Theoretical Discussion on Individual Investor Behavior from a Quantitative Finance Perspective: Possibilities for Machine Learning Applications

Abstract

Understanding the behaviors of individual investors is a complex and crucial task in the modern financial landscape. As the financial markets continue to grow in complexity and digitalization, the behavior patterns and decision- making processes of individual investors are increasingly drawing the attention of scholars and market regulators. This study embarks from a quantitative finance perspective, theorizing on the potential application of machine learning in analyzing and predicting individual investor behavior. Despite the myriad influences on investor behavior—such as individual differences, market conditions, information factors, and psychological biases—we still identify common patterns in their behavior. Furthermore, we propose that machine learning technology holds significant potential for predicting the behavior of individual investors. This study presents new perspectives and methods for understanding and predicting the behavior of individual investors.