Empirical Asset Pricing Using Explainable Artificial Intelligence

Abstract

This paper applies explainable artificial intelligence in empirical asset pricing to explain the reasoning behind return predictions made by various complex machine learning models. We use two state-of-the-art explainable AI methods, LIME and SHAP. Our findings indicate that the primary drivers in our model predictions are stock-level characteristics such as momentum, 52-week high, and volatility. We demonstrate large improvements in predictive power and investment performance when incorporating insights from explainable AI into model refinement, surpassing the performance of machine learning models without such explanations. Additionally, we use a variety of data visualization methods within explainable AI to help institutional investors interactively communicate the inner workings of these models to stakeholders.

Keywords: Explainable Artificial Intelligence; Asset Pricing; Machine Learning; SHAP; Investment Performance

JEL Classification: C52, C55, C58, G12, G17