Research on Intelligent Early Warning of Corporate Bond Default Based on Machine Learning

Abstract

This study aims to construct an efficient and accurate intelligent early warning model for corporate bond default with the help of machine learning tools. The study takes the real default data from 2017-2022 as samples, uses random undersampling and SMOTE oversampling to deal with the unbalanced data, and applies random forest for feature screening. The data covers the market conditions in recent years, includes the actual performance of defaulted and non-defaulted enterprises, and effectively reflects the default characteristics of corporate bonds. The sample covers 12 industries, including transportation, non-ferrous metals, fossil energy, etc., to avoid single-industry bias and capture the similarities and differences of default risks in different industries. Three machine learning algorithms, namely XGBoost, logistic regression and decision tree, are selected to construct the early warning model, and XGBoost is determined to be the optimal model through the comparative analysis of accuracy, recall, AUC and F1 value. The core indicators include ROA, net cash flow from financing activities, etc. The XGBoost model integrates multiple decision trees to form a powerful prediction capability, and it can accurately identify 86% of bond default risk enterprises. The model provides a data-driven early warning tool for regulatory and investment decisions, verifies the effectiveness of integrated learning in unbalanced data scenarios, and is of great practical value for improving the risk-return control ability of the bond market.