Effective identification of corporate default risk is crucial for maintaining financial stability and safeguarding investors’ interests. Existing models remain limited in addressing class imbalance and the dynamic evolution of default-related features over time. To overcome these challenges, we propose an adaptive spherical neighborhood resampling and class-specific reliability evidential reasoning model (ASNR-crER). By combining feature-weighted minority sample reconstruction with reliability-guided recursive evidence fusion, the proposed model aims to improve the prediction accuracy of both default and non-default firms under class imbalance. This study uses Chinese listed small enterprises from 2000 to 2023 as the research sample, comprising 10,449 firm-year observations from 2182 firms. By matching default status in year t with firm indicators from t-0 to t-5, six rolling prediction windows are constructed. The empirical results show that: (1) Compared with mainstream benchmark methods, ASNR-crER achieves the best overall performance in terms of accuracy, AUC, and F1 across all prediction windows, indicating that it can more reliably identify high-risk default firms while maintaining strong recognition of non-default firms. (2) SHAP analysis indicates that financial, non-financial, and macroeconomic indicators exert time-varying effects on corporate default risk. Financial indicators, including “Retained earnings/total assets”, “Other receivables/current assets”, and “Annualized return on assets”, reflect internal capital accumulation and profitability, serving as key predictors of default risk. Non-financial indicators, such as “Top 10 Tradable Shares H-index” and “Top 10 shareholders H-index”, can provide supplementary signals for medium-term risk identification. Macroeconomic indicators, including “M2 YoY growth rate”, “Urban HH per capita income”, and “Benchmark short-term loan rate”, show stronger explanatory power in longer prediction windows. Therefore, this study provides an effective early-warning tool for financial institutions and relevant stakeholders to identify high-risk firms, and enriches empirical evidence on the time-varying drivers of corporate default risk.
Gao et al. (Fri,) studied this question.