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Bond defaults in China: Using machine learning to make predictions

Author

Listed:
  • Bei Cui
  • Li Ge
  • Priscila Grecov

Abstract

This paper proposes a superior default‐prediction model using machine‐learning techniques. Traditional risk‐assessment tools have fallen short, especially for foreign investors who face significant transparency issues. Using detailed financial data on Chinese bond issuers, our model provides much broader coverage than international credit‐rating agencies offer. We achieve better than 90% accuracy in predicting credit‐bond defaults, significantly outperforming Altman's Z‐scores. This study not only advances predictive analytics in financial risk management but also serves as an early warning device and reliable default‐risk detector for investors aiming to navigate the complexities of the Chinese bond market.

Suggested Citation

  • Bei Cui & Li Ge & Priscila Grecov, 2025. "Bond defaults in China: Using machine learning to make predictions," International Review of Finance, International Review of Finance Ltd., vol. 25(1), March.
    • Handle: RePEc:bla:irvfin:v:25:y:2025:i:1:n:e70010
    DOI: 10.1111/irfi.70010
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    References listed on IDEAS

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    Cited by:

    1. Yi Lu & Aifan Ling & Chaoqun Wang & Yaxin Xu, 2025. "Why Bonds Fail Differently? Explainable Multimodal Learning for Multi-Class Default Prediction," Papers 2509.10802, arXiv.org.

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