Predicting bond risk premiums with machine learning: Evidence from China

Bailin Chai, Fuwei Jiang, Yihao Lin and Tian You

Pacific-Basin Finance Journal, 2025, vol. 93, issue C

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.

Keywords: Chinese bond market; Risk premium; Machine learning; Big data; Credit rating (search for similar items in EconPapers)
JEL-codes: G12 G20 G30 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0927538X25002197
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:pacfin:v:93:y:2025:i:c:s0927538x25002197

DOI: 10.1016/j.pacfin.2025.102882

Access Statistics for this article

Pacific-Basin Finance Journal is currently edited by K. Chan and S. Ghon Rhee

More articles in Pacific-Basin Finance Journal from Elsevier
Bibliographic data for series maintained by Catherine Liu ().