Glass Box Machine Learning and Corporate Bond Returns

Sebastian Bell, Ali Kakhbod, Martin Lettau and Abdolreza Nazemi

No 33320, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: 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 dataset. 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 pricing, notably related to term structure and macroeconomic uncertainty. Subsample analysis reveals stronger sensitivities to these effects for small firms and long-maturity bonds. Finally, we demonstrate how interpretable models enhance transparency in portfolio construction by providing ex ante insights into portfolio composition.

JEL-codes: C45 C55 G11 G12 (search for similar items in EconPapers)
Date: 2024-12
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ecm
Note: AP
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