Interdisciplinary Studies in Media and Culture

Combining behavioral culture and artificial intelligence in analyzing cognitive biases of investors in the Iranian capital market

Document Type : .

Authors

Mehdi Rostamnia, 1
Seyed Abbas Hosseini Ghaffar 2
Ali Esmaili 3

1 PhD student in Financial Economics, Shahid Ashrafi University of Isfahani, Isfahan, Iran

2 Faculty of Shahid Ashrafi Isfahani University, Isfahan, Iran

3 Faculty member and assistant professor, Faculty of Islamic Education and Thought, University of Tehran

10.30465/ismc.2026.52273.2966
Abstract
. Cognitive biases can lead to misinterpretation of information, flawed analyses, and ultimately detrimental financial choices. The present study aims to identify and analyze these biases in the Iranian stock market, employing machine learning algorithms to model investor behavior. Data related to 10 selected stocks from the Iranian market were collected over a specified time period, preprocessed, and prepared for training machine learning models. In this research, two algorithms—logistic regression and random forest—were applied for performance comparison. The results indicated that the random forest model, with an accuracy of 97%, significantly outperformed the logistic regression model, which achieved 71% accuracy. These findings demonstrate the higher effectiveness of more sophisticated algorithms in understanding and analyzing investors’ irrational behaviors. The study was conducted without any bias in model selection, and all results are presented transparently and reproducibly. The primary objective is to enhance rational decision-making in the capital market through data-driven analysis and artificial intelligence tools. Finally, we demonstrate that each identified cognitive bias manifests in specific behavioral patterns in the Iranian financial market, influenced by the cultural roots of the population.

Keywords

Behavioral culture
behavioral economics
stock market
artificial intelligence
Subjects
Interdisciplinary Studies in Media and Culture
Volume 16, Issue 1 - Serial Number 31
1 (Spring& Summer)2026
September 2026

  • Receive Date 16 June 2025
  • Revise Date 04 May 2026
  • Accept Date 15 April 2026
  • Publish Date 23 August 2026

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