Authors: Daria Azhyshcheva, Vi Dinh, Aanya Gothal, and Abhinav Sisodiya
Mentor: Dr. Gerard Dericks (PhD, London School of Economics). Dr. Dericks is currently the director of the Center for Entrepreneurship and Economic Education at Hawaii Pacific University.
Abstract
This review identifies common behavioral factors that have been empirically demonstrated to lead to suboptimal investment decisions. The breadth of the biases observed in finance is large and continues to grow. In addition, the susceptibility of investors to these biases also appears to vary by contextual factors such as expertise, skill, knowledge, and the experience of the investor. Better understanding the behavior of investors could help explain stock market anomalies, prevent investors from making costly mistakes, and thereby dampen the business cycle and lessen the extent and impact of destructive asset bubbles.
Introduction
Academic finance has established a variety of theories on the basis of human rationality. These analytical frameworks include Modern Portfolio Theory (MPT), the Efficient Market Hypothesis (EMH), and Expected Utility Theory (EUT). Modern Portfolio Theory (MPT) instructs investors to maximize expected returns within a desired level of risk - as measured by volatility, through forecasting the future based on past data and adopting complete asset diversification. The Efficient Market Hypothesis (EMH) posits that stock prices reflect all relevant information, which means that stocks will always trade at their fair values. While EUT likewise assumes that investors treat market losses and gains symmetrically.
Nonetheless, the academic literature is replete with examples of apparent puzzles and anomalies that cannot be explained by traditional approaches to finance, thus giving rise to ‘so-called’ behavioral finance theories. Behavioral finance makes use of psychological and behavioral aspects of human decision-making to examine financial markets. In addition, with over 100 cognitive biases listed on Wikipedia, it is vital to include those elements to how investors view finance market since everyone has biases. Furthermore, in a real-world context, investors seem to behave very differently than what was reported and predicted by financial models. For instance, such observations can be seen in the Asset Price Bubbles or crashes in the market. Also, stock markets move irrationally because individual investors are irrational.
This is a review of the most prevalent biases in behavioral finance shown to affect the investment decision-making process. The remainder of this paper presents an outline of the findings related to biases, the contexts that it will have effect on, and the conclusion of the overall findings.
Literature Review
Anchoring
DEFINITION: The cognitive tendency of investors to rely too heavily on the first piece of information (the “anchor”) they encounter when making decisions.
SYNTHESIS: Research consistently demonstrates the prevalence of anchoring bias in financial decision making across a variety of contexts. Such studies have shown that individuals rely excessively on initial figures when making subsequent judgments, whether it’s overvaluing real estate (Matsumoto et al., 2013), relying too heavily on past earnings when forecasting future performance (Bouteska and Regaeg, 2020), or making impulsive wagers under pressure (Jetter and Walker, 2017). This bias is also present in financial professionals: Campbell and Sharpe (2009) show that expert consensus forecasts are anchored to initial estimates, and Cen and Hilary (2013) highlight how analysts’ overconfidence is shaped by past performance. Additionally, factors such as gender and financial knowledge seem to influence susceptibility to anchoring, with women being at a greater risk of being affected despite greater financial knowledge (Owusu and Laryea, 2023).
Availability Bias
DEFINITION: The tendency to place too much emphasis on information that is easily available.
SYNTHESIS: The current research on availability bias consistently demonstrates its influence on investment decisions (Sadeeq and Butt, 2024; Salman et al., 2020; Khan, 2017). However, the extent of the bias varies across studies. While some research indicates a strong correlation between availability bias and investment decisions (Sadeeq & Butt, 2024), other studies suggest a less noticeable effect (Khan, 2017). The difference in findings can be attributed to the varying sample sizes and characteristics across the studies. Sadeeq and Butt (2024) used retail investors in the Delhi-NCR area, while Khan (2017) included a mix of Islamabad stock exchange and business students. Additionally, circumstantial factors such as market conditions (Rahim et al., 2022) and investor characteristics (Sachan and Chugan, 2020; Moradi et al. 2013) can influence the extent of availability bias.
Confirmation Bias
DEFINITION: The tendency to interpret new evidence as confirmation of one's existing beliefs or theories.
SYNTHESIS: Confirmation bias is proven to have a significant effect on investor behavior (JaeHong et al., 2010; Cipriano & Gruca, 2015; Legoux et al., 2014; Khachikian, 2021; Zaleśkiewicz et al., 2016). Studies have shown that investors are more likely to overstate their investment judgment and engage in excessive trading (Jaehong et al., 2010). While the impact on individual investors is clear, the broader market implications are more nuanced. (Cipriano and Gruca, 2015) suggests that widespread confirmation bias can affect market prices, but a diverse investor base can mitigate these effects. Additionally, Legoux et al., (2014) show that even financial experts are susceptible to confirmation bias.
Disposition Effect
DEFINITION: the tendency of investors to ride losses and realize gains
SYNTHESIS: The current research on the disposition effect, which is the tendency of investors to rise losses and realize gains, is prevalent across several markets and investor types (Frazzini, 2006; Lepone, and Wright, 2014). However, its severity varies based on market conditions and investor characteristics. Choe & Eom (2009) and Orange, Yoshinaga, & Eid (2021) found that more experienced and sophisticated investors are less prone to the disposition effect, although they are not entirely immune. Additionally, Dierck et al. (2019) highlighted the role of investor attention, finding that those who frequently monitor their portfolios are less likely to exhibit the disposition effect. The differences in findings can be attributed to the variations in sample types and sizes, ranging from individual retail investors to professional traders, and from small datasets to large-scale studies.
Endowment Effect
DEFINITION: A cognitive bias where people tend to value objects they own more than objects they don’t (how emotional attachment can change an object’s worth)
SYNTHESIS: Recent studies show the appearance of endowment effect in diverse settings (Carkey et al., 2022; Gine & Goldberg, 2018; McGranaghan & Otto, 2022; Pan, 2023). Carkey et al. 2022) investigated the endowment effect on the demand for collateralized loans of the members of SACCO. They found that the effect inhibits demand for loans collateralized using a borrower’s existing assets versus using the asset to be acquired as collateral. Gine and Goldberg (2018) found that forty-two percent of subjects who already owned a more expensive savings account with a local bank in Malawi chose to retain it instead of moving to a cheaper account, whereas, none of the first-time customers offered the same two options chose the more expensive account. Pan (2023) analyzes cases that reveal the negative consequence of endowment effect in the investment field in Nagpur city, and the great influence that endowment effect have on Indian investors’ behavior.
However, not all research has been able to identify an endowment effect. For instance, Armansyah (2022) failed to find an influence of the endowment effect on investment decisions. In particular, Armansyah in an analysis of the data from 205 individual Indonesian capital market investors that was obtained through e-questionnaires found no result. The result is attributed to the nature of capital market investors, in which they tend to put heavy emphasis on substantial economics. It is also speculated that when investors have many options in the capital market, the decision of keep assets will drop. McGranaghan and Otto (2022) examined the presence of the endowment effect through comparing acceptable sale (Willingness to Accept) and purchase (Willingness to Pay) prices for chocolate that had been tasted versus chocolate that had not. However, when an attempt to replicate the study was made by the authors, they failed to find this result again possibly due to COVID-19 precautions.
Framing Effect
DEFINITION: a cognitive bias that describes how people's decisions and choices are influenced by the way information is presented
SYNTHESIS: The framing effect in behavioral finance has been studied over a long period of time (Diacon & Hasseldine, 2005; Seo et al, 2010; Lucarelli and Mazzocchini, 2019; Ventre et al, 2023). Likewise the contexts in which it has been investigated have been varied, including investment funds (Diacon and Hasseldine, 2005) and mortgage banking litigations (Lucarelli and Mazzocchini, 2019). Framing effects also appear to impact risk assessment and financial decisions. Ventre et al (2023) researched the correlation between the framing effect and the choice of financial products, and they recognized that frames have a huge influence on risk assessment and financial decisions. Seo et al (2010) examined the role of affect (pleasant or unpleasant feelings) and decision frames (gains or losses) in risk taking in stock investment simulation in the New England area. The findings presented that feelings may intensify the framing effect of loss toward a greater degree of risk taking. However, the framing effect only partially explained risk-taking behavior. Furthermore, the level of expertise appears to partially determine how susceptible people might be to the framing effect. Diacon and Hasseldine (2005) found that people with a low level of expertise are more prone to the framing effect.
Herding Bias
DEFINITION: Investors' tendency to follow and copy what other investors are doing.
SYNTHESIS: The occurrence of herding bias in financial-decision making has been researched and studied widely across a variety of contexts, such as capital markets (Armansyah, 2022) and stock markets (Bogdan et al, 2022). Furthermore, herding bias tends to manifest most strongly during bear markets, as demonstrated by Shah et al (2019), in which they examined the relationship between investors and managers’ herding bias and the firm value from 2008 to 2017–the period of the Great Recession. Specifically, investors’ herding bias is measured through the degree of individual movements that are affected by the overall price movements and whether buy orders exceed sell orders (and vice versa), while managers’ herding bias is determined by the probability of firm managers to invest similar proportion of their overall capital subsequently through the years as other companies. In addition, they stated that findings confirmed the influence of herding bias on firm value, and the findings were robust under different time intervals. Armansyah (2022) found a positive correlation between herding bias and investment decisions, specifically on the capital markets in Indonesia. The paper found that, in response to the COVID-19 Pandemic, investors exhibited more hedging behavior, suggesting that herding behavior may be more prevalent in bear markets. Bogdan et al (2022) found that herding behavior is most notable in emerging markets, followed by frontier markets and developed markets in Europe Stock Markets during the time of the COVID-19 Pandemic.
In addition, elements such as financial knowledge and experience does not affect the rate of herding bias. Pons-Novell (2003) investigated whether economic forecasters behave strategically or match the consensus forecasts (herding).They found that although the participants belonging to non-financial business categories exhibited behavior based on herding, the evidence from other categories is not conclusive to indicate a relationship of reputation and experience with the reason why forecasters either make radical forecasts or forecasts that are similar to the consensus. Fernández et al (2011) showed that herding behavior occurs more frequently when the level of information uncertainty decreased.
Hindsight Bias
DEFINITION: the tendency to claim current events were to happen even though it was completely unpredictable in the past.
SYNTHESIS: Hindsight bias in financial markets has now been researched for some time (Biais and Weber, 2009; Kudryavtsev and Cohen, 2011; Hussain et al, 2013). Hussain et al (2013) studied hindsight bias and investor decision making in asset selection effect and sign of return effect. Although all three groups–including banks financial managers, stock market investors, and students who major in finance–are hindsight biased, in asset selection effect, the stock market investors were found to be highly exposed to hindsight bias, while the exposure was believed to be significant to the bank financial managers in sign of return effect. Metilda, J. (2013) summarized previous works and concluded that hindsight bias prevents investors to think rationally and leads to taking risk excessively, which puts customers’ portfolio in a dangerous circumstance. Biais and Weber (2009) found that hindsight bias was proved to be positively correlated with risk perception and investment performance.
In addition, the degree of impact by hindsight bias can be different among the opposite sex. Kudryavtsev and Cohen (2011) conducted an investigation about the difference of the degree of being influenced by hindsight bias between women and men. The results showed that all attendees were affected by hindsight bias, specifically women experienced a heavier influence of this.
Home / Familiarity Bias
DEFINITION
Home or familiarity bias is largely an emotional bias that can lead us to favor investment strategies that are potentially too similar or heavily concentrated.
SYNTHESIS: The phenomenon of home bias, driven by emotional inclinations, can lead to overconcentration in local investment strategies. Silva et al. (2020) found that home bias is costly and may stem from behavioral biases. They also discovered lower home bias in mutual funds for qualified investors and those with higher minimum investment requirements. Andrikogiannopoulou and Papakonstantinou's (n.d.) study indicates that psychological motives sustaining home bias endure in market settings, reinforcing its behavioral explanation in financial markets. However, a criticism of the existence of home-bias is that investing locally allows investors to better understand the dynamics of the market and riskiness of investments.
House Money Effect
DEFINITION: The behavioral finance concept where people risk more with winnings than they would otherwise, due to believing it is less valuable or they are playing with money they didn't have before.
SYNTHESIS: There are many different ways in which the House Money Effect seems to show up in financial markets. Duxbury et al. (2015) claimed the house money effect coexists with other biases such as the disposition effect - which is a variant of loss aversion, causing investors to hold onto losing stocks and sell winning ones.
Massa and Simonov (2002) discussed how investors react to prior gains by increasing risk taking which was measured by trading actions and market trends that the individuals used or earned. Ackert et al.(2003), stated wealth affects market and bidding prices because traders are more interested in gambling money that was seen as shared money. Their study showed that larger bids because of higher endowments were important in seeing the changing market prices.
This bias appears to also influence behaviors around money outside of financial markets. For instance, Reinstein and Riener (2009) find that money received through working was less generously donated to charity than money that was received as a gift. They show how the psychological and physical factors of money influence donation behavior.
Loss Aversion / Disposition Effect
DEFINITION: the tendency to prefer avoiding losses over acquiring equivalent gains.
SYNTHESIS: Loss Aversion was found in various studies which showcased the understanding of loss aversion in various contexts. Novemsky and Kahneman (2005) discussed loss aversion is consistent regardless of whether there is an extra money in stake. Gal and Rucker (2018) claimed loss aversion as a universal principle to seeing its limits and conditions. The experiments showed the impact of losses and gains is situation dependent. Schmidt and Traub (2001) add onto previous findings by suggesting the significant psychological effect of losing versus gaining the identical quantity. They show the concept that loss aversion impacts decisions which ultimately impacts consumer and economics activities.Adding onto the previous idea, Iqbal (2021) found that investors tend to not make poor decisions on short-terms losses when they get less frequent feedback which reduces myopic loss aversion(MLA). This furthermore shows how loss aversion significantly affects trading behavior. Christoph (2020), suggested investors tend to expect significantly unfavorable responses to losses however their real experience is less impactful which connects to the idea of overestimation of loss aversion.
Mental Accounting Bias
DEFINITION: How people categorize, and keep track of financial activities. Dividing money for a specific purpose. In effect, this is a lack of adequate diversification bias. For example, only investing in several retail REITs and considering your portfolio diversified.
SYNTHESIS: In several studies, mental accounting has played a role in effecting investor decisions.These show how this idea influences how individuals categorize and keep track of financial activities.
Some studies found that individual differences in mental accounting play a significant role in how people handle financial losses and gains (Muehlbacher and Kirchler, 2019). This bias influences decision making because mental accounting can influence decisions regarding redistributive actions (Becker et. al (2020). Cheema and Soman (2006) discussed that individuals tend to justify their overspending because of the flexibility that expenses are in different mental accounts.
Throughout their experiments, testing Parsaei et.al (2024) revealed that managers prone to mental accounting tended to retain debt-financed assets over equity-financed assets. Furthermore, different results were found which showed that only overconfidence and risk perception significantly influence investment decision-making among Generation Y workers in Yogyakarta, not mental accounting(Sukamulja and Senoputri (2017).
Optimism Bias
DEFINITION: The tendency to overestimate the likelihood of positive events and underestimate the likelihood of negative events.
SYNTHESIS: Optimism bias is shown to have a significant influence on investment decisions. Research proves that this bias causes investors to overestimate potential gains and underestimate risks (Iqbad, 2015; Wang, Sheng, & Yang, 2013). Wang, Sheng, and Yang (2013) demonstrated that optimism bias, when combined with incentive contracts, results in increased risk-taking and poorer portfolio performance. Studies have also shown that financial literacy is a crucial factor in mitigating the negative effects of optimism bias (Sri & Arik, 2021).
Overconfidence Bias
DEFINITION: when individuals show more confidence in their capabilities than what they truly are.
SYNTHESIS: Overconfidence has effects on influencing various market situations, as well as investor choices.Pikulina, et al. (2017) discussed investors who were overconfident in their abilities leaned toward overinvest, specifically they chose higher investment levels. Similarly, Metwally and Darwish (2015), found that investors tended to succeed when the stock market does well due to their recent success. They claim that overconfidence influences traders to more actively trade due to the belief they will continuously succeed in the market.
However, Armansyah (2022) found support that overconfidence negatively influenced investors behavior,and how it endangers the investments made if they do not pay attention to the fundamentals of the stock. Benoit et al. (2013) supported how overconfidence influences decision-making in scenarios, where individuals access their abilities.Specifically the experiment showed how a large number of the participants chose to bet on their performance because they believed that they had above-average capabilities.
Present / Temporal Bias
DEFINITION: Present bias, the tendency of people to discount their future preferences in favor of more immediate gratification, is an important concept derived from the theory of self-control in behavioral finance
SYNTHESIS: The concept of present bias, which involves favoring immediate rewards over long-term goals, is a crucial aspect of behavioral finance. Meier and Sprenger (2010) discovered that individuals exhibiting present bias tend to rely on credit card borrowing. In a related vein, Goda et al. (2019) have suggested that mitigating present bias could potentially boost retirement savings by around 12 percent.
Ben-Zion et al. (2012) demonstrate that when short-term information is introduced, investors reduce allocations to risky assets, even though this negatively affects long-term outcomes, while Aïd et al. (2014) explore the impact of temporal discounting and precautionary biases on irreversible investment decisions. These findings indicate that the relative volatility of markets can influence optimal investment strategies over time, suggesting that local volatility can shape the timing and magnitude of investments. This shows how the availability of immediate data fosters temporal bias in decision-making. Muralidhar (2015) further explores the "time contradiction" in asset management, highlighting how the time horizon required to establish high confidence in investment effectiveness often surpasses the period within which investors anticipate returns. This temporal bias often leads to suboptimal decision-making, as investors tend to lean on single-period models in a multi-period volatile environment, resulting in less effective comparisons and compensation mechanisms.
Recency Bias
DEFINITION: Recency bias is the tendency to overemphasize the importance of recent experience or latest information in estimating future events.
SYNTHESIS: Recency bias can affect asset pricing and financial risk tolerance. Sulistiawan and Wijaya (2019) aimed to mitigate this bias by using expert recommendations in group discussions. The results showed that expert recommendations helped reduce overvaluation when participants received negative news followed by positive news. Additionally, Sulistiawan and Rudiawarni found that individual decision-making after group discussions can lead to increased bias. Their experiment showed that participants' judgments were influenced by the group discussion, even when they already had the information beforehand.
Regret Aversion Bias
DEFINITION: Regret Aversion is cognitive bias where a decision maker often chooses the option that would carry the least regret even if it's not the most optimal.
SYNTHESIS: Farhana and Jannatul (2023) conducted a pattern analysis to identify the most influential psychological biases affecting investors' decisions. They found that regret aversion bias is a significant behavioral factor impacting investment decisions. This finding is consistent with studies conducted in Indian (Mukherjee et al., 2019), Malaysian (Lim, 2012), and other global stock markets (Kengatharan, 2014). Rehan and Umer (2017) gathered data through a questionnaire distributed among investors at the Pakistan Stock Exchange, revealing that regret aversion has a positive impact on investor decisions. However, Shah and Malik (2021) discovered that regret aversion and loss aversion have statistically significant negative impacts on individual investors' trading frequency, while risk perception has an insignificant but positive impact. Gazel (2015) agreed with this and said that according to expectancy theory the pain of losses are higher than the joy of gain. Likewise, Awais and Estes (2019) found that the strongest variable in the generation of regret-averse bias of investor is Errors of Commission, in which, investors usually feel fear at the time of taking decision in the market. Regret aversion therefore appears to lead to many investment mistakes such as excessive conservatism about investment options and herd behavior.
Representativeness Bias
DEFINITION: Representative bias often occurs based on preconceived notions of personal characteristics and attributes related to peoples' appearances, attire, or personal habits.
SYNTHESIS: Representative bias stems from preconceived notions based on individuals' appearances, attire, or behaviors. In their study of individual investors in the Tehran Stock Exchange, Jamshidi et al. (2019) found a prevalent representativeness effect. Khan (2020) investigated the factors contributing to representativeness bias in the Stock Market of Pakistan, suggesting that experts and investors may succumb to this bias due to the overwhelming nature of recent information and misinterpretation of chance. Soraya et al. (2023) conducted an analysis of the influence of representativeness bias and herding on investment decision-making through risk tolerance, revealing a significant impact on investors' decision-making processes.
Conclusion - Biases in Behavioral Finance
Behavioral finance asserts that psychological factors such as cognitive and emotional biases significantly impact investment decisions, often leading to suboptimal choices. In this review we have made efforts to include all the relevant studies and have highlighted the main biases affecting investor behavior.
Cognitive biases such as optimism, anchoring, availability bias, confirmation bias, and overconfidence bias stem from errors in logical thinking. These biases lead investors to rely too heavily on initial information (Khan et al., 2022), overestimate positive outcomes while downplaying risks (Petersen, 2023), and favor information that is most easily accessible over a broader range of data (Khan et al., 2022). In contrast, emotional biases such as herding bias, loss aversion, house money effect, mental accounting, recency bias, regret aversion bias, framing effect, hindsight bias, representative bias, and the endowment effect are driven by feelings and emotions. These biases cause investors to mimic market trends (Kim & Nam, 2021), overvalue their owned assets (Patel & Chen, 2023), and cling onto losing investments rather than cutting their losses. These biases are proven to result in suboptimal decisions (Smith et al., 2022; Jones & Evans, 2023). The findings also show that both professional and casual investors are susceptible to these biases, though the extent of the impact varies based on financial knowledge and experience (Ackert, Church, & Jayaraman, 2003; Armansyah, 2022).
Behavioral biases often lead to decisions that deviate from what would be considered rational, raising questions about whether government intervention is justified to correct or prevent them. Proponents of such governmental ‘paternalism’ argue that because individuals make suboptimal decisions due to biases, it is justifiable for the government to intervene to help individuals make better choices. Current research challenges this notion, with there being little to no substantial evidence proving that government intervention is necessary. For instance, Arkes et al. (2016) searched for such evidence through articles that demonstrated biases. In over 100 studies on violations of transitivity, the search found not a single one demonstrating that a person could become a money pump, that is, be continually exploited due to intransitive choices. Furthermore, in more than 1,000 studies that identified preference reversals, arbitrage or financial feedback made preference reversals and their costs largely disappear. Similarly, in hundreds of studies on the Asian Disease Problem and other framing effects, little evidence was found that “irrational” attention to framing would be costly. All in all, little to no evidence was found that violations of these and other logical rules are either associated or causally connected with less income, poorer health, lower happiness, inaccurate beliefs, shorter lives, or any other measurable costs.
Gigernezer (2018) adds to this perspective, arguing that “bias bias” influences behavioral economics to observe biases even where they are absent. His critique alters the idea of cognitive biases in various ways. To start off, he stated that many biases come from misjudgements in statistical evaluation. In addition, he believed that these should not just be seen as statistical but as intellectual limitations. Moreover, he claimed that errors in judgements are labeled as systematic biases which are just random errors. Furthermore, he suggested that in unknown situations, deviations show the ways individuals make choices and the variation in those decisions.
Also, Gigernerzer’s critique discusses his main point that biases are really good decisions made in unknown situations. An example is that he suggested that the framing effect gives a good amount of recognition of communication and not just a mistake. Through understanding the processes of decision-making during various scenarios, shows how purported cognitive biases can in fact be seen as rational. For example, overconfidence which may be seen as a bias however it correlates with Gigerenzer’s opinion when looking how choices are made it is important to consider the context. Furthermore, it also shows how cognitive biases can be used and interpreted in research. Instead of seeing these biases as human flaws we could see how they are strategies used in unknown or risky scenarios. Herbert Simon’s(1979) idea about certain fields and resources decision making is rational which coincides with previous findings.Therefore, Gigerrenzer’s critique allows for a unique perspective on cognitive biases in contrast to the traditional view seen as flaws. He allows for a new idea of thinking that biases are behaviors which are adaptable and showcases a different side of decision-making and understanding of biases.
The studies presented here have brought forward many issues for future research. First, the research on how these biases affect emerging markets remains relatively unexplored despite their recent growth in the global economy. Second, there is a gap in understanding how biases impact decisions related to non-traditional investments such as cars, cryptocurrency, real estate, etc. with the majority of the sources focus on stocks and bonds. Third, while existing research primarily relies on secondary data, future studies should prioritize primary data collection to gain a more in-depth comprehension of investor behavior across various contexts. Fourth, to better understand the global impact of behavioral biases, research should examine these biases across different cultural and economic environments. Such comparisons could reveal how biases vary internationally and influence investment decisions in diverse settings.
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