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 charac-teristics 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 eco-nomically meaningful interactions in bond pricing, notably related to term structure and macroeconomic un-certainty. 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
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