The Impact of Investors’ Cognitive Biases and Artificial Intelligence on Capital Market Performance in the Tehran Stock Exchange: A Behavioral Finance ۳.۰ Approach

Behavioral finance provides a vital framework for understanding market anomalies that traditional theories struggle to explain, especially in emerging markets like the Tehran Stock Exchange (TSE), where investor behavior is often driven by heightened cognitive biases. While interest in artificial intelligence (AI) within finance is growing, research remains limited regarding the interaction between AI interventions and cognitive biases in the Iranian capital market. This study addresses this gap by investigating how AI tools can detect and mitigate biases such as overconfidence, herd mentality, loss aversion, and confirmation bias. Utilizing a mixed-methods approach, the research integrates primary data from ۴۵۰ surveys with secondary market data from ۲۰۱۸–۲۰۲۴. AI-driven methodologies, including sentiment analysis via natural language processing and machine learning, are employed to analyze investor behavior and market trends. Preliminary findings reveal that overconfidence significantly increases volatility ($p < ۰.۰۱), while herd behavior exacerbates market inefficiencies. Furthermore, AI-based sentiment analysis exhibits strong predictive power for price movements (R۲ = ۰.۶۸), and robo-advisory interventions demonstrate the potential to reduce emotional trading by ۳۴%. This study contributes to the concept of "Behavioral Finance ۳.۰" by highlighting AI's dual role as a diagnostic tool for bias detection and a therapeutic mechanism for debiasing. The results offer significant implications for regulators, fintech developers, and investors aiming to improve market efficiency in emerging economies through technology-integrated decision-making.

کلیدواژه ها:

Behavioral Finance ، Cognitive Biases ، Artificial Intelligence ، Tehran Stock Exchange ، Market Efficiency