A novel URP-CNN model for bond credit risk evaluation of Chinese listed companies.

The effective identification of bond credit risk is critical for maintaining stability in the bond market and safeguarding investor rights. This paper proposes a URP-CNN model for evaluating bond credit risk of Chinese listed companies. This model effectively combines the unique advantages of the Unthresholded Recurrence plot in extracting and representing spatial domain association information, along with the powerful performance of Convolutional Neural Networks in image feature extraction and classification tasks, thus demonstrating outstanding overall performance in tackling the complex task of predicting default of listed company bonds. The research results show that the URP-CNN model constructed in this paper improves accuracy by 1 %–10 % and F1 score by 10 %–30 % compared to other methods, ensuring accurate identification of non-defaulting enterprises while effectively reducing Type II-Error occurrence rates. Furthermore, the combination of URP method and machine learning models significantly enhances model effectiveness. Compared to single models, models combined with the URP method show varying degrees of improvement in accuracy and F1-score numerical indicators. [ABSTRACT FROM AUTHOR]