989.508 | 4,495 |
Source(s): Calculation by authors based on survey
Summary of paired-samples t-test
| 95% confidence interval of the difference | t | DF | Sig. (2-Tailed) | |||
|---|---|---|---|---|---|---|
| Lower | Upper | |||||
| Pair 1 | LA - OC | 0.56624 | 1.08876 | 10.080 | 3 | 0.002 |
| Pair 2 | LA - HR | 1.36060 | 1.69940 | 28.744 | 3 | 0.000 |
| Pair 3 | LA - RP | 0.04219 | 0.30781 | 4.193 | 3 | 0.025 |
| Pair 4 | OC - HR | 0.47770 | 0.92730 | 9.945 | 3 | 0.002 |
| Pair 5 | OC - RP | −0.93874 | −0.36626 | −7.255 | 3 | 0.005 |
| Pair 6 | HR - RP | −1.49523 | −1.21477 | −30.750 | 3 | 0.000 |
Summary of factors impacting decision-making
| Factors | Mean | Standard deviation | Meaning | Ranking |
|---|---|---|---|---|
| Loss aversion | 4.31 | 0.907 | High impact | 1 |
| Overconfidence | 3.48 | 1.265 | Moderate impact | 2 |
| Herding | 2.78 | 1.289 | Low impact | 3 |
| Risk perception | 4.14 | 0.828 | High impact | 1 |
| Average | 3.68 | 1.247 | High impact |
Appendix Questionnaire for survey
Section A: General information: (Please tick the correct answer)
Gender: Male [ ] Female [ ]
Age: Less than 40 years [ ] 40–50 years [ ] Above 50 years [ ]
What level of education have you completed?
SSC or equivalent [ ] HSC or equivalent [ ] Diploma or equivalent [ ] Honors or equivalent [ ] Masters or equivalent [ ] Others [ ]
Have you attended any course of stock exchange? Yes [ ] No [ ]
How long have you been participating in the stock market?
Less than 1 year [ ] 1–3 years [ ] 3–5 years [ ] 5–10 years [ ] Over 10 years [ ]
Section B:Behavioral factors influencing investment decisions
Please evaluate the degree of your agreement with the impacts of behavioral factors on your investment decision-making:
| Strongly agree(5) | Agree | Neutral | Disagree | Strongly disagree | ||
|---|---|---|---|---|---|---|
| Loss aversion | ||||||
| 1 | A large loss in my investment is more important to me than missing a substantial gain (profits) | |||||
| 2 | Large price drops in my invested stocks make me nervous | |||||
| 3 | I will avoid increasing my investment when the market performs poorly | |||||
| 4 | I will not sell shares that have observed a decline in value whereas sell shares that have a rise in value | |||||
| Risk perception | ||||||
| 5 | I generally do not have a fear of capitalizing on stocks with a certain gain | |||||
| 6 | I am careful about stocks that show unexpected fluctuations in price or transaction | |||||
| 7 | I generally have concerns about investing in stocks with a historical adverse performance in trading | |||||
| 8 | I do not consider the idea of trading in the stock market attractive | |||||
| Overconfidence | ||||||
| 9 | I sense more assurance in my own investment views over others | |||||
| 10 | I don't look up to others in case of making investment decisions | |||||
| 11 | I am certain of my expertise and experience in outpacing the stock market | |||||
| 12 | I am successful in an environment where others fail | |||||
| Herding | ||||||
| 13 | My investment choices are affected by the choices of choosing stocks of other investors | |||||
| 14 | My investment choices are affected by the choices of the stock volume of other investors | |||||
| 15 | My investment decisions are affected by the decisions of buying and selling stocks of other investors | |||||
| 16 | I generally respond fast to the fluctuations of other investors' choices and track their responses to the stock market | |||||
References
Akerlof, G.A. and Dickens, W.T. (1982), “The economic consequences of cognitive dissonance”, American Economic Review, Vol. 72, pp. 307-319.
Akhter, R. and Ahmed, S. (2013), “Behavioral aspects of individual investors for investment in Bangladesh stock market”, International Journal of Ethics in Social Sciences, Vol. 1 No. 1, ISSN (P): 2308-5096, available at: http://dspace.iiuc.ac.bd:8080/xmlui/handle/88203/244.
Andrade, E.B. (2005), “Behavioral consequences of effect: combining evaluative and regulatory mechanisms”, Journal of Consumer Research, Vol. 32 No. 3, pp. 355-362, doi: 10.1086/497546.
Areiqat, A.Y., Abu-Rumman, A., Al-Alani, Y.S. and Alhorani, A. (2019), “Impact of behavioral finance on stock investment decisions applied study on a sample of investors at Amman stock exchange”, Academy of Accounting and Financial Studies Journal, Vol. 23 No. 2, pp. 1-17.
Aronson, E. (1979), The Social Animal, 3d ed., W. H. Freeman, San Francisco.
Bakar, S. and Yi, A.N.C. (2016), “The impact of psychological factors on investors' decision making in Malaysian stock market: a case of Klang Valley and Pahang”, Procedia Economics and Finance, Vol. 35, pp. 319-328, doi: 10.1016/S2212-5671(16)00040-X.
Baker, M. and Wurgler, J. (2007), “Investor sentiment in the stock market”, Journal of Economic Perspectives, Vol. 21 No. 2, pp. 129-152, doi: 10.1257/jep.21.2.129.
Banerjee, A.V. (1992), “A simple model of herd behavior”, The Quarterly Journal of Economics, Vol. 107 No. 3, pp. 797-817, doi: 10.2307/2118364.
Barber, B.M. and Odean, T. (2000), “Trading is hazardous to your wealth: the common stock investment performance of individual investors”, The Journal of Finance, Vol. 55 No. 2, pp. 773-806, doi: 10.1111/0022-1082.0022602271190900.
Barber, B. and Odean, T. (2001), “Boys will be boys: gender, overconfidence, and common stock investment”, Quarterly Journal of Economics, Vol. 116 No. 1, pp. 261-292, doi: 10.1162/003355301556400.
Barberis, N. (2001), “Mental accounting, loss aversion, and individual stock returns”, The Journal of Finance, Vol. 56 No. 4, pp. 1247-1292, doi: 10.1111/0022-1082.00367.
Bashar, Y., Almansour, B. and Arabyat, Y. (2017), “Investment decision making among gulf investors: behavioral finance perspective”, International Journal of Management Studies, Vol. 51, pp. 222-241, doi: 10.32890/ijms.24.1.2017.10476.
Bouwman, C.H.S. (2014), “Managerial optimism and earnings smoothing”, Journal of Banking and Finance, Vol. 41, pp. 283-303, doi: 10.1016/j.jbankfin.2013.12.019.
Caparrelli, F., D'Arcangelis, A.M. and Cassuto, A. (2004), “Herding in the Italian stock market: a case of behavioral finance”, Journal of Behavioral Finance, Vol. 5 No. 4, pp. 222-230, doi: 10.1207/s15427579jpfm0504_5.
Chaudhary, A.K. (2013), “Impact of behavioral finance in investment decisions and strategies: a fresh approach”, International Journal of Management Research and Business Strategy, Vol. 2 No. 2, pp. 66-83.
Choi, N. and Skiba, H. (2015), “Institutional herding in international markets”, Journal of Banking and Finance, Vol. 55, pp. 246-259, doi: 10.1016/j.jbankfin.2015.02.002.
Chun, W.W. and Ming, L.M. (2009), “Investor behaviour and decision-making style: a malaysian perspective”.
De Bondt, W.F.M. and Thaler, R. (1985), “Does the stock market overreact?”, The Journal of Finance, Vol. 40 No. 3, pp. 793-805, doi: 10.1111/j.1540-6261.1985.tb05004.x.
De Bondt, W.F.M. and Thaler, R. (1995), “Financial decision-making in markets and firms: a behavioral perspective”, in Jarrow, R. (Ed.), Handbook in OR & MS, Elsevier, The Netherlands, Vol. 9, pp. 385-410, doi: 10.1016/S0927-0507(05)80057-X.
Dickinson, J.P. and Muragu, K. (1994), “Market efficiency in developing countries: a case study of the Nairobi stock exchange”, Journal of Business Finance and Accounting, Vol. 21 No. 1, pp. 133-150. DOI: 10.1111/j.1468-5957.1994.tb00309.x.
Durham, J. (2002), “The effects of stock market development on growth and private investment in lower-income countries”, Emerging Markets Review, Vol. 3, pp. 211-232, doi: 10.1016/S1566-0141(02)00022-5.
Fama, E.F. (1965), “The behavior of stock-market prices”, The Journal of Business, Vol. 38 No. 1, pp. 34-105, available at: http://www.jstor.org/stable/2350752.
Fama, E.H. (1991), “Efficient capital markets: II”, The Journal of Finance, Vol. 46 No. 5, pp. 1575-1617, doi: 10.1111/j.1540-6261.1991.tb04636.x.
Fogel, S.O. and Berry, T. (2006), “The disposition effect and individual investor decisions: the roles of regret and counterfactual alternatives”, Journal of Behavioral Finance, Vol. 7 No. 2, pp. 107-116, doi: 10.1207/s15427579jpfm0702_5.
Gächter, S., Johnson, E. and Herrmann, A. (2010), “Individual-level Loss aversion in riskless and risky choices”, (Discussion Paper No. IZA DP No. 2961), IZA Institute of Labor Economics, Bonn, Germany, available at: https://ftp.iza.org/dp2961.pdf.
Grinblatt, M. and Bing, H. (2005), “Prospect theory, mental accounting, and momentum”, Journal of Financial Economics, Vol. 78 No. 2, pp. 311-339, doi: 10.1016/j.jfineco.2004.10.006.
Grinblatt, M. and Keloharju, M. (2001), “What makes investors trade?”, The Journal of Finance, Vol. 56 No. 2, pp. 589-616, available at: http://www.jstor.org/stable/222575.
Hilbert, M. (2012), “Toward a synthesis of cognitive biases: how noisy information procession can bias human decision making”, Psychological Bulletin, Vol. 138 No. 2, pp. 211-237, doi: 10.1037/a0025940.
Hirshleifer, D. (2001), “Investor psychology and asset pricing”, Journal of Finance, Vol. 56 No. 4, pp. 1533-1597.
Jokar, H. and Daneshi, V. (2018), “The impact of investors' behavior and managers' overconfidence on stock return: evidence from Iran”, Cogent Business and Management, Vol. 5 No. 1, doi: 10.1080/23311975.2018.1559716.
Kahneman, D. and Tversky, A. (1979), “Prospect theory: an analysis of decision under risk”, Econometrica: Journal of the Econometric Society, Vol. 47, pp. 263-291, doi: 10.2307/1914185.
Liu, Y., Wu, A.D. and Zumbo, B.D. (2010), “The impact of outliers on Cronbach's coefficient alpha estimate of reliability: ordinal/rating scale item responses”, Educational and Psychological Measurement, Vol. 70 No. 1, pp. 5-21, doi: 10.1177/0013164409344548.
Mouna, A. and Anis, J. (2014), “Stock return and investor sentiment: evidence for an emerging market”, I-Manager’s Journal on Management, Vol. 9, pp. 32-39, doi: 10.26634/jmgt.9.2.2991.
Nevins, D. (2004), “Goals-based investing: integrating traditional and behavioral finance”, The Journal of Wealth Management, Vol. 6 No. 4, pp. 8-23, doi: 10.3905/jwm.2004.391053.
Ngoc, L.T.B. (2014), “Behavior pattern of individual investors in stock market”, International Journal of Business and Management, Vol. 9 No. 1, doi: 10.5539/ijbm.v9n1p1.
Nunnally, J. (1978), Psychometric Theory, McGraw Hill Book Company, New York.
Odean, T. (1999), “Do investors trade too much?”, American Economic Review, Vol. 89 No. 5, pp. 1279-1298, doi: 10.1257/aer.89.5.1279.
Olsen, R.A. (1998), “Behavioral finance and its implications for stock-price volatility”, Financial Analysts Journal, Vol. 54 No. 2, pp. 10-18, available at: http://www.jstor.org/stable/4480062.
Raiffa, H. and Raiffa, H. (1968), Decision Analysis: Introductory Lectures on Choices under Uncertainty, Addison-Wesley, MA.
Ritter, J.R. (2003), “Behavioral finance”, Pacific-Basin Finance Journal, Vol. 11 No. 4, pp. 429-437, doi: 10.1016/S0927-538X(03)00048-9.
Sent, E. (2004), “Behavioral economics: how psychology made its (limited) way back into economics”, History of Political Economy, Vol. 36 No. 4, pp. 735-760, doi: 10.1215/00182702-36-4-735.
Shelby, L.B. (2011), “Beyond Cronbach's alpha: considering confirmatory factor analysis and segmentation”, Human Dimensions of Wildlife, Vol. 16 No. 2, pp. 142-148, doi: 10.1080/10871209.2011.537302.
Shleifer, A. (2000), Inefficient Markets: an Introduction to Behavioral Finance, Oxford University Press, Oxford.
Sochi, M.H. (2018), “Behavioral factors influencing investment decision of the retail investors of Dhaka stock exchange: an empirical study”, The Cost and Management, Vol. 46 No. 1, pp. 20-29.
Statman, M. (1999), “Behavioral finance: past battles and future engagements”, Financial Analysts Journal, Vol. 55 No. 6, pp. 18-27, doi: 10.2469/faj.v55.n6.2311.
Tan, L., Chiang, T.C., Mason, J.R. and Nelling, E. (2008), “Herding behavior in Chinese stock markets: an examination of A and B shares”, Pacific-Basin Finance Journal, Vol. 16, pp. 61-77, doi: 10.1016/j.pacfin.2007.04.004.
Trehan, B. and Sinha, A.K. (2018), “A study of the existence of overconfidence biases among investors and its impact on investment decisions”, SSRN Journal, Vol. 61, pp. 54-79, available at: https://ssrn.com/abstract=3285671.
Tversky, A. and Kahneman, D. (1974), “Judgment under uncertainty: heuristics and biases”, Science, Vol. 185 No. 4157, p. 1124, LP – 1131, doi: 10.1126/science.185.4157.1124.
Waweru, N., Munyoki, E. and Uliana, E. (2008), “The effects of behavioral factors in investment decision-making: a survey of institutional investors operating at the Nairobi stock exchange”, International Journal of Business and Emerging Markets, Vol. 1, pp. 24-41, doi: 10.1504/IJBEM.2008.019243.