Predicting Bond Default Risk Based on the XGBoost Model

Abstract

At present, China faces a large scale of bond defaults. To address the problems of low prediction accuracy and limited indicator systems in traditional models, this paper conducts research on bond default risk prediction based on the XGBoost model. This study selects 616 valid samples of credit bonds from entity industries in the Wind database from 2019 to 2024, and constructs a multi-dimensional indicator system covering bond characteristics, financial leverage, and other factors. After data cleaning and feature selection, the dataset is divided into training and test sets at a 7:3 ratio, and the XGBoost model is trained using 3-fold cross-validation. The results show that the sustainability of corporate profitability is the core influencing factor of default. The model achieves an AUC of 0.997 on the test set, with no severe overfitting, demonstrating excellent generalization ability and prediction accuracy. This provides methodological support for the prevention and control of bond default risk.