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Machine learning in corporate bonds: Evidence from China

Author

Listed:
  • Fang, Yvonne
  • Hu, Xiaolu
  • Zhong, Angel
  • Pan, Zheyao
  • Cao, Youdan

Abstract

This study employs a broad set of machine learning (ML) methods to examine cross-sectional variation in corporate bond returns in China. Using macroeconomic indicators together with bond- and issuer-specific characteristics, we find that ML techniques outperform traditional linear models in both statistical and economic terms. These models are particularly effective at capturing distinctive features of the Chinese market, including the dominance of state-owned enterprises, implicit government guarantees, and rapid market evolution. We compare long-short and long-only portfolio strategies to account for practical constraints on short selling. The results indicate that ML methods are effective in markets where institutional features and information asymmetries play a central role in asset pricing.

Suggested Citation

  • Fang, Yvonne & Hu, Xiaolu & Zhong, Angel & Pan, Zheyao & Cao, Youdan, 2026. "Machine learning in corporate bonds: Evidence from China," Journal of Banking & Finance, Elsevier, vol. 184(C).
    • Handle: RePEc:eee:jbfina:v:184:y:2026:i:c:s0378426626000105
    DOI: 10.1016/j.jbankfin.2026.107636
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    Keywords

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    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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