Dynamic Prediction of Bond Default Risk Driven by Deep Learning

The sound development of the bond market is crucial for maintaining financial security. However, traditional bond default risk prediction methods rely on subjective logical judgments, making them difficult to adapt to complex, nonlinear, and dynamic financial systems. Furthermore, existing models suffer from poor dimensionality reduction, weak interpretability, limited data types, and excessively long prediction intervals. This study aims to construct a deep learning-based dynamic prediction system for bond default risk, improving the accuracy, real-time performance, and interpretability of predictions, and addressing core pain points such as data sparsity, sample imbalance, and delayed early warning. The study integrates multi-dimensional data sources, including macroeconomic indicators, corporate financial data, market transaction information, and unstructured text. After preprocessing such as cleaning, encoding, and feature selection, a multi-layer neural network architecture is employed, combined with the SMOTE-Tomek sampling algorithm to balance the sample distribution. Early stopping mechanisms, learning rate decay, and hyperparameter optimization are used to improve model performance. Simultaneously, feature importance analysis and the SHAP value method are used to enhance model interpretability. Experimental results show that deep learning models significantly outperform traditional machine learning methods, with the Transformer model performing best, achieving an F1 score of 0.880 and an AUC of 0.952, and can identify default risks 12-18 months in advance. This research not only deepens the theoretical understanding of the factors influencing bond default risk but also provides an efficient tool for precise regulation by regulatory authorities and risk avoidance by investors, effectively compensating for the lag in traditional rating systems and having significant practical implications for improving my country’s credit risk management system.