Interpretable Machine Learning Model for Default Risk Identification of Corporate Bonds

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Abstract: Against the backdrop of the gradually exposed credit bond default risk in China, how to accurately identify and efficiently warn of corporate bond default risk has become a key concern for both academia and practice. To effectively solve a series of key problems in the traditional credit risk warning model, such as insufficient warning performance, single optimization target of hyperparameters, and weak model interpretability, this study integrates machine learning algorithms such as LightGBM, NSGA-II, and SHAP to constructs a LightGBM-NSGA-II-SHAP for early warning of corporate bond default risk, and empirically analyzes and tests the warning performance of the proposed model. The research results show that the warning accuracy of the proposed model exceed 85%, and compared with traditional machine learning models, the warning performance of the proposed model in this study is more excellent. In addition, the visualization of warning features’ impact on warning results is demonstrated through the SHAP algorithm, and it is found that coupon interest rate, profit margin on fixed assets, total issuance, and receivable turnover etc. are the key features for identifying corporate bond defaults.