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Individual investor’s behavior is extensively influenced by various biases that highlighted in the growing discipline of behavior finance. Therefore, this study is also one of another effort to assess the impact of behavioral biases in investment decision-making in National Stock Exchange. A questionnaire is designed and through survey responses collected from 243 investors. The present research has applied inferential statistics and descriptive statistics. In the existing study, four behavioral biases have been reviewed namely, overconfidence, anchoring, disposition effect and herding behavior. The results show that overconfidence and herding bias have significant positive impact on investment decision. Overall results conclude that individual investors have limited knowledge and more prone towards making psychological errors. The findings of the study also indicate the existence of these four behavioral biases on individual investment decisions. This study will be helpful to financial intermediaries to advice their clients. Further, study can be elaborated to study other behavioral biases on investment decisions.
Content may be subject to copyright. International Journal of Financial Research Vol. 10, No. 4; 2019
Published by Sciedu Press 55 ISSN 1923-4023 E-ISSN 1923-4031
An Analysis of Behavioral Biases in Investment Decision-Making
Geetika Madaan1 & Sanjeet Singh1
1 Chandigarh University, Mohali, Punjab, India
Correspondence: Geetika Madaan, Research Scholar, Chandigarh University, Mohali, Punjab, India.
Received: May 31, 2019 Accepted: June 25, 2019 Online Published: June 26, 2019
doi:10.5430/ijfr.v10n4p55 URL:
Individual investor’s behavior is extensively influenced by various biases that highlighted in the growing discipline of
behavior finance. Therefore, this study is also one of another effort to assess the impact of behavioral biases in
investment decision-making in National Stock Exchange. A questionnaire is designed and through survey responses
collected from 243 investors. The present research has applied inferential statistics and descriptive statistics. In the
existing study, four behavioral biases have been reviewed namely, overconfidence, anchoring, disposition effect and
herding behavior. The results show that overconfidence and herding bias have significant positive impact on
investment decision. Overall results conclude that individual investors have limited knowledge and more prone
towards making psychological errors. The findings of the study also indicate the existence of these four behavioral
biases on individual investment decisions. This study will be helpful to financial intermediaries to advice their clients.
Further, study can be elaborated to study other behavioral biases on investment decisions.
Keywords: behavioral biases, anchoring, disposition effect, overconfidence and individual investors
1. Introduction
The theory of rationality based on two assumptions, which are- "A rule of rationality" and "an act of rationality." In the
situation of a rule of rationality, an individual adopts the mode of behavior that maximizes the expected utility whereas
in case of an act of rationality investor chooses to act in such way that yields maximum utility. Investors make choices
that maximize the benefits and minimize the cost (Ahmad Zamri, Ibrahim, Haslindar, Tuyon, 2017). Evidence and
explanation proposed in the theory of bounded rationality explain that individuals are not always able to obtain all the
relevant information, which is required to make possible decisions (Kinoshita, Suzuki, & Shimokawa, 2013). Bounded
rationality comprehensively concerned with the manner actual decision-making process impact the decisions that
arrived (Kinoshita et al., 2013); (Ahmad Zamri, Ibrahim, Haslindar, Tuyon, 2017).
People are either partly rational or irrational in their decisions. This theory showed that individuals have biases and
cognitive limitations, which forbid them from achieving full rationality at the time of decision-making (Ahmad Zamri,
Ibrahim, Haslindar, Tuyon, 2017). When it comes to individual investment decision making, it is essential to consider
that a certain degree of uncertainty and risk is associated with each investment decision choice (Paul Slovic, 1972).
There is sufficient evidence, point out that due to the occurrence of market anomalies, markets react differently
compared to the behavior of a rational man. Various cognitive biases often prevent individuals from rational thought.
Individuals endowed with rationality that allows them to consider all the available information. From this, they develop
unbiased forecasts about happening of future events, which allow them to make the best financial decisions (Fama,
1970); (Michael C. Jensen, 1978).
The foundation base of traditional finance is efficient market hypothesis. As per this hypothesis, investors have access
to market information and prices of assets and also investors considered to be rational. Even though the discipline of
modern finance has grown progressively, it is still difficult to explain on the scientific grounds that why people behave
non- rationally while dealing in money. While traditional finance assumes people rationalize and enhance their
financial decisions, behavior finance includes the relevance of what investors should do and blend the basics of
traditional finance with what people do in terms of their investment decisions (Mitroi, Adrian Stancu, 2014). The
domains of Psychology and sociology are considered to be imperative accelerators within the field of study of
Behavioral Finance (Robert J. Shiller, 1997). In contradiction to the efficient market hypothesis, many studies have
indicated behavioral biases in investors (Musciotto, Marotta, Piilo, & Mantegna, 2018). The behavioral finance
approach replaces the traditional rationality hypothesis and asserting that behavioral biases influence individuals. International Journal of Financial Research Vol. 10, No. 4; 2019
Published by Sciedu Press 56 ISSN 1923-4023 E-ISSN 1923-4031
Behavioral Finance has seen as a study of how psychology influence financial markets and financial decision making
(H. Shefrin, 2001). Behavioral finance is a new phenomenon to financial markets that have emerged in response to
hurdles faced in traditional finance. It understood as a financial event where agents are not entirely rational (Barberis,
The father of Behavioral finance is ‘Daniel Kahneman' who have received Nobel prize in the field of economics for his
prospect theory. The pioneer researchers in the field of behavioral finance who's contributed tremendously are Daniel
Kahneman, Amos Tversky, and Richard Thaler. They evolved behavioral biases that considered as building blocks of
behavioral finance. Behavioral biases are critical issues for the contradictions between traditional finance and
behavioral finance domains. There have been numerous studies that challenge rationality and thereby evolved
Behavioral Finance (Tversky & Kahneman, 1971). Behavioral biases provide reasons for the asymmetry between the
way humans make decisions involving gains and decisions involving losses (Tversky, A & Kahneman, 1973). The
same individual who is a risk-averse for a decision involving gains becomes a risk seeker for a loss- avoiding decision
(Tversky, A & Kahneman, 1973). Overconfidence, herding, anchoring, cognitive dissonance, availability bias,
self-attribution, mental accounting, framing, representative bias, are few biases that viewed as building blocks of
behavioral finance that significantly influence the decision making of individual investor (H. M. Shefrin & Thaler,
1988); (Singh, 2016).
Behavioral finance proposes that the investment decision -making process is influenced by various behavioral biases
that boost investors to deviate from rationality and take irrational investment decisions (Niehaus & Shrider, 2014). The
present study is an extensive review of behavioral biases in individual investment decision-making (Taffler, Spence, &
Eshraghi, 2017). The relevance to this research field has increased recently, covering both theoretical and empirical
contributions. After studying various literatures, it found that there is a need to conduct one study who carry systematic
review on behavioral biases (Kumar & Goyal, 2015). Here, in the present study, four different behavioral biases have
been taken as a framework to study their impact on individual investment decision-making. This framework seems to
reveal an intention to explore various behavioral biases through the lens of the discipline of behavioral finance.
These behavioral biases are:
Disposition effect
Herding bias
1.1 Overconfidence
In behavioral finance, overconfidence is a psychological trait that has a substantial impact on individual investment
decisions. Those decisions can be stock market investment decisions or other investment decisions (Joo, 2017). In
behavioral finance, overconfidence mirrored as a common psychological bias, which makes financial markets
inefficient by creating mispricing in the form of massive volatility and return variability (Ko & James Huang, 2007).
Many kinds of literature have mentioned the definition of overconfidence, but its definition better understood in
following mentioned manner. Overconfidence is a psychological bias that directs an investor to think a parameter
which is more significant than it is, and it also assumed that an individual‘s judgments are worsened and wrong. With
those judgments, an individual would never learn the correct distribution (Dubra, 2004). During overconfidence, it
observed that average mutual fund manager underperforms the market (Gruber, 1996).
Despite ample evidence that overconfidence is pervasive, it has not gained much attention in the field of economics.
Much attention and broad applicability of overconfidence bias have found in the behavior of rational agents (Dubra,
2004). Researchers argue that overconfidence bias often directed by the neglect of unknowns (Walters, Fernbach, Fox,
& Sloman, 2016). It documented that increased overconfidence leads to excessive trading volume and higher price
volatility in stock markets (Graham, Harvey, & Huang, 2009); (Nir Jaimovich and Sergio Rebelo, 2007).
Overreactions by investors are due to overconfidence about their abilities to grasp or process information (Kent
Daniela, David Hirshleifer, 2002); (Mushinada & Veluri, 2018). In contrary to this research, researchers argued that
overconfidence is also a positive predictor that forecast the investment performance of individual investors that are
followed by market anomalies (Abdin, Farooq, Sultana, & Farooq, 2017). Therefore, Overconfidence is a judgmental
error in which people overestimate their skill, knowledge, perceive information or expand the subjective probability of
particular result happening (Campbell, Goodie, & Foster, 2004); (Glaser, Markus and Weber, 2010).
1.2 Herding Behavior International Journal of Financial Research Vol. 10, No. 4; 2019
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Herding is a commonly observed phenomenon in the financial market. It is a common tendency of human nature to
refer, observe, and imitate other's behavior during the irregular condition in financial markets (Yu, Dan, Ma, & Jin,
2018). At the presence of herding, investors do not act rationally in their investment choices. They prefer to follow
other investor's belief and opinions for taking their investment decision. Hence when investors herd, they tend to
restrain their decisions and follow others. During times of market distress such as market anomalies, price bubbles,
rumors, presence of herding effect is more profound (Mertzanis & Allam, 2018). Herding has seen as collective
imitation leading to a confluence of movements (Philippas, Economou, Babalos, & Kostakis, 2013). It also showed in
many research papers that herding behavior might result in similar patterns of movements across individuals realizing
substantial welfare losses. In one of the research papers, herd behavior has defined in a different way that is if agents
are granted to approach the strategies of their network neighbors, a feedback effect on the network structure and game
outputs observed. Such an effect termed as herding behavior (Mello, Souza, Cajueiro, & Andrade, 2010). When some
agents are prepared to pursue their wealthiest neighbors instinctively, a new type of corporate event, called herding
behavior, may arise (Mello et al., 2010).
1.3 Anchoring
Anchoring is one of the most researched psychological biases (Shin & Park, 2018). Anchoring bias effect investor’s
decision-making processes (Wright & Anderson, 1989). Anchoring defined as a cognitive bias that explains the
ordinary human being's tendency to depend massively on the first piece of information while making decisions (Shin &
Park, 2018); (Maqsood Ahmad, Syed Zulfiqar Ali Shah, 2018); (Singh, 2016). Investors are likely to anchor their
purchases of stocks on the recent highest price of the stock. Such behavioral reactions show that anchoring bias
associated with suboptimal decisions of investors in their decision-making processes (Krause, Shiller, Shleifer, Wilcox,
& Shiller, 1970). Anchoring bias shows as a criterion to analyze stock returns or profitability by a behavioral approach
at uncertain circumstances in 52- week high and momentum strategies (Jahanmiri, 2018). Researchers documents that
anchoring lays a negative impact on investment decisions made by individual investors or traders (Maqsood Ahmad,
Syed Zulfiqar Ali Shah, 2018). Researchers also argue that Anchoring as a judgmental bias, final judgments when to
comprehend toward the starting point of the judge's consideration. The anchoring-and-adjustment heuristic holds that
anchoring bias caused by insufficient adjustment (Qu, Zhou, & Luo, 2008). The anchoring heuristic holds to abundant
moderation that is underreaction. People anchor at some noticeable results, during using this heuristic and conform
based on predictive information (Amir & Ganzach, 1998). Therefore, anchoring is a heuristic bias that causes investors
to rely on recent price changes and price levels (Ormos & Timotity, 2016).
1.4 Disposition Effect
The disposition effect is the propensity of investors to refrain from realized loss in expectations of realized gains
(Pelster & Hofmann, 2018). The disposition effect estimated as the difference between the fraction of realized gains
and the fraction of realized losses (Odean, 1999); (Odean, Strahilevitz, & Barber, 2010). Researchers have recognized
that the tendency of investors to hold losses in investment to prolong and sell winners in investment too early has
marked as disposition effect. The disposition effect has adverse outcomes on an individual's investment because losing
investments commonly carry on to underperform, whereas winning investments usually carry on to outperform
(Aspara & Hoffmann, 2015). Researchers have recognized that the disposition effect is not affected by experimental
manipulations of expected future gains or losses (H.-J. Lee, Park, Lee, & Wyer, 2008).
2. Review of Literature
Even though the discipline of modern finance has proliferated, it is still difficult to explain on scientific grounds why
people behave irrationally while dealing with money (Smit & Moraitis, 2010); (Mitroi, Adrian Stancu, 2014). The
behavioral approach is a new approach to explain the movement in financial markets, which is contrary to the efficient
market hypothesis. Traditional finance believed that security prices always completely mirrored the available
information (Fama, 1970) whereas behavioral finance states that due to non-rational behavior of investors and
inefficiency of financial markets stock or security prices deviate (Cabral de Avila, Lucimar Antonio de Oliveira,
Alanna Santos de Melo Silva Avila, Jessica Rayse Malaquias, 2016); (Kabasinskas & Macys, 2010). With the
increased involvement of investors, behavioral finance focuses on investor behavior and their investment
decision-making process (Liu, Jin, Wang, & Yuan, 2015). Overconfidence is one of the primary building blocks in the
discipline of behavioral finance. Overconfidence is a vigorous outcome of psychology, which studied as one of the
principal reasons for market anomalies (Ko & James Huang, 2007). Overconfidence is a well-established bias that
makes investors too confident about their knowledge and skills and ignores the risk linked to investment decisions
(Prosad, Kapoor, Sengupta, & Roychoudhary, 2018); (Kamoto, 2014). Confidence explained as complete faith in
oneself. While dealing with securities, when an investor is much confident about his strategies and mental tendency, International Journal of Financial Research Vol. 10, No. 4; 2019
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then, in that case, the investor is influenced of overconfidence bias (Huang, Tan, & Zhong, 2014). In the circumstances
of overconfidence, the investor may take the happening of events for granted, which may or may not due to dynamic
economic conditions. Prevalent contributors to the discipline of overconfidence bias are Kahneman, Tversky, Shiller,
Shefrin, Barber, and Odean. The impact of behavioral baizes in investment decision making drawn from trading
volumes of stock markets. Overconfidence behavior can lead to higher trading volumes (Darrat, Zhong, & Cheng,
2007); (Phan, Rieger, & Wang, 2018); (Mushinada & Veluri, 2018); (Khan, Tan, Chong, & Ong, 2017). Advice driven
or gambling oriented study has been undertaken where structured retail products that are faces of financial innovation
have been taken for study. Findings of the study indicated that structured retail products are comprehensively
connected to investor’s behavioural biases particularly overconfidence (Abreu & Mendes, 2018).
The research was investigated to study the influence of past behavior or belief on, and it found that anchoring bias
attitude on belief represents the inconsistent result. It further investigated that individuals resolve conflicts between
their resultant belief and past behavior by adjusting anchoring attitudes with beliefs instead of behavior. It further
supported the interpretation that individuals resolve the conflict between past behavior and subsequent beliefs about it
by aligning attitudes with beliefs instead of behavior. On anchoring bias, a study has been conducted to investigate the
effects of anchoring on stock return estimates by using data from surveys involving university students and financial
market professionals. The background information given to subjects also manipulated. It also observed that the
assessment of university students influenced by initial starting value of the stock and in case of professionals'
assessment was not statistically and economically significant and also professionals are not conscious of the influence
of historical returns based on their expectations (Kaustia, Alho, & Puttonen, 2008). By applying an event-related
potential experiment on young, healthy subjects, the impact of hidden anchoring effects studied that generated by false
information on numerical judgements. The effect of anchoring on investors’ estimates of returns earned in unit trust
has been studied in investing environment in which subjects were invested and assessed that subjects would adjust their
estimates subsequently presented with an anchor. Study resulted presence of an anchor appeared to have no effect on
respondents ‘assessments (Dowie & Willows, 2016). To explore trading strategies that exploit investor’s behavioral
anchoring bias, a study was investigated where anchoring was studied both from technical signal and anomalies point
of view. In study empirical evidence indicated that anchoring bias explains the profitability as a broad set of anomalies
and describes trading strategies that exploit the anchoring bias (Qingzhong Ma, Hui Wang, 2017).
The behavioural results indicated that false information comprehends final assessments having a direct relationship
that is higher subsequent higher anchors and lower subsequent lower anchors. In comparison to uncertain information,
false information induces a fragile anchoring bias (Qu et al., 2008). Anchoring behavior also induced by an empirical
assessment of price anchoring in online markets. The research explored that uninformative information impact choices
of investment decision, particularly the anchoring effect found in bidding for online auctions. Results of the study
confirmed the existence of anchoring behavioral bias in an online auction.
To understand the bias of disposition effect on individual investment decision making, the effect of disposition on the
underlying mechanism of e-trading of stocks has investigated by researchers in their study. It appeared in the study that
disposition has at times, negative financial effect. In recently emerged internet-based e-trading situation, disposition
effect is not affected by the possibility of prospective gains or losses (H.-J. Lee et al., 2008). The existence of
Disposition effect in investors of Taiwanese warrant markets assessed in a study. Empirical results of the research
indicated that the presence of disposition effect in Taiwanese warrant markets, and further, it also indicated that
warrants with different market price displayed different disposition behavior (Chang, 2008). To investigate the
influence of disposition effect on the investment decision making in an emerging Chinese stock market, the researchers
used the brokerage account data from China and found that Chinese investors made bad off trading decisions and
recognized gains but not losses. They incline to sell those stocks that have increased in price but not those stocks that
have decreased in price that displayed persistent disposition behavior (Gongmeng Chen, Kenneth A. Kim, 2007).
Further analyzing disposition effect, a paper investigated the effect in trading records of a discount brokerage house.
By applying socio-economic and demographic factors, empirical evidence has found that wealthier investors and
professional investors displayed a lower disposition effect (Dhar & Zhu, 2006). Empirical evidence was appeared to
study the market-wide impact of disposition effect on trading volume of Initial Public Offer and disposition effect was
found to be at its most reliable due to current purchase price initiated by investors in Initial Public Offer (Kaustia, 2004).
Similar to this IPO study, another research exists that investigated the aftermarket effect of disposition on IPO in Bursa
Malaysia market. This study indicated that 2.64 times more investors intended to flip a winning IPO in comparison to
losing IPO that result in consistent disposition effect (Chong, 2009). The role of disposition has studied in equity
premium. Paper showed that the existence of disposition effect by investors tend investors to bear higher risk
premiums for investing in stocks (Roger, 2009). Research has examined the disposition effect in the investment International Journal of Financial Research Vol. 10, No. 4; 2019
Published by Sciedu Press 59 ISSN 1923-4023 E-ISSN 1923-4031
performance in the Korean stock index futures market and provided strong evidence of the existence of disposition
effect in the investment performance. It found in the study that individual investors are more prone towards disposition
effect than foreign and institutional investors.
Moreover, there is a negative relationship between investment performance and disposition effect, and also disposition
bias is stronger in long positions than in short position. In support of this research, another study has been conducted to
study the effects of disposition on individual investors in the Taiwan Stock Market. Results of this research indicated
that the level of education of individual investors has a positive relationship with the disposition effect. The role of
disposition effect has been studied in Taiwan mutual fund investors. The result indicated that disposition effect varies
across market states. Disposition effect was not found uniform in Taiwan mutual fund investors. Results suggested that
during bear market investors redeem their mutual funds more in comparison to bull market. Therefore, in the research,
it was found that the phenomenon of disposition effect presents for Taiwan mutual fund investors also (J. S. Lee, Yen,
& Chan, 2013).
To study the impact of herding bias on individual investment decision making, an alternative approach was proposed to
assess an asymmetric risk-return relationship in financial markets with having the involvement of herding behavior.
Results of the study indicated inverse feedback found in Asian financial markets as a consequence of the herding
phenomenon (Bekiros, Jlassi, Lucey, Naoui, & Uddin, 2017). In one of the studies herding behavior of Muslims in
Islamic banking and finance has been studied. The study revealed the existence of herding behavior as the first-order
factor. Research has conducted where the impact of herding examined on the firm's business cycle. Findings of the
study suggested that the aggregated pattern of herding illuminated in a firm's business cycle (Mueller & Brettel, 2012).
On the contrary, evidence was obtained when it comes to herding effect at times when markets were highly volatile,
that Exchange traded funds’ return dispersion decreases due to excessively intraday volatility (Rompotis, 2018).
One of the studies examined how herding behavior is related to rational decision making among individuals. The
results of the study implied the existence of herding behavior in individual investment decision making. Also, further
in the study, it was demonstrated that herding bias is different in the case of male and female (Lin, 2011). One of the
researches contributed towards the understanding of the impact of herding behavior on property investment decisions
taken in a highly volatile environment. The study suggested that herding behavior may exist in the decisions made by
property fund managers (Hall, 2016). Experimental research examined the impact of herding behavior on
diversification behavior. Results suggested that herding behavior significantly influence nonoptimal portfolio choices
(Filiz, Nahmer, Spiwoks, & Bizer, 2018). In one of the research influences of herding behavior was examined on
institutional investor attributing this behavior to consider same published information and thereby safeguarding
investors reputation and career. A role of herding behavior of mutual fund managers examined, and it analyzed that
herding behavior influenced by paper gain ratio and paper loss ratio. It also found that, but herding behavior of mutual
fund manager harms investment performance.
Moreover, in sell effect of herding behavior disappeared. It further found that even disposition effect also influences
herding bias as well. The presence of the herding effect more profound in Asian markets, their herding asymmetry
during rising markets have witnessed. Moreover, during the global crisis as well herding presence was found in the US
and Latin American markets. It was appeared in research that bank’s herding diverges with different types of loans. It
was showed in paper that herding was more frequently found in housing loans and credit cards than other types of loans.
Moreover, it was also examined in the paper that big banks herd more in comparison to smaller and regional banks
(Tran, Nguyen, & Lin, 2017). In one of the studies herding behaviour presence was investigated between stock market
and oil market during market distress. Researchers found that herding behaviour reduced due to the volatility of the
stock market. It was also examined and occurred that herding behaviour moved inversely in both the markets and
herding behaviour is further enhanced by unavailability of information in both the markets (Ben Mabrouk, 2018).
However, review of previous studies indicates that many behavioral biases are overlapping or expansion of other
behavioral biases. This study considers only four biases which frequently observed in investment decision-making.
The above-cited previous studies support the fact that behavioral biases have a significant impact on
investment-decision making. There are studies like Paul Slovic (1972) that claimed that behavioral biases have a
significant relationship with investors type. Fundamentally, Every investor influenced by different behavioral biases.
Therefore, different investors make investment decisions differently. The present research made an effort to overcome
the gaps found in literature and thereby analyze the impact of four behavior biases on investor's decision making.
The following is the conceptual framework, and that can draw from the above literature review: International Journal of Financial Research Vol. 10, No. 4; 2019
Published by Sciedu Press 60 ISSN 1923-4023 E-ISSN 1923-4031
Figure 1. Conceptual framework
Research hypothesis can draw from the above literature review:
Ha1: there is a significant relationship between behavioral biases and investment decision- making.
Ha2: overconfidence is significantly influenced by investment decision- making.
Ha3: anchoring is significantly influenced by investment decision- making.
Ha4: disposition is significantly influenced by investment decision- making.
Ha5: herding is significantly influenced by investment decision- making.
3. Research Methodology
The present study is a cross-sectional study and Quantitative method used for data analysis. A questionnaire designed
and survey method is applied to obtain responses. The actual sample size for the study was 385, but few questionnaires
found not adequately filled. Therefore, only 243 questionnaires were found useful and selected as sample size.
Convenience sampling technique is used to collect data from investors in National Stock Exchange. The purpose of
this research analysis is to interpret and draw a conclusion from the collected data. Inferential statistics, as well as
descriptive statistics, are applied for data analysis. Spss software used for statistical computation. Firstly, Cronbach
Alpha is applied to check the reliability of data. The range from 0.70- 0.90 is considered to be acceptable. Secondly, the
correlation coefficient is carried out to test the relationship among variables that is investment decision making and
behavioral biases. Then, finally, regression analysis is conducted to identify the impact among variables.
4. Results
This chapter comprised of results and interpretation of the data. First, the Cronbach's alpha test for measurement of
reliability done. Secondly, the correlation coefficient among the behavioral biases and investment decision making is
studied. The results of the reliability test (Table 1) indicated that the value of Cronbach Alpha is 0.762. Therefore, the
scale is reliable as the value of Cronbach alpha is more significant than 0.6.
Table 1. Reliability statistics
Table 2. Investor’s demographic profile
Cronbach's Alpha
Cronbach's Alpha Based on Standardized Items
N of Items
Demographic Components
18 -25
25 30
30 35
Above 35
Investment decision making International Journal of Financial Research Vol. 10, No. 4; 2019
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4.1 Correlation Analysis
Table 3. Results of correlation
Sig. (2-tailed)
Sig. (2-tailed)
Sig. (2-tailed)
Sig. (2-tailed)
Sig. (2-tailed)
**. Correlation is significant at the 0.01 level (2-tailed)
From the analysis of the relationship among variables (table 3), it found that all the variables have a strong relationship
Level of Education
Under 5 years
6 -7 years
8 - 10 years
11 - 15 years
Above 15 years
13.2 International Journal of Financial Research Vol. 10, No. 4; 2019
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with each other. There is a positive relationship between investment decision-making and overconfidence variables,
that is 0.409 at 1% level of certainty. This shows that if the overconfidence of investor increases, the investor’s
decision making for investment also increases. The degree of relationship between Anchoring bias and investment
decision making is 0.378, which is critical at a 1% level of certainty. This expresses that there is a significant positive
relationship between Anchoring bias and investment decision making variable, and when anchoring increases, then
investment decision making also rises. The connection between disposition bias and investment decision making also
significantly positive, which is 0.325 noteworthy at 1% level of certainty. This expresses that investment decision
making also increases with increase in disposition bias also. The degree of relationship among herding bias and
investment decision making is 0.402 at 1% level of certainty which shows that there is a positive relationship among
these two variables and if herding bias increases then investor make investment decisions accordingly.
4.2 Regression Analysis
Table 4. Regression analysis
Predictors: herding, overconfidence, disposition, anchoring
The analysis mentioned above (table 4) shows the results of linear regression. Investment decision-making considered
as the dependent variable and four independent variables considered for the study, i.e., Overconfidence, anchoring,
disposition, and herding bias. To assess the model fitness, the results of the model summary evaluated. The model
summary indicates that R Square shows 0.235 variations in investment decision making is explained by the model,
while adjusted R squared is 0.218, which is close to r squared. A reliable model indicated because of the high value of
R. The results show 23.5% of predictability level, which is low. However, this may be because of the other variables
not considered in this model.
4.3 Overall and Individual Difference
The below mentioned F-statistics (Table 5) shows the overall fitness of the model, and it can be analyzed that the model
is overall fit as its p-value (sig.) is less than 0.05.
Table 5. Overall significance
Table 6. Individual significance
R Square
Adjusted R Square
Std. Error of the Estimate
a. Dependent Variable: Investment decision-making
b. Predictors: (Constant), herding, overconfidence, disposition, anchoring
Unstandardized coefficients
Std. Error
0.012 International Journal of Financial Research Vol. 10, No. 4; 2019
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The results mentioned above (table 6) show individual significance test, which depicts that overconfidence and herding
bias have a significant influence on the investment decision-making of an investor as their significance values are less
than 0.05. The remaining variables i.e., anchoring and disposition biases have significance value higher than 0.05,
indicating that disposition bias and anchoring bias, do not have any influence on investment decision-making of
5. Discussion
While dealing with different investment options, there is one fundamental question people face that what is the best
plan of action for investing in the financial instruments and to what magnitude can the historical price movements in
the stock market can be used to forecast of the future price movements? Based on the assumption of rationality, it
appeared that investors would choose those financial instruments that maximize gains and minimize losses (Ahmad
Zamri, Ibrahim, Haslindar, Tuyon, 2017). People are considered partly rational and irrational in their investment
behavior. Behavioral finance studied the irrational aspect of human as an investor in their investment decision process.
This branch of discipline indicated that cognitive biases prevent investors from realizing a complete sense of rationality
at the time of investment decision-making. An act of rationality linked with a magnitude of uncertainty and risk, which
is associated with every investment decision option (Paul Slovic, 1972). Various behavioral biases influence these risk
and uncertainty. These behavioral biases focus on investor's behavior and their investment decision-making process.
Behavioral biases are considered to be building blocks of behavioral finance that combine individual behavior and
market phenomenon. In behavioral finance domain, various biases exist. For the present research, four biases have
considered for a study that is overconfidence, anchoring, disposition effect, and herding bias.
After studying research papers on various behavioral biases, it documented that most of the financial and economic
theories displayed that individuals behave rationally in the process of investment decision making only when they have
all available relevant information. When information is not fully available to them in that case, empirical evidence
indicated repeated patterns of irrationality that appeared like the way individual investors arrive at decisions and
choices when confronted with risk and uncertainty. The four common behavioral biases also documented that their
presence exists in the stock market or portfolio selection during decision making. Their presence leads to excessive
trading volume and results in higher transaction cost. The present study also throws light on market psychology
whereby it indicated that investors buy or sell stocks and why sometimes they do not buy or sell at all hence, the most
critical challenge faced by investors is in the field of investment decisions. It reflected in the present study that the
gains and losses realized by investors mainly depend on his investment decision-making competences. It observed that
in the present scenario, investment decision-making process needs a better understanding of individual investor's
behavioral biases as the existence of these behavioral biases have also been found both before and during the global
financial crisis as well. The focus in the present study was on individual investors as they found to have limited
knowledge about the application of conventional finance theories in decision making and hence more inclined towards
making psychological errors. The result of our study shows that from four independent variables only two variables
namely Overconfidence and herding bias have a significant impact on investment decision making with t-values of
3.759, 2.561 respectively while other variables namely disposition and anchoring have no significant impact on
investment decision making. The present study was focussed on mainly formulation and analysis of four behavioral
bias, namely, anchoring, overconfidence, disposition effect, and herding behavior. In future study can be elaborated by
analyzing other behavioral biases that too have a substantial impact on individual investment decision making in their
unique way. Moreover, the study can also further be elaborated to investigate the impact on a group or corporate
investment decision making as well.
6. Conclusion
The discipline of behavioral finance has emerged in response to handle the difficulties faced by the traditional finance
discipline. In essence, behavioral finance explains that investment choices not always influenced based on rationality.
Behavioral finance also tried to understand the investment market anomalies by unwinding the two assumptions of
standard finance, that is, (i) investors fail to update their beliefs precisely and (ii) there is a systematic variation from
the normative process in making investment choices (Kishore, 2004).
In the 1960s, Kahneman and Tversky concentrated on different fields of research. After the energy crisis of the 1970s,
they came together and conducted research and found inconsistent results with the Efficient Market Hypothesis and
Expected Utility theory (Daniel Kahneman and Amos Tversky, 1979). In the 1980s, behavioral finance has emerged as
an alternative perspective that combined the behavioral and psychological aspects in economic and financial
decision-making or in another way we can understand that this field of behavioral finance provides behavioral and
psychological explanations (Abay, Blalock, & Berhane, 2017). Scientific work on the normative theory by Tversky International Journal of Financial Research Vol. 10, No. 4; 2019
Published by Sciedu Press 64 ISSN 1923-4023 E-ISSN 1923-4031
and Kahneman's 'psychophysical emphasis on the difference between objective stimulus and subjective sensation'
combined precisely to suffice the motive (Heukelom, 2007). Behavioral finance helps the academicians, economists,
and researchers to study financial markets in complex and uncertain circumstances (Shleifer, 2000).
To understand the irrational behavior of investors in financial markets, researchers draw on knowledge from cognitive
psychology theories. Researchers have developed "prospect theory" and "heuristics" to explain the behavior of
individual investors in financial or economic decisions. Behavioral finance involves various behavioral biases based
on an individual's social and emotional recognition and tolerance. The present study aims to determine the influence of
behavioral biases on investment decision making of individuals. Mainly four behavioral biases are taken into current
research for identification of impact, namely, Overconfidence, Anchoring, disposition effect, and Herding. The study
concluded that two, namely overconfidence and herding, have a strong influence on the investment decisions of
individuals. The current research also emphasized that participants in financial markets are not rational in their
decision-making process, and even their choices are limited.
In the present study, the analysis and discussion brought forward many issues for further research. In the future,
investigations can be performed to study other biases that are not included in the present study this time. Also, the
impact of decisions of individuals and institutions can be reviewed on mutual funds as well.
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Personality and Emotions are essential attributes that differentiate one individual from another. A thorough understanding of both constructs can be beneficial for uncovering the linkages in human behaviour. This review sought to determine whether emotional intelligence (EI) was only a measure of personality traits or a distinct concept. In this article, a systematic review is presented of the relationship between personality traits and emotional intelligence, through a review of 80 research papers spanning from the year 2000 to 2021. Papers have been critically examined to identify the type of study, sample and models that have been employed over the years. Interest in this topic has gradually increased over the years and continues till today. The findings suggest that there is a relational and predictive relationship between emotional intelligence and personality traits; however, the results can vary depending on the combination of measures used. Additionally, personality traits are more closely associated with trait EI than ability EI. Despite the strong correlation between the two constructs, EI has been shown to be a reliable predictor of other variables beyond personality traits. Relation between EI and personality traits can vary among young and older individuals but the data available to support this claim is insufficient. The findings and research gaps that were identified through this review have relevance for many different research domains.
Purpose This study aims to establish the factors affecting the financial investment decision-making of an investor, with specific reference to investors’ emotions and how various events such as festivals, the pandemic and sports matches affect their investors’ investment decision-making. The authors further intend to understand the role of these investor emotions in creating stock market anomalies. Design/methodology/approach Twenty-nine semistructured exploratory interviews with fund managers from the top 10 asset management companies in India, who deal with individual investors regularly, were taken. The interviews were conducted to identify and describe the underlying ideas and sentiments that influence an individual’s investment behavior. Findings Although risk and return are the primary motivators of investment decisions, fund managers’ daily interactions with individual investors are affected by unpredictability and technical ambiguity, and investing is an inherently emotionally arousing process, according to the findings of the in-depth interviews. Originality/value To the best of the authors’ knowledge, this study is one of the first studies in Indian market to report the views of financial professionals about the emotional aspect of investors in making an investment decision. With most of the research conducted using quantitative methods, the current study brings in the perspective of financial professionals using primary data.
Purpose This study aims to systematically review various behavioral biases that impact an investor’s decision-making process. The prime objective of this paper is to thematically explore the behavioral bias literature and propose a comprehensive framework that can elucidate a more reasonable explanation of changes in financial markets and investors’ behavior. Design/methodology/approach Systematic literature review (SLR) methodology is applied to a portfolio of 71 peer-reviewed articles collected from different electronic databases between 2007 and 2021. Content analysis of the extant literature is performed to identify the research themes and existing gaps in the literature. Findings This research identifies publication trends of the behavioral biases literature and uncovers 24 different biases that impact individual investors’ decision-making. Through thematic analysis, an attribute–consequence–impact framework is proposed that explains different biases leading to individual investors’ irrationality. The study further proposes directions for future research by applying the theory–characteristics–context–methodology framework. Research limitations/implications The results of this research will help scholars and practitioners in understanding the existence of various behavioral biases and assist them in identifying potential strategies which can evade the negative effects of these biases. The findings will further help the financial service providers to understand these biases and improve the landscape of financial services. Originality/value The essence of the current paper is the application of the SLR method on 24 biases in the area of behavioral finance. To the best of the authors’ knowledge, this study is the first attempt of its kind which provides a methodical and comprehensive compilation of both cognitive and emotional behavioral biases that affect the individual investor’s decision-making.
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The cornerstone of modern finance is the efficient market hypothesis. Under this hypothesis all information available about a financial asset is immediately incorporated into its price dynamics by fully rational investors. In contrast to this hypothesis many studies have pointed out behavioral biases in investors. Recently it has become possible to access databases that track the trading decisions of investors. Studies of such databases have shown that investors acting in a financial market are highly heterogeneous among them, and that heterogeneity is a common characteristic of many financial markets. The article describes an empirical study of the daily trading decisions of all Finnish investors investing Nokia stock over a time period of 15 years. The investigation is performed by adapting and using methods and tools in network science. By investigating daily trading decisions, and by constructing the time-evolution of statistically validated networks of investors, clusters of investors—and their time evolution— which are characterized by similar trading profiles are detected. These clusters are performing distinct trading decisions on time scales ranging from several months to twelve years. These empirical observations show the presence of an ecology of groups of investors characterized by different attributes and by various investment styles over many years. Some of the detected clusters present a persistent over-expression of specific investor categories. The study shows that the logarithm of the ratio of pairs of statistically validated trading decisions is different for different values of the market volatility. These findings suggest that an ecology of investors is present in financial markets and that groups of traders are always competing, adopting, using and eventually discarding new investment strategies. This adaptation process is observed over a multiplicity of time scales, and is compatible with several conclusions of behavioral finance and with the assumptions of the so-called adaptive market hypothesis.
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Purpose The purpose of the paper is to empirically test the overconfidence hypothesis at Bombay Stock Exchange (BSE). Design/methodology/approach The study applies bivariate vector autoregression to perform the impulse-response analysis and EGARCH models to understand whether there is self-attribution bias and overconfidence behavior among the investors. Findings The study shows the empirical evidence in support of overconfidence hypothesis. The results show that the overconfident investors overreact to private information and underreact to the public information. Based on EGARCH specifications, it is observed that self-attribution bias, conditioned by right forecasts, increases investors’ overconfidence and the trading volume. Finally, the analysis of the relation between return volatility and trading volume shows that the excessive trading of overconfident investors makes a contribution to the observed excessive volatility. Research limitations/implications The study focused on self-attribution and overconfidence biases using monthly data. Further studies can be encouraged to test the proposed hypotheses on daily data and also other behavioral biases. Practical implications Insights from the study suggest that the investors should perform a post-analysis of each investment so that they become aware of past behavioral mistakes and stop continuing the same. This might help investors to minimize the negative impact of self-attribution and overconfidence on their expected utility. Originality/value To the best of the authors’ knowledge, this is the first study to examine the investors’ overconfidence behavior at market-level data in BSE, India.
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This article examines the existence of herding behaviour in the Egyptian stock market during the 2011 revolution period. Using daily and monthly data, we test for the existence of herding for the whole period, as well as for the pre- and post-revolution phases. For the whole period, our results fail to provide evidence of herding behaviour in the Egyptian stock market, but do provide evidence of adverse herding behaviour that exhibits non-linearity. The results also fail to provide evidence of herding behaviour during bull and bear markets, and show that herding behaviour is a short-lived phenomenon. When the pre- and post-revolution phases are considered separately, the results provide evidence of weak adverse herding for both phases and of adverse herding in bullish markets, but they are inconclusive regarding bearish markets. JEL Classification: G10, G15
Decision making process is very complex task that involves various activities like industry and company analysis along with analysis of past performance of individual stocks/assets. Asunder from this, one of the most important factors that influence the individual's investment decision is cognitive illusions. Individual investor‘s behavior is influenced by various heuristic and biases, which are brought to light by the emerging field of behavioral finance. This paper provides aconceptual framework of the various principles of Behavioral Finance including cognitive illusion: Heuristics, Overconfidence, Representativeness, Anchoring, Gambler's Fallacy,Prospect Theory, Loss Aversion, Regret Aversion, Mental Accounting and Disposition Effect.
This article studies the relationship between giving financial advice and the disposition effect in an online trading environment. Our empirical findings suggest that leader traders are more susceptible to the disposition effect than investors who are not being followed by any other trader. Using a difference-in-differences approach, we show that becoming a first-time financial advisor increases the disposition effect. This finding holds for investors who engage in foreign exchange trading and for investors who trade stocks and stock market indices. The increased behavioral bias may be explained by leaders feeling responsible to their followers, by a fear of losing followers when admitting a poor investment decision, or by an attempt by newly appointed leaders to manage their social image and self-image.
This experimental study examines the influence of herding [following the majority of fellow gamblers or the most successful gambler (guru)], status-quo bias, and the gambler’s fallacy on diversification behavior. We find that neither herding nor status-quo bias contributes significantly to non-optimal portfolio choices. The gambler’s fallacy, however, plays an important role in these decisions. Many subjects appear to find patterns in a history of random events and then use these “patterns” to infer the sequence of future events. The gambler’s fallacy is significantly responsible for the fact that the optimal structure of a portfolio is considered in only 37.7% of all choices made by an investor.
As herding is a typical characteristic of human behavior, many researchers have found the existence of herding behavior in online peer-to-peer lending through empirical surveys. However, the underlying neural basis of this phenomenon is still unclear. In the current study, we studied the neural activities of herding at decision-making stage and feedback stage using event-related potentials (ERPs). Our results showed that at decision-making stage, larger error related negativity (ERN) amplitude was induced under low-proportion conditions than that of high-proportion conditions. Meanwhile, during feedback stage, negative feedback elicited larger feedback related negativity (FRN) amplitude than that of positive feedback under low-proportion conditions, however, there was no significant FRN difference under high-proportion conditions. The current study suggests that herding behavior in online peer-to-peer lending is related to individual's risk perception and is possible to avoid negative emotions brought by failed investments.
The author examines whether the trading behavior of exchange-traded funds (ETFs) is biased by any herding effect. Return data of a sample of 66 and 34 large-cap and small-cap ETFs, respectively, are used over the period 2012–2016 to assess whether these funds herd and whether herding is more pronounced during extreme markets, during down markets, and during days with extreme trading activity and volatility. The results show that herding is not the case for ETFs. However, some evidence is obtained on a decreasing return dispersion among ETFs on days with negative market returns. Trading activity seems not to induce herding. On the contrary, the author obtains evidence that shows that the higher the trading volumes are, the higher the return dispersion among ETFs is. When it comes to herding during highly volatile markets, the author finds that return dispersion among ETFs decreases on days with extremely high intraday volatility. However, when assessing the relationship between return dispersion and volatility without focusing on days with extremely high risk, the author obtains strong evidence of a linear relation between the 2 variables. This contradicting finding suggests that the stronger the intraday volatility in the ETF market is, the wider the dispersion in returns among ETFs is.
Purpose The purpose of this paper is to investigate herding behavior around the crude oil market and the stock market and the possible cross-herding behavior between the two markets. The analysis examines also the herding behavior during financial turmoil and includes the investor sentiment and market volatility. Design/methodology/approach The authors use a modified version of the cross-sectional standard deviation and the cross-sectional absolute deviation to include investor sentiment, financial crisis and market volatility. Findings The authors find that the volatility of the stock market reduces the herding behavior around the oil market and boosts that around the stock market. However, the investors’ sentiment reduces the herding around the stock market and boosts that around the crude oil market. Consequently, the authors can conclude that the herding behavior around the two markets moves inversely and the herding in each market is enhanced by the lack of information in the other market. Research limitations/implications This paper is limited to the herding of stocks around the crude oil market and ignores the possible herding of commodities around the oil market. Originality/value The originality of the paper rests on the study of the possible cross-herding behavior between the oil market and the stock market especially during financial turmoil.