Significance of predictors: revisiting stock return predictions using explainable AI
本文使用可解释人工智能重新检验了166个资产定价特征对股票收益的预测能力，发现基于集成学习和深度学习的模型在预测中表现更优，且动量与交易类特征具有更高的预测力。
Abstract In this paper, we re-examine 166 previously identified asset pricing characteristics and their ability to successfully predict stock returns. We use Explainable Artificial Intelligence to rank these return predictors based on their importance in various asset pricing model settings. Our findings suggest that ensemble and deep learning-based models have an advantage in providing generalized predictions across different return measures. Using SHapley Additive exPlanations, we also find that momentum and trading-based features possess higher predictive power in estimating asset returns. The long-short portfolio analysis reveals that key return predictors exhibit substantial economic significance, reflected in the large differences in out-of-sample $$R^2$$ . These findings remain robust across various models and persist even after controlling for characteristics-based predictors.