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Predicting bond risk premiums with machine learning: Evidence from China

Author

Listed:
  • Chai, Bailin
  • Jiang, Fuwei
  • Lin, Yihao
  • You, Tian

Abstract

This study evaluates the ability of machine-learning algorithms to forecast bond risk premiums in the Chinese market. Using a comprehensive set of macro-, firm- and bond-level predictors, we find that machine learning, especially neural network, delivers markedly higher out-of-sample performance than traditional linear benchmarks. The local per-capita fiscal expenditure (EXPEND), bond credit ratings (CREDIT), and profitability- and intangible-related firm characteristics emerge as the most informative variables. Predictive gains are especially pronounced for low-rated issues, non-state-owned enterprises, and periods of heightened economic policy uncertainty. Incorporating machine-learning-based forecasts also helps to enhance credit rating accuracy. Collectively, our findings highlight the value of non-linear machine learning modeling techniques for bond pricing in emerging markets.

Suggested Citation

  • Chai, Bailin & Jiang, Fuwei & Lin, Yihao & You, Tian, 2025. "Predicting bond risk premiums with machine learning: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 93(C).
    • Handle: RePEc:eee:pacfin:v:93:y:2025:i:c:s0927538x25002197
    DOI: 10.1016/j.pacfin.2025.102882
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    Keywords

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    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G30 - Financial Economics - - Corporate Finance and Governance - - - General

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