Imbalanced corporate bond default modeling using generative adversarial networks oversampling techniques

Based on the data of corporate bond issuers in Chinese market, this study applies oversampling techniques including Wasserstein generative adversarial networks (WGAN) and SMOTE to the imbalanced sample to improve the performance of bond default prediction. To explore the effect of oversampling techniques on classification models, the predictive outputs with difference imbalanced ratios are reported in the experimental results. It finds that the classification performance is significantly improved with the application of oversampling techniques, and the improvement is further enhanced when the sample distribution becomes more balanced. Compared to the classical SMOTE technique, both AUC and F1 score can be improved by WGAN. Overall, the experimental results demonstrate that the predictive performance of bond default models can be effectively boosted by generating artificial minority samples based on WGAN combined with the application of machine learning algorithms, which provides new insights into the bond default risk prediction of imbalanced samples. © 2022 Systems Engineering Society of China. All rights reserved.