DISCUSSION THREAD 2
Data Science: 2.6 Behavioral Models
Behavioral biases have a direct influence on one’s decision-making process. These biases
can consist of irrational ways of thinking, beliefs, and/or behaviors. When applied in the world of
Finance, behavioral biases found amongst investors can impact the stock market significantly.
Investors exhibiting these biases rely more on emotions than logic when making investment
decisions. The biased led investment decisions can lead to the creation of several different types
of market anomalies which is nothing more than when stock pricing contradicts the expected
performance of a stock. To understand how investors’ behavioral biases link to market
anomalies, behavioral models are used. The pseudo-Bayesian model is one commonly used to
examine the linkage between the two. This model reflects investors’ behavioral biases in
decision-making and incorporates two types of cognitive heuristics: conservatism heuristics and
representativeness heuristics. In this discussion, we will look at the chain reactions caused by
behavioral biases in investment. We will also take a deeper look into the two cognitive heuristics
and how they tie into market influence. We will end the discussion by discussing areas of future
research.
Investor cognitive bias is one of the main reasons for abnormal movements in stock
prices and it leads to mispricing and market inefficiency (Li, He & Shi, 2023; Cao & Copeland,
2023). There are two types of cognitive bias that lead to this phenomenon: representativeness
heuristic bias and conservatism heuristic bias. Representativeness bias is when judgements and
decisions are based on fresh information and results in investors updating their beliefs too
dramatically (Li, He, & Shi, 2023; Rika Dwi, Syariati & Sumarlin, 2022). Conservatism heuristic
bias believe that recent information is only temporary, and they are accustomed to clinging onto
prior beliefs based on earlier information and it results into slow updating of beliefs (Li, He &
DISCUSSION THREAD 3
Shi, 2023; Ha & Oh, 2021; Lévy-Garboua, Askari & Gazel, 2018). Thanks to cognitive bias,
momentum profits are the outcome of exploiting mispriced stocks and they reflect variations in
expected returns (Cao & Copeland, 2023). Momentum is when stock prices trend in either an
upward or downward direction. Investment behavior influenced by cognitive biases can create a
change in trading behaviors and strategies which can lead to underreaction or overreaction to
new news. This change can thus create a shift in momentum and the shift in momentum is known
as market volatility. An investor’s overconfidence can be shaped by market volatility, and it
changes their attitude towards risk (Cao & Copeland, 2023). Overconfidence is a cognitive bias
where investors believe they can do more than what they are actually capable of doing and can
lead to overestimation, over-placement, and over-precision (Lévy-Garboua, Askari & Gazel,
2018; Rika Dwi, Syariati & Sumarlin, 2022). This bias in investments can reduce accuracy in
investment decisions and lead to stock market anomalies. When we take a look at
overconfidence, we also need to understand what factors can lead to an investor being
overconfident in decision making. Prior information or beliefs, and the illusion of control can
make them believe in possible success and lead to overconfidence in events. Studies showed that
the illusion of control, or the thought that they have more control over things than they really do,
was the most compelling and relevant factor creating overconfidence (Rika Dwi, Syariati &
Sumarlin, 2022; Lévy-Garboua, Askari & Gazel, 2018). If we examine these biases in their
entirety, we find emotions at the center of it all. Emotions promote risky decisions and
sometimes make investors’ behavior irrational which leads to the emergence of bias in decision
making.
The five articles used in this discussion focused on cognitive biases and their effect on
investment decision making. However, there wasn’t much consideration given to the entire role
DISCUSSION THREAD 5
References
Cao, J. & Copeland, L. (2023). Momentum and market volatility: A Bayesian regime-switching
model. The European Journal of Finance, 29(5), 483-507.
https://doi.org/10.1080/1351847X.2022.2062250
Ha, Y., & Oh, H. (2021). Prior beliefs in market efficiency and fund cash flows. Applied
Economics, 53(59), 6878-6896. https://doi.org/10.1080/00036846.2021.1949434
Lévy-Garboua, L., Askari, M., & Gazel, M. (2018). Confidence biases and learning among
intuitive Bayesians.Theory and Decision,84(3), 453-482.
https://doi.org/10.1007/s11238-017-9612-1
Li, S., He, F., & Shi, F. (2023). Cognitive biases, downside risk shocks, and stock expected
returns.GNorth American Journal of Economics and Finance.,G68.
https://doi.org/10.1016/j.najef.2023.101981
Rika Dwi, A. P., Syariati, A., & Sumarlin. (2022). Chain reaction of behavioral bias and risky
investment decision in Indonesian nascent investors. Risks, 10(8), 145.
https://doi.org/10.3390/risks10080145
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