Prediction of Insider Trading Behaviours through Highly Interconnected Core People Network with Graph Machine Learning

This study investigates the potential of graph-based machine learning to enhance the detection and understanding of trading by insiders, encompassing both legal and potentially illicit activities. Given the complex and interconnected nature of financial markets, insider trading poses a significant challenge to market fairness and resource allocation efficiency. This research employs Graph Convolutional Networks to model insider trading within a network constructed from shared board memberships among S&P 500 companies. By integrating both historical stock prices and board interlocks, GCNs offer a comprehensive perspective to uncover subtle interactions leading to insider trading behaviours. The study aims to demonstrate the superiority of GCNs over traditional models on predicting the tendency of insider trading by conducting rigorous ablation studies and benchmark comparisons, which assess the impact of feature removal, edge deletion, and edge rewiring on model performance. These results demonstrate the potential benefits of graph-based methods for financial applications such as market regulation and monitoring.