A Random Forest approach to detect and identify Unlawful Insider Trading
This study proposes a new method for detecting insider trading. The method combines principal component analysis (PCA) with random forest (RF) algorithms. The results show that this method is highly accurate, achieving 96.43% accuracy in classifying transactions as lawful or unlawful. The method also identifies important features, such as ownership and governance, that contribute to insider trading. This approach can help regulators identify and prevent insider trading more effectively.