Adaptive Meta-Weighting Learning Model for Financial Distress Prediction in Listed Corporations

Corporate debt crises constitute a critical source of instability in modern financial distress, rendering their early prediction essential for market regulators and investors. However, corporate debt crisis prediction is severely hindered by extreme class imbalance, as actual crisis samples are far fewer than normal ones. This issue greatly undermines the robustness and generalization ability of conventional forecasting models. To address this issue, we propose an adaptive meta weighting learning (named AMetaW) for corporate debt crisis prediction. Specifically, the model incorporates an adaptive meta weighting mechanism to alleviate class imbalance, ensuring that rare crisis samples receive sufficient attention during training. Moreover, AMetaW integrates multiple financial characteristics into a unified framework, while employing explainable machine learning techniques to reveal the heterogeneous importance of indicators across regions. Empirical analysis using firm-level data across multiple provinces in China demonstrates that: (1) AMetaW achieves superior predictive performance compared with state-of-the-art baselines under imbalanced conditions; (2) our analysis reveals that short-term benchmark interest rate, equity concentration degree, and operating profit margin are consistently the strongest predictors of debt crises; and (3) the relative importance of indicators varies across regions, with eastern firms more sensitive to equity concentration degree and cash ratio, while western firms are more exposed to risks from short-term benchmark interest rate and operating profit margin. These findings provide both methodological contributions to Corporate Debt Crises forecast model and practical insights for region-specific debt crisis prevention and offering practical guidance for group enterprises and regulators.