Predicting Credit Bond Default with Deep Learning: Evidence from China

Abstract

China’s credit bond market has rapidly expanded in recent years. However, since 2014, the number of credit bond defaults has been increasing rapidly, posing enormous potential risks to the stability of the financial market. This study proposed a deep learning approach to predict credit bond defaults in the Chinese market. A convolutional neural network (CNN) was selected as the classification model and to reduce the extreme imbalance between defaulted and non-defaulted bonds, and a generative adversarial network (GAN) was used as the oversampling model. Based on 31 financial and 20 non-financial indicators, we collected Wind data on all credit bonds issued and matured or defaulted from 2014 to 2021. The experimental results showed that our GAN+CNN approach had superior predictive performance with an area under the curve (AUC) of 0.9157 and precision of 0.8871 compared to previous research and other commonly used classification models—including the logistic regression, support vector machine, and fully connected neural network models—and oversampling techniques—including the synthetic minority oversampling technique (SMOTE) and Borderline SMOTE model. For one-year predictions, indicators of solvency, capital structure, and fundamental properties of bonds are proved to be the most important indicators.

Keywords

• credit bond default
• prediction
• convolutional neural network
• imbalanced data processing
• generative adversarial network