2025/09/23 by Yanran Wu, Wu, Yanran, Zhang, Xinlei +6
Business, Management and Accounting · Economics, Econometrics and Finance · #Corporate Insolvency and Governance #Credit Risk and Financial Regulations #FOS: Economics and business #Financial Distress and Bankruptcy Prediction #General Economics (econ.GN)
paper · pdf · doi:10.48550/arxiv.2509.19042
openalex publication_date 2025/09/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We build a 167-indicator comprehensive credit risk indicator set, integrating macro, corporate financial, bond-specific indicators, and for the first time, 30 large-scale corporate non-financial indicators. We use seven machine learning models to construct a bond credit spread prediction model, test their spread predictive power and economic mechanisms, and verify their credit rating prediction effectiveness. Results show these models outperform Chinese credit rating agencies in explaining credit spreads. Specially, adding non-financial indicators more than doubles their out-of-sample performance vs. traditional feature-driven models. Mechanism analysis finds non-financial indicators far more important than traditional ones (macro-level, financial, bond features)-seven of the top 10 are non-financial (e.g., corporate governance, property rights nature, information disclosure evaluation), the most stable predictors. Models identify high-risk traits (deteriorating operations, short-term debt, higher financing constraints) via these indicators for spread prediction and risk identification. Finally, we pioneer a credit rating model using predicted spreads (predicted implied rating model), with full/sub-industry models achieving over 75% accuracy, recall, F1. This paper provides valuable guidance for bond default early warning, credit rating, and financial stability.