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Predicting individual corporate bond returns

Author

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  • Feng, Guanhao
  • He, Xin
  • Wang, Yanchu
  • Wu, Chunchi

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.

Suggested Citation

  • Feng, Guanhao & He, Xin & Wang, Yanchu & Wu, Chunchi, 2025. "Predicting individual corporate bond returns," Journal of Banking & Finance, Elsevier, vol. 171(C).
    • Handle: RePEc:eee:jbfina:v:171:y:2025:i:c:s0378426624002863
    DOI: 10.1016/j.jbankfin.2024.107372
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    More about this item

    Keywords

    Aggregate predictors; Bond characteristics; Forecast-implied investment gains; Machine learning; Time-varying return predictability;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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