Journal of Risk Model Validation
ISSN:
1753-9587 (online)
Editor-in-chief: Steve Satchell
The application of a corporate bond default risk-identification model to digital economic security
Need to know
- A CatBoost-based risk identification model is developed for corporate bond default prediction.
- Financial performance and corporate governance indicators were jointly incorporated to improve risk recognition.
- The proposed model achieved 0.88 accuracy, 0.91 F1 score, and 0.99 precision at a training set size of 1000.
- CatBoost reduced recognition time to 0.9 s at 40 iterations, outperforming Random Forest, Back-Propagation, and Logistic Regression models.
Copyright Infopro Digital Limited. All rights reserved.
As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (point 2.4), printing is limited to a single copy.
If you would like to purchase additional rights please email info@risk.net
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (clause 2.4), an Authorised User may only make one copy of the materials for their own personal use. You must also comply with the restrictions in clause 2.5.
If you would like to purchase additional rights please email info@risk.net
