Interpretable Machine Learning Models for Predicting the Next Targets of Activist Funds

Battle Count: 87

The model provides insights for predicting activist targets, which can be valuable for quantitative trading strategies focused on event-driven investing or risk management.

The implementation involves multiple data preprocessing steps, various machine learning models, and interpretation techniques like SHAP, requiring moderate to high technical expertise.

The paper provides detailed information on data sources, preprocessing steps, and model configurations, enhancing reproducibility.