Abstract:
Debt default risk is the result of the combined effect of subjective and objective factors including corporate financial condition, industry cycles, and internal governance. Traditional debt default risk prediction models mainly utilize financial indicators including capital structure, profitability, and operating efficiency, failing to fully reflect the impact of subjective factors such as managerial project planning, internal control, and risk response. Using the text of Management Discussion and Analysis (MD&A) in annual reports, this paper takes Shanghai and Shenzhen A-share listed companies as samples, identifies debt default events based on litigation and arbitration data, extracts 31 text topic factors using the LDA topic model, and employs machine learning models such as XGBoost to predict corporate debt default risk. The study finds that after adding text topic factors into financial indicators, the prediction performance of corporate debt default model improves significantly. The accuracy, AUC value, F1-score, precision, and recall of the XGBoost model increase by 4.1, 2.4, 4.1, 4.7, and 3.5 percentage points, respectively. Further analysis indicates that topic factors related to industry characteristics, project investment, and internal control have relatively large marginal contributions to the model's prediction results, helping the model identify sources of operating pressure, risk transmission paths, and management response logic, thereby enhancing the explanatory power and forward-looking capability of debt default prediction. In addition, text topic factors demonstrate good reusability; after changing machine learning models, using datasets with different time windows, and conducting grouped tests by enterprise size, text topic factors still significantly improve the prediction effect of debt default. This paper provides a new tool for monitoring corporate debt default risk and maintaining financial stability and security.
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