AI in Behavioral Finance: Understanding Investor Bias Through Machine Learning

Abstract

Behavioral finance explores the psychological influences and cognitive biases that affect investor behavior and financial decision-making. As markets become increasingly complex, traditional models often fall short in capturing the nuanced, irrational patterns observed in real-world investor conduct. This research investigates how artificial intelligence (AI), particularly machine learning (ML) techniques, can be applied to identify, analyze, and potentially predict behavioral biases in investor activity. By leveraging large-scale financial datasets—ranging from trading histories to sentiment analysis of financial news and social media—this study utilizes supervised and unsupervised ML models to detect patterns associated with common biases, including overconfidence, loss aversion, herding behavior, and confirmation bias.