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<p>Predicting corporate financial crises is challenging due to the imbalanced and multi-state nature of real-world financial datasets. To address this issue, we developed a reinforced distillation learning (Rf-DL) method that combines fine-grained classification with reinforcement learning and knowledge distillation (KD). Corporate financial conditions can be subdivided into four categories using the Rf-DL method: financial soundness, onset of crisis, moderate crisis, and severe crisis. Deep networks are employed for pre-training, then sample weights are iteratively updated through a reward function before an optimal student network is identified to improve multi-class imbalanced classification. Fine-tuning techniques are also applied to enhance accuracy while reducing the number of parameters. Empirical results based on Chinese listed companies indicate that the Rf-DL model outperforms traditional deep networks and standard KD in recognizing multi-class financial conditions. The reinforced distillation mechanism effectively recognizes heterogeneity within classes, while comparisons with ensemble models highlight its success in handling minority classes. In summary, this paper presents a practical and robust approach to financial crisis prediction.</p>

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Reinforced Distillation Learning: Fine-Grained Imbalanced Classifier for Financial Crisis Prediction

  • 期刊论文

  • 发表日期：

    2025年3月29日

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  • Zengli Mao,
  • Xiaofang Chen,
  • Chong Wu

摘要

Predicting corporate financial crises is challenging due to the imbalanced and multi-state nature of real-world financial datasets. To address this issue, we developed a reinforced distillation learning (Rf-DL) method that combines fine-grained classification with reinforcement learning and knowledge distillation (KD). Corporate financial conditions can be subdivided into four categories using the Rf-DL method: financial soundness, onset of crisis, moderate crisis, and severe crisis. Deep networks are employed for pre-training, then sample weights are iteratively updated through a reward function before an optimal student network is identified to improve multi-class imbalanced classification. Fine-tuning techniques are also applied to enhance accuracy while reducing the number of parameters. Empirical results based on Chinese listed companies indicate that the Rf-DL model outperforms traditional deep networks and standard KD in recognizing multi-class financial conditions. The reinforced distillation mechanism effectively recognizes heterogeneity within classes, while comparisons with ensemble models highlight its success in handling minority classes. In summary, this paper presents a practical and robust approach to financial crisis prediction.