Understandable AI Stock Pricing?

Can explainable artificial intelligence (AI) bridge the gap between complex machine learning predictions and economically meaningful interpretations? In their December 2023 paper entitled “Empirical Asset Pricing Using Explainable Artificial Intelligence”, Umit Demirbaga and Yue Xu apply explainable artificial intelligence to extract the drivers of stock return predictions made by four machine learning models: XGBoost, decision tree, K-nearest neighbors and neural networks. They use 209 firm/stock-level characteristics and stock returns, all measured monthly, as machine learning inputs. They use 70% of their data for model training, 15% for validation and 15% for out-of-sample testing. They consider two explainable AI methods:

1. Local Interpretable Model-agnostic Explanations (LIME) – explains model predictions by approximating the complex model locally with a simpler, more interpretable model.
2. SHapley Additive exPlanations (SHAP) – uses game theory to determine which stock-level characteristics are most important for predicting returns.

They present a variety of visualizations to help investors understand explainable AI outputs. Using monthly data as described for all listed U.S stocks during March 1957 through December 2022, they find that:

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