A Study on Familiarity Bias in Investor Decision-Making

Abstract

For a considerable amount of time, conventional finance theories have maintained that investors make judgments based on logical analyses and economic models. But the development of behavioral finance casts doubt on this notion, showing that emotions, cognitive biases, and natural tendencies frequently influence human decision-making. This study looks into familiarity bias, a psychological component that affects investor decisions, and how common it is. Using a thorough methodology, the study examines investor choices in the banking, FMCG, and auto mobile industries. Results repeatedly point to a considerable preference for well-known businesses, even in situations when risks and returns on investment are similar. Investors exhibit behavioral biases and brand loyalty; familiarity bias is particularly persistent across various investor profiles. The findings show that investors, irrespective of expertise level, devote a sizeable chunk of their capital to reputable businesses. The results of hypothesis testing show relationships between gender and familiarity bias as well as between different investor types according to holding length and risk. However, monthly income did not show a significant correlation with familiarity bias, indicating that it is prevalent at different income levels. The study comes to the conclusion that a thorough understanding of investor decision-making requires an awareness of behavioral biases.

Introduction

I. INTRODUCTION

Everyone believed for a long time that traditional finance theory was correct since it claimed that investors make thoughtful judgments based on estimates or economic models and that they think logically. Nevertheless, numerous studies shown that human decisions frequently rely on innate tendencies, instincts, and subconscious emotional or cognitive prejudices. After enough data was gathered to support specific human behaviors that defied conventional finance theory, a new field known as behavioral finance emerged. The study of behavior finance focuses on how psychological factors influence financial markets and decision-making processes. Psychology examines human judgment, conduct, and wellbeing, therefore it can offer valuable insights into how human behavior deviates from conventional economic theories [1-5].

As a result, in today's global financial markets, investment decision processes predicated on projections and the extensive knowledge of market participants are becoming increasingly implausible. International scientists Berber & Odean (1999), Huberman (2001), Pompian (2008), and Shefrin (2011) have discovered that people's psychological states have an impact on their decision-making when it comes to investments. An investor's perspective is greatly impacted by a variety of environmental shifts, such as fluctuations in the price and the state of the economy. People's incessant fear of losing money causes them to respond impulsively to market fluctuations, alter their long-term investing goals on the spur of the moment, and start to question their investments. Such circumstances often lead to irrational decisions that result in inefficient investments or escalating losses, which in turn lowers the pool of potential investors. As a result, financial behavior is a science that examines the subtleties of market participants' conduct and exposes their illogical reasons for making judgments. This helps to mitigate the influence of financial behavior on investment decisions and draws in more willing investors [6, 7].

The efficient market hypothesis serves as the cornerstone of conventional finance. According to this concept, investors are thought to be rational and have access to market data and asset values. Even if contemporary finance has developed gradually, it is still challenging to provide a scientific justification for why people act irrationally while handling money. While behavior finance incorporates the importance of what investors should do and combines the fundamentals of traditional finance with what people really do when making investment decisions, traditional finance presumes that people rationalize and improve their financial judgments. Sociology and psychology are regarded as essential fields of study that advance the subject of behavioral finance research. Numerous research have shown that investors have behavioral biases that run counter to the efficient market hypothesis. The classical rationality theory is replaced by the behavioral finance approach, which contends that behavioral biases have an impact on people [8-12].

According to H. Shefrin (2001), behavioral finance is the study of how psychology affects financial markets and financial decision-making. A recent development in the financial markets, behavioral finance was brought about by challenges in traditional finance.  According to Barberis (2002), it is perceived as a financial event in which agents lack perfect rationality. "Daniel Kahneman," the father of behavioral finance, is an economist who won the Nobel Prize for his prospect theory. Three of the most influential pioneers in the field of behavioral finance are Richard Thaler, Amos Tversky, and Daniel Kahneman. They developed behavioral biases that are thought to be the foundation of behavioral finance. The inconsistencies between the traditional finance and behavioral finance areas are often caused by behavioral biases. Behavioral finance has emerged as a result of multiple research that cast doubt on rationality. The asymmetry in human decision-making between benefits and losses can be attributed to behavioral biases. When faced with a decision involving gains, the same person who is risk-averse turns into a risk-taker when faced with a decision involving avoiding losses. A few biases that are thought of as the fundamental components of behavioral finance and have a substantial impact on an individual investor's decision-making process are overconfidence, herding, anchoring, cognitive dissonance, availability bias, self-attribution, mental accounting, framing, and representative bias [12-20].

According to behavioral finance, a variety of behavioral biases can impact an investor's decision-making process, leading them to diverge from rationality and make illogical choices. This study provides a thorough analysis of behavioral biases in the decision-making process of individual investors. Recent advancements in this field of study have made theoretical and empirical contributions more relevant. Following a review of the literature, it was determined that one study with a systematic examination of behavioral biases was necessary. Four distinct behavioral biases have been used as a framework in this study to examine how they affect the way that individuals make financial decisions. It appears that the goal of this approach is to investigate different types of behavioral biases using the behavioral finance field as a lens [21].

The objectives of the present work include a thorough investigation of investor familiarity biases, with an emphasis on the FMCG, banking, and auto industries. In order to offer insight on how investor decisions may be influenced by their familiarity with companies in various industries, the study first attempts to determine the existence and magnitude of familiarity biases within these particular sectors. Second, by exploring the reasons underlying such conduct, the study aims to determine whether investors have a propensity to invest in amounts greater than their anticipated financial returns. Thirdly, the study looks into possible connections between investor familiarity biases and income levels, asking whether financial standing influences investors' decisions to make familiarity-based investments. Furthermore, an analysis of the relationship between familiarity biases and investor type will provide insights into how various investor profiles can be vulnerable to them. Finally, the study intends to investigate if prior investing experience acts as a mitigating factor, thereby lowering investors' familiarity bias and offering important new perspectives on the processes involved in making investment decisions. This study explores familiarity bias in behavioral finance with a particular focus on individual stock market participants who live in Surat's suburbs. It investigates the complex relationship between familiarity bias and monthly income, investor characteristics, and the effect of prior investing experience on biased choices in three different segments of the Indian stock market. The study also looks into the relationship between return expectations and familiarity bias, particularly as it relates to human emotions associated with certain stocks or businesses.

II. DYNAMIC INTERFACE OF BRAND FAMILIARITY AND INVESTMENT CHOICES

A sophisticated investigation that goes beyond traditional financial measures examines the relationship between brand familiarity and investing choices. Aspara and Tikkanen's research clarifies the complex relationship between investor conduct and consumer mood. Investors frequently exhibit a readiness to purchase the stocks of particular companies, a decision that is heavily impacted by emotional ties to the related brand as well as financial factors. The emotional evaluation of a company's product brand is crucial to this relationship since it shows that investors' decisions are influenced by feelings rather than just logic. Furthermore, a company's product categories' perceived personal significance is crucial. Aside from standard financial analyses, investors also take into account the diverse range of activities and interests linked to these products [22, 23].

When corporations with relatively unknown holding organizations that house well-known subsidiary companies are taken into consideration, the complexity of this relationship becomes evident. Investors may have favorable opinions of a subsidiary even though they are unaware of its connection to a particular parent firm. As a result, it can be difficult to distinguish between consumer preferences and investment decisions since investment decisions are frequently influenced by satisfied customers with subsidiary products. This complex network of relationships also includes people's opinions about a firm, their propensity to buy its stock, and their propensity to use its goods. The fact that investors also play the role of consumers increases the level of entanglement because people tend to favor goods from businesses in which they own stock [24].

Investment and consumption sectors have always been seen as two distinct industries. Behavioral finance concepts, in particular the notion of familiarity bias, highlight the necessity of viewing these domains as interrelated. Understanding the mutually beneficial relationship between consumer attitude and investing behavior is essential to having a thorough grasp of the complex decision-making processes that influence financial environments.

III. BEHAVIORAL BIASES

A. Overconfidence

Overconfidence affects individual investment decisions in behavioral finance, whether they are made in the stock market or other financial domains. A recurring theme in behavioral finance, this psychological bias results in mispricing, which introduces inefficiencies and increases volatility and return unpredictability. To put it simply, overconfidence causes investors to overvalue parameters and make poor assumptions, which impedes accurate distribution knowledge. Interestingly, mutual fund managers typically underperform the market when they are overconfident. Despite its pervasive influence, overconfidence in economics is still mostly disregarded in favor of rational agent behavior. Overconfidence bias has been connected to higher trading volume and price volatility in stock markets and is frequently linked to the disregard of unknown factors. Overreactions by investors as a result of misplaced confidence in information processing are common. On the other hand, some contend that overconfidence is a good indicator since it can be used to foretell an investor's performance in the stock market and coincide with abnormalities in the market. Therefore, overconfidence is a judgmental error in which people exaggerate the likelihood of a specific outcome or overestimate their abilities and expertise [25].

B. Disposition Effect

The disposition effect, a prominent phenomena in investor behavior, characterizes investors' propensity to hang onto realized losses in the hopes of realizing gains in the future. The difference between the percentage of realized gains and losses is used to measure this effect. The disposition effect is a behavioral phenomenon that academics have discovered as occurring when investors have a tendency to hang onto failing assets longer than winning ones while dumping successful ones sooner. Individual investments suffer as a result of this behavioral propensity. Losing endeavors usually continue to perform below expectations, whereas profitable investments frequently continue to perform better. It is interesting that the appearance of disposition effect appears to be insensitive to experimental interventions aimed at modifying projected future gains or losses. This robustness of the disposition effect in influencing investors' decision-making processes is highlighted by its resistance to experimental modifications [26].

C. Anchoring

Among the psychological biases in decision-making that have been researched in great detail is anchoring. According to Wright and Anderson's 1989 analysis, investors' decision-making processes are greatly impacted by this cognitive bias. An explanation of anchoring, which is characterized as a cognitive bias, is people's propensity to excessively depend on the first piece of information while making judgments. Particularly when it comes to making poor decisions, investors have a propensity to base their stock purchases on the stock's most recent high price [27]. In uncertain situations like the 52-week high and momentum tactics, the anchoring bias becomes an important criterion for measuring stock returns or profitability through a behavioral approach. Studies demonstrate how anchoring has a detrimental effect on the choices that traders and investors make about their investments. Anchoring, perceived as a bias in judgment, suggests that decisions end up revolving around the judge's initial point of evaluation. According to the anchoring-and-adjustment heuristic, inadequate adjustment leads to anchoring bias. According to this heuristic, people prefer to underreact by anchoring on notable events and then making adjustments based on information that is predictive. As a result, investors are forced to rely on recent market movements and levels due to the heuristic bias known as anchoring [28].

D. Herding Behavior

Herding is a phenomenon that is commonly observed in financial markets. It is a reflection of human nature's innate desire to refer to, observe, and emulate the conduct of others in unpredictable circumstances. Investors that exhibit this behavioral tendency stray from making logical decisions and instead choose to make decisions that are consistent with the views and opinions of their peers. As a result, the herding effect intensifies during periods of market distress, such as anomalies, price bubbles, or rumors. Herding is all about group imitation, which brings investors' movements closer together. Several studies show that herding behavior can result in similar movement patterns among individuals, which can have a major negative impact on welfare.  According to one viewpoint, herding behavior occurs when agents are given the opportunity to adopt the strategies of their network neighbors, which can have a feedback effect on the structure of the network and the results of games. This type of herding behavior presents a new class of corporate event called herding behavior, which is seen when agents follow their wealthier neighbors' strategies out of instinct [29].

IV. METHODOLOGY

This study's methodology displays thoughtful decisions made to maximize the research strategy. The 100 respondents in the sample size recognizes the limitations of extrapolating findings from a tiny portion of the total universe sample, allowing for a more in-depth examination of familiarity bias within this group. The Surat city suburbs are the exclusive geographic emphasis. Because only respondents in these areas were included in the survey distribution, the results are intrinsically restricted to this particular area. The study focuses on the banking, FMCG, and automotive sectors of the stock market as its three main focus areas. In order to provide a more targeted analysis of familiarity bias within certain segments, this selection is made based on the observed volume of interest among individual investors in these industries. Participants of any age are welcome to participate, but the study is limited to those who are active individual stock market investors. Relevance to this particular demographic's behavioral patterns is ensured by this strategic focus. Owing to geographical limitations, face-to-face conversations are replaced with an online survey. This recognizes the constraints of gaining a comprehensive grasp of human emotion and bias, along with possible difficulties in elucidating respondents' viewpoints, which could have been possible through in-person conversations. The decision to use an online survey is in accordance with the requirement to get beyond geographical constraints while taking into account any compromises in the breadth of the insights gained. For this study work, convenience sampling was specifically chosen as the sample technique. Using a non-probability technique, units are chosen for the sample depending on criteria like proximity to the study site, availability at a specific time, or research participation willingness. Convenience sampling is acknowledged in the world of research as one of the most often used and simple techniques for gathering data. This study paper's sample size is set at 100 respondents, and the sample unit is made up only of individual investors who have firsthand knowledge of the stock market's operations and functioning knowledge. This focused strategy guarantees that the study's conclusions are based on the knowledge of a particular group, whose expertise in the field adds to the breadth and significance of the information gathered. The main source of data used in this research article is primary data collected using questionnaire techniques. In order to look at familiarity bias, the poll focused on a variety of investors in Surat's suburbs. In response to predetermined scenarios, participants were asked to apply their knowledge and make decisions between organizations they were familiar with and ones they weren't. Rather of using corporate emblems or logos, the questionnaire gave a variety of situations across different sectors to measure the factors influencing investors' decisions.  The demographic information gathered from the respondents was imported into Excel and subjected to additional analysis using a variety of tests in order to perform hypothesis testing. Both published and unpublished secondary sources, such as journals, articles, and research papers, were consulted in order to provide strong evidence to support the main data. This all-encompassing strategy seeks to validate results and strengthen the research's credibility. For its online dissemination, the survey administration used Google Forms, a survey tool made available by Google. The survey was distributed to respondents via a hyperlink, and the utilization of Google Forms enabled effective data gathering, optimizing the analytic procedure for increased ease and efficiency.

V. RESULTS AND DISCUSSION

Figure 1 represents the demographic and socio-economic details of the respondents.

To investigate the extent to which familiarity bias affects investment decisions in a given industry and the impact of changes in financial returns on investment decisions, three distinct sectors are selected. Based on the overall number of retail investors in each sector, the sectors were selected. To find the point at which bias is overcome by return, two companies—one popular and one unpopular—are selected from each industry and given access to various risk and return scenarios. The banking, FMCG, and auto-mobile sectors are the ones being studied.

A. Banking

The study compared the decisions made by 100 respondents regarding HDFC Bank and AU Small Finance Bank when the stocks' financial returns and hazards were the same. 83 respondents (83%) selected HDFC Bank in the first scenario, while 17 respondents (17%) selected AU Small Finance Bank, indicating a preference for well-known equities when the return and risk are equal.

34 respondents (34%) changed their investment selections in the second scenario, where the unselected stock's financial returns exceeded those of the chosen one. This shows that after this situation, 39% of investors selected AU Small Finance Bank and 61% of investors selected HDFC Bank.

Among the 66 respondents who did not alter their decisions, the reasons varied (Figure 2). Five respondents based their decisions on past returns, 11 invested due to a lack of awareness about the other stock, 33 perceived their chosen stock as fundamentally strong, 11 exhibited brand loyalty (a manifestation of familiarity bias), and the remaining 6 respondents cited other reasons. This nuanced analysis reveals that factors beyond mere returns, such as behavioral biases, knowledge, and perceived company strength, influence investors' choices.

Within the Banking sector, 16.67% of investors maintain their decisions unchanged, influenced by familiarity bias. Despite 34% of respondents basing their investment decisions on stock returns, it is noteworthy that over 60% of all investors still prefer familiar stocks over unfamiliar ones.

B. FMCG

The study focused on HUL (Hindustan Unilever) and AVT Natural Product Ltd., analyzing investor choices under different scenarios. When financial return and risk were identical for both stocks, 95 out of 100 respondents opted for HUL, while the remaining 5 chose AVT Natural Ltd. This highlights a clear inclination towards familiar stocks in situations of equivalent financial return and risk. In instances where the financial returns of the unselected stock exceeded the selected one, 30 out of 100 investors altered their decisions. This underscores that 67% of investors chose HUL, while 33% opted for AVT Natural Ltd. in scenario 2.

Among the 70 individuals who retained their initial investment decision, their choices were driven by factors beyond mere returns (Figure 3). Seven respondents remained with the same company based on past returns, 17 invested due to a lack of awareness about the other stock, and the majority (26 individuals) perceived their chosen stock as fundamentally strong. Seventeen individuals displayed brand loyalty, a manifestation of familiarity bias, while the remaining 3 respondents cited other reasons. This nuanced analysis underscores that investor decisions are influenced by a range of factors beyond financial returns, including behavioral biases, knowledge, and perceived company strength.

In the FMCG sector, 24.29% of unchanged investor decisions are attributed to familiarity bias. Although stock returns play a role in 30% of total respondents' investment choices, it's noteworthy that over 70% of all investors still favor familiar stocks over unfamiliar ones.

C. Automobile

Tata Motors and Olectra Greentec were the chosen companies for this study. In situations where financial return and risk were identical for both stocks, 95 out of 100 respondents opted for Tata Motors, with the remaining 5 selecting Olectra Greentec. This underscores a clear preference for familiar stocks when financial return and risk are equal. When the financial returns of the unselected stock exceeded the selected one, 23 out of 100 investors altered their decisions. This indicates that 23% of investors change or modify their choices based on the returns generated by the other stock for every 100 people, resulting in 74% of investors choosing Tata Motors and 26% opting for Olectra Greentec in scenario 2.

The 77 individuals who maintained their initial investment decision had reasons beyond mere returns (Figure 4). Fifteen respondents chose to stick with the same company based on past returns, 10 invested in the other stock due to a lack of awareness, and the majority (26 individuals) perceived their chosen stock as fundamentally strong. Twenty-two individuals displayed brand loyalty, indicative of familiarity bias, while the remaining 4 respondents cited other reasons. This nuanced analysis emphasizes that investor decisions are shaped by factors beyond financial returns, including behavioral biases, knowledge, and perceived company strength.

In the Auto-Mobile sector, 28.58% of unchanged investor decisions are guided by familiarity bias. Although stock returns impact 23% of total respondents' investment choices, it's notable that over 70% of all investors still lean towards familiar stocks over unfamiliar ones.

D. Returns V/S Familiarity Bias

Investors' decisions are predominantly influenced by familiarity bias, leading them to invest in known companies or factors that can outweigh the bias, often associated with returns, enticing them to consider unfamiliar companies. Among 100 investors, 36 individuals indicated that a 5% or higher return on investment in an unknown company could alter their investment decision. Meanwhile, 30% of investors remained unwavering in their preference for known companies, regardless of the returns generated. Additionally, there is a segment of investors who might reconsider their decision, even if the returns of the unknown company are slightly higher than those of known companies. Specifically, 6%, 10%, and 18% of investors could change their decision with 1%-2%, 2%-3%, and 3%-5% higher returns, respectively. This nuanced analysis reveals the intricate interplay between familiarity bias, returns, and investors' decision-making processes. In the early stages of investing, a substantial 71% of investors allocate over 60% of their funds to familiar companies. Another 26% of investors maintain holdings of 30%-60% in familiar companies, while a smaller 3% invest more heavily in unknown companies (Figure 5). The finding suggests that, initially, individual investors entering the market are significantly influenced by friends, family, and other investors in their investment decisions. There's a prevalent trust in the rationale of these external sources, leading investors to favor known companies, particularly those whose products or services they use—a manifestation of robust brand loyalty. This underscores the impact of social networks and brand affinity in shaping the investment choices of new entrants to the market.

Despite gaining experience in investing, a significant 75% of investors continue to allocate more than 60% of their funds to well-known companies. A mere 5% of investors direct more investment toward unknown companies, while 20% maintain holdings of 30%-60% in familiar or known companies (Figure 6). The finding underscores that even with investment experience, individuals often succumb to biases, notably familiarity bias. The influence of brand names and brand loyalty remains a major factor, suggesting that investors may still make irrational market decisions when swayed by brand associations. This insight highlights the enduring impact of psychological factors on investment choices, even among experienced investors.

E. Hypothesis Testing

The Chi-square test of independence is used in hypothesis testing to evaluate the connection between two variables. To ascertain whether categorical variables show a statistically significant association, this statistical test is essential. The Chi-square test of independence, also known as the Chi-square test of association, basically looks at whether the values of one category variable are influenced by the values of another categorical variable. When doing this test, there is enough evidence to conclude that the observed distribution deviates from the expected distribution if the p-value is less than or equal to the selected significance level. It is implied that the category variables under investigation are connected in these situations. This highlights how useful the Chi-square test is when looking for meaningful relationships between categorical variables in hypothesis testing.

1. Hypothesis I

Consider, H0 = there is no association between types of investors and familiarity bias among individual investors. H1 = there is association between types of investors and familiarity bias among individual investors.

Table 1: Hypothesis-I testing for association between type of investors and familiarity bias among individual investor

The Chi-square test of independence was applied to examine the relationship between gender and the level of familiarity bias. The calculated Chi value is 6.33, surpassing the tabulated value of 5.99 at a 5% significance level with 2 degrees of freedom. Consequently, the null hypothesis is rejected at a 95% confidence level, leading to the conclusion that a significant association exists between gender and the level of familiarity bias. Regarding types of investors, categorized based on risk and holding period, long-term investors exhibit the highest familiarity bias. With a holding period of more than 5 years, these investors tend to prefer stocks that are fundamentally strong and have growth potential. Their familiarity bias stems from a desire to secure investments against long-term volatility. Medium-term investors, aiming for returns in a shorter timeframe than long-term investors, rank second in familiarity bias. Their objective emphasizes returns over risk, leading them to invest in a mix of growth stocks with varying degrees of familiarity. On the other hand, short-term investors, with a timeframe ranging from 1 day to a few months, display the least biases. Their investment decisions are driven by volatility, and they engage in trades based on technical analysis, prioritizing intraday profits over familiarity with a specific company.

2. Hypothesis II

Consider, H0 = There is no association between monthly income level and familiarity bias among individual investors. H1 = there is association between monthly income level and familiarity bias among individual investors.

Table 2: Hypothesis-II testing for association between type of investors and familiarity bias among individual investor

The Chi-square test of independence is employed to scrutinize the relationship between monthly income and the level of familiarity bias. With a calculated Chi value of 1.90, falling below the tabulated value of 7.81 at a 5% significance level and 2 degrees of freedom, the null hypothesis is accepted at a 95% confidence level. This leads to the conclusion that there is no significant association between monthly income and the level of familiarity bias. Similar to numerous other research papers, this study identifies the prevalence of familiarity biases among investors, surpassing their expectations for financial returns. Psychological factors significantly influence investor decisions. Interestingly, investors appear more willing to embrace the risk associated with unknown stocks when financial returns are notably high. However, they exhibit a lower propensity to divest from unfamiliar stocks during market downturns compared to their inclination to sell shares of familiar companies.

In general, individual investors enter the stock market with the primary objective of earning desirable returns by allocating excess funds. Due to time constraints and a lack of capacity to delve into market intricacies, they often gravitate towards well-known companies. This trend is driven by the influence of prevailing market trends and the opinions of those in their immediate surroundings. Notably, this behavior is observed not only among novice investors but also among those with over 5 years of experience in the market.

Conclusion

In conclusion, this research delves into the intricate realm of behavioral finance, challenging the traditional assumptions of rational decision-making in financial markets. The efficient market hypothesis, once considered the bedrock of conventional finance, is scrutinized in light of behavioral biases that drive investors to make decisions influenced by psychological factors rather than rational economic models. Behavioral finance, pioneered by figures like Daniel Kahneman, Richard Thaler, and Amos Tversky, acknowledges the impact of human psychology on financial markets. Behavioral biases such as overconfidence, disposition effect, anchoring, and herding behavior play significant roles in shaping investor decisions. This study focuses on the familiarity bias, exploring how investors\' inclination towards known companies affects their investment choices. The methodology involves a systematic analysis of investor decisions in the banking, FMCG, and automobile sectors. The study finds a consistent preference for familiar companies, even when financial returns and risks are comparable. Investors tend to stick with familiar stocks, demonstrating brand loyalty and exhibiting behavioral biases that influence their decisions. The results highlight the enduring impact of familiarity bias, with a substantial percentage of investors allocating a significant portion of their funds to well-known companies. Even experienced investors, with more than five years of market exposure, exhibit a pronounced preference for familiar stocks, indicating the robustness of behavioral biases over time. Hypothesis testing reveals significant associations between familiarity bias and both gender and investor types. Long-term investors show the highest familiarity bias, driven by a desire to secure investments against long-term volatility. Meanwhile, monthly income does not demonstrate a significant association with familiarity bias, indicating that this bias is prevalent across various income levels. The study concludes by emphasizing the need for a comprehensive understanding of behavioral biases in investor decision-making. It sheds light on the complex interplay between familiarity bias, returns, and various demographic and behavioral factors. The insights gained contribute to the broader field of behavioral finance, offering valuable perspectives for both academics and practitioners in the financial markets.

References

[1] Kipngetich, T. J. (2011). Determinants of Initial Public Offer Pricing in Kenya, London:. Retrieved July 23, 2014, from Annual Conference on Innovations in Business & Management [2] Kumar, R. (2005). Research Methodology: a step-by-step guide for beginners. London: Sage Publications. [3] Li, X. (2004). Behavioural Explanation for Mispricing of IPOs\' Discretionary Current Accruals and Impact of Firm\'s Information Environment on Information Assymetry. Boston: Boston College: Unpublished Thesis. [4] Loomes, G. & Sugden. (1982). Regret Theory: An Alternative Theory of rational choice under uncertainty. Economic Journal , 92(4), 805-24. [5] March, J. S. (1987). Managerial Perspective on Risk Taking. Management Science , No. 33. Mellenbergh. (2008). [6] Mittal, M. & Vyas. (2010). Study of Differences in Behavioural Biases in Investment Decision- Making between the Salaries and Business Class Investors. Indore: IUP. [7] Mugenda, O. & Mugenda. (2003). Research Methods: Quantitative and Qualitative Approaches. Nairobi: Act Press. [8] Nyamute, W. & Maina. (2010). Effect of Financial Literacy on Personal Financial Management Practices: A case Study of Employees of Finance and Banking Institutions. [9] Pompian, M. (2012). Behavioural Finance and Investor Types: Managing Behaviour to Make Better Investment Decisions. New York: John Wiley & Sons. [10] Raines, J. (2011). Behavioural Finance and Post Keynesian-Institutional Theories of Financial Markets. Journal of Post Keynesian Economics . [11] Ramnath, S. R. (2008). The Financial Analyst Forecasting Literature: A Taxonomy with suggestions for further Research. International Journal of Forecasting , (24)1, 34-75. [12] Darrat, A. F., Zhong, M., & Cheng, L. T. W. (2007). Intraday volume and volatility relations with and without public news. Journal of Banking and Finance, 31(9), 2711-2729. https://doi.org/10.1016/j.jbankfin.2006.11.019 [13] de Avila, C., de Oliveira, L. A., de Melo Silva Avila, A. S., & Jessica Rayse Malaquias, R. F. (2016). Behavioral Biases in Investors’ Decision: Studies Review From 2006-2015. Revista De Gestao Financas E Contabilidade. [14] Dhar, R., & Zhu, N. (2006). Up Close and Personal: Investor Sophistication and the Disposition Effect. Management Science, 52(5), 726-740. https://doi.org/10.1287/mnsc.1040.0473 [15] Dowie, G., & Willows, G. (2016). An investigation of investors’ estimates of returns earned and the effect of anchoring on these estimations. South African Journal of Accounting Research, 30(1), 29-40. https://doi.org/10.1080/10291954.2015.1021559 [16] Dubra, J. (2004). Optimism and overconfidence in search. Review of Economic Dynamics, 7(1), 198-218. https://doi.org/10.1016/S1094-2025(03)00036-X [17] Fama, E. F. (1970). American Finance Association Efficient Capital Markets : A Review of Theory and Empirical Work. Journal of Finance, 25(2), 28-30. https://doi.org/10.2307/2325486 [18] Filiz, I., Nahmer, T., Spiwoks, M., & Bizer, K. (2018). Portfolio diversification: the influence of herding, status-quo bias, and the gambler’s fallacy. Financial Markets and Portfolio Management, 32(2), 167-205. https://doi.org/10.1007/s11408-018-0311-x [19] Glaser, M., & Weber, M. (2010). Overconfidence. Behavioral Finance: Investors, Corporations, and Markets, 241-258. [20] Gruber, M. J. (1996). Another Puzzle: The Growth in Actively Managed Mutual Funds. The Journal of Finance, 51(3), 783-810. https://doi.org/10.2307/2329222 [21] Huang, J. B., Tan, N., & Zhong, M. R. (2014). Incorporating overconfidence into real option decision-making model of metal mineral resources mining project. Discrete Dynamics in Nature and Society. https://doi.org/10.1155/2014/232516 [22] Jahanmiri, M. (2018). Anchoring Bias a Criterion for Explain Profitability of 52-Weeks High and Momentum Strategies. Pacific Business Review International, 10(7), 115-124. [23] Jaimovich, N., & Rebelo, S. (2007, May). Behavioral Theories of the Business Cycle. Journal Ofthe European Economic Association, 5, 361-368. [24] Joo, B. A. K. (2017). Influence of Overconfidence, Optimism and Pessimism on the Rationality of the Individual Investors: An Empirical Analysis. Pacific Business Review International. [25] Kabasinskas, A., & Macys, U. (2010). Calibration of bollinger bands parameters for trading strategy development in the Baltic stock Market. Engineering Economics, 21(3), 244-254. [26] Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291. https://doi.org/10.2174/138920312803582960 [27] Kaustia, M. (2004). Market-wide impact of the disposition effect: Evidence from IPO trading volume. Journal of Financial Markets, 7(2), 207-235. https://doi.org/10.1016/j.finmar.2003.11.002 [28] Kaustia, M., Alho, E., & Puttonen, V. (2008). How Much Does Expertise Reduce Behavioral Biases ? The Case of Anchoring Return Effects Estimates in Stock. Financial Management, 37(3), 391-411. [29] Khan, M. T. I., Tan, S. H., Chong, L. L., & Ong, H. B. (2017). Investment characteristics, stock characteristics and portfolio diversification of finance professionals. Borsa Istanbul Review, 17(3), 164-177.

Copyright

Copyright © 2024 Jahanvi Patel. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.