Predicting individual corporate bond returns

Guanhao Feng (gavinfeng702@outlook.com), Xin He, Yanchu Wang and Chunchi Wu

Journal of Banking & Finance, 2025, vol. 171, issue C

Abstract: Using machine learning and many predictors, we find strong bond return predictability, with an out-of-sample R-squared of 4.48% and an annualized Sharpe ratio of 3.27. ML models identify important predictors for aggregate predictors (bond market returns, TERM and HML factors, GDP growth) and bond characteristics (downside risk, short-term reversal, return skewness, and credit spreads). Predictability varies over time, being stronger during periods of high investor risk aversion, slow economic growth, and strong cross-sectional factor explanatory power. Our results highlight the benefits of leveraging both cross-sectional and time-series predictors to forecast corporate bond returns while considering public and private bonds.

Keywords: Aggregate predictors; Bond characteristics; Forecast-implied investment gains; Machine learning; Time-varying return predictability (search for similar items in EconPapers)
JEL-codes: C53 G12 G17 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:171:y:2025:i:c:s0378426624002863

DOI: 10.1016/j.jbankfin.2024.107372

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