Interpretable Machine Learning

This study introduces an innovative framework to interpret the behaviors of firm characteristics in predicting expected returns through machine learning models, directly addressing the challenges of transparency and interpretability. Our approach utilizes the Local Interpretable Model-Agnostic Explanations (LIME) to evaluate firm characteristics based on their statistical significance and behaviors—linearity, independence, insignificance, and interaction—offering a novel perspective on their predictive roles.

Our study introduces a novel framework to interpret machine learning asset pricing models through the Local Interpretable Model-agnostic Explanations (LIME) method. This methodology illuminates how the inclusion of LIME local coefficients, representing the interaction among characteristics within ML models, modifies the relationship between a firm characteristic and stock returns.