Abstract: Bond default prediction faces multitude challenges, including severely imbalanced samples, concept drift, and the difficulty of identifying hard-to-classify samples. Existing models, both basic ones and improved ones to address individual issues, often fall short of meeting these complex demands. To address this, this paper proposes a concise composite loss function based on cross-entropy loss, termed as TW-focal loss, which adjusts the loss weights of different samples by incorporating tailored improvement factors, enabling the model to effectively learn from default samples, new samples and hard-to-classify samples. Using publicly issued credit bond data in China from 2014 to 2022, and adopting XGBoost as the experimental model, the empirical results show that TW-focal loss effectively controls the Type I error rate while reducing the Type II error rate. Compared to the standard cross-entropy loss, the performance evaluation index Gmean has increased by 46.4%, and by 12.9% compared to the weighted cross-entropy loss that focused on imbalance. Additionally, using the SHAP interpretation method, this paper analyzes the distribution of feature importance and the partial dependency curves under different loss functions. The results reveal that the model can control the recognition of default samples by altering the impact degree and range of features. This paper provides an effective attempt to complete the design of bond default prediction models and to explore the discriminative logic of the models.

Key words: bond default prediction, cross-entropy loss, imbalanced samples, concept drift, SHAP interpretation

摘要： 债券违约预测需应对样本不平衡、概念漂移及困难样本识别在内的多重问题。然而，现有的基础模型与解决单一问题的改进模型难以满足这种需求。为此，基于交叉熵损失提出一种改进的复合损失函数（TW-FocalLoss），通过加入改进因子来调节不同样本的损失权重，使得模型能有效学习 违 约 样本、新样本和困难样本。利用2014~2022年我国公开发行的信用债数据，以XGBoost为实验模型，结果表明，TW-FocalLoss使模型 在 降 低 第 2 类 错 误 率 的 同 时，能 够 有 效 控 制 第 1 类 错 误 率，性 能 评 估 指 标Gmean相比于交叉熵损失提升46.4%，相比于专注不平衡改进的加权交叉熵损失提升12.9%。进一步，通过SHAP解释分析了不同损失函数下模型的特征重要性分配比例和部分依赖曲线，发现模型可以通过改变特征的影响程度和影响区间来控制对违约样本的识别。该研究为债券违约预测模型的设计与逻辑探索提供了新思路。

关键词: 债券违约预测, 交叉熵损失, 不平衡样本, 概念漂移, SHAP解释