Financial Research Journal

Financial Research Journal

The Role of Experience in Behavioral Biases: A Bayesian Probabilistic Framework Under Uncertainty

Document Type : Research Paper

Authors
Farideh Faraji, 1
Hamid Rostami Jaz 2
Saeed Moradpour 2
Mahdi Dasineh 2
1 Ph.D. Candidate, Department of Accounting, Bandar Abbas Branch, Islamic Azad University, Bandar Abbas, Iran.
2 Assistant Prof., Department of Accounting, Bandar Abbas Branch, Islamic Azad University, Bandar Abbas, Iran.
10.22059/frj.2025.393962.1007734
Abstract
Objective
Given the importance of psychological biases and their influence on modeling decision-making variables under uncertainty, overconfidence and underconfidence biases have been examined through conceptual probabilistic frameworks to predict expected value, overall risk, downside risk, value-at-risk, and expected drawdown. However, the uncertainty inherent in these models limits their effectiveness, as it introduces asymmetry that is not adequately incorporated into their underlying structures. Accordingly, the main objective of this study is to provide a probabilistic framework to clarify managerial biases and examine the role of managers' experience in reducing behavioral biases in risky and uncertain financial environments based on the Markov Chain Monte Carlo method and the Metropolis-Hastings algorithm, in which the uncertainty conditions are well represented by using an asymmetric distribution function.

Methods The statistical population of this study consists of 40 investment companies listed on the Tehran Stock Exchange and the Iranian Over-the-Counter (OTC) market in 2022. The research methodology is based on a two-layer Bayesian framework combined with a normal distribution approach. Expected value, total risk, downside risk, 1% value-at-risk, and expected drawdown are estimated for three managerial states—rational, overconfident, and underconfident—using the Markov Chain Monte Carlo (MCMC) method and the Metropolis–Hastings algorithm based on 150,000 randomly simulated portfolio returns.

Results
The results show that overconfident managers tend to overestimate the likelihood of favorable events and underestimate the likelihood of unfavorable outcomes, whereas underconfident managers exhibit the opposite pattern by underestimating favorable events and overestimating unfavorable outcomes. The findings further indicate that overconfident managers overestimate expected values compared with rational managers while underestimating downside risk, value-at-risk, and expected drawdown. Conversely, underconfident managers underestimate expected values but overestimate downside risk, value-at-risk, and expected drawdown relative to rational managers. Also, managerial experience was identified as a moderating factor that reduces the effects of behavioral biases through increased use of historical data and quantitative analysis. The results of Bayesian hierarchical modeling confirmed that managers’ practical experience not only reduces biases but also improves the ability to more accurately predict risk and returns.

Conclusion
This study develops a probabilistic framework for modeling the managerial biases of overconfidence and uncertainty. The framework is used as a tool to compare the differences and similarities of these biases and analyze their impact on expected value, overall risk, downside risk, value-at-risk, and expected drawdown of decision variables. In summary, both overconfident and underconfident managers can experience behavioral biases in their decision-making processes due to miscalibration of probabilities and distorted subjective probability distributions. Overconfident managers tend to overestimate the probability of success and underestimate risk, while underconfident managers do the opposite. These biases can lead to suboptimal decision-making and potentially negative outcomes for the organization. Although managers’ awareness of these biases and their efforts to assess probabilities and risks more accurately and objectively are essential for effective decision-making, managerial experience can play a significant moderating role in this process
Keywords
Experience
Managerial biases
Uncertainty
Value at risk
Subjects
 
 
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Financial Research Journal
Volume 28, Issue 3
2026
Pages 771-809