Glass box machine learning and corporate bond returns

• 成果类型: Article
• 署名作者: Bell, Sebastian; Kakhbod, Ali; Lettau, Martin; Nazemi, Abdolreza
• 署名单位: Helmholtz Association; Karlsruhe Institute of Technology; University of California System; University of California Berkeley; National Bureau of Economic Research; Center for Economic & Policy Research (CEPR)
• 刊物名称: JOURNAL OF FINANCIAL ECONOMICS
• ISSN/ISSBN: 0304-405X
• DOI: 10.1016/j.jfineco.2026.104294
• 发表日期: 2026-07
• 页码: 104294
• 关键词: Corporate bonds; uncertainty; Glass box machine learning; Generalized additive model; Explainable boosting machine; cross-section; liquidity risk; large number; volatility; prices

摘要

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 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.