Insider Trading

This project builds upon my PhD dissertation, "Identification, Classification, and Interpretation of the Covariates Characterizing Unlawful Insider Trading: A Comparative Analysis Using Machine Learning Techniques". The following papers have been published as part of this research:

• A Random Forest Approach to Detect and Identify Unlawful Insider Trading

• An Extreme Gradient Boosting (XGBoost) Trees Approach to Detect and Identify Unlawful Insider Trading (UIT) Transactions

• Detecting and Explaining Unlawful Insider Trading: A Shapley Value and Causal Forest Approach to Identifying Key Drivers and Causal Relationships. In review by Computational Economics.