论文检索

Machine learning methods in asset pricing are often criticized for their black box nature. We study this issue by predicting corporate bond returns using interpretable machine learning on a high-dimensional bond characteristics data set. We achieve state-of-the-art performance while maintaining an interpretable model structure, overcoming the accuracy–interpretability trade-off. The estimation uncovers nonlinear relationships and economically meaningful interactions in bond…

潜在资产定价因子的估计AI 解读

We develop an estimator for latent factors in a large-dimensional panel of financial data that can explain expected excess returns. Statistical factor analysis based on Principal Component Analysis (PCA) has problems identifying factors with a small variance that are important for asset pricing. We generalize PCA with a penalty term accounting for the pricing error in expected returns. Our estimator searches for factors that can explain both the expected return and…