<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper studies the financial risk management of resource allocation of listed commercial banks in my country, starting from the fundamental influence of lender credit rating in the internal rating system of commercial banks, and analyzes the influence of a large number of subjective factors contained in the bank’s credit rating system on the rating effect. Influence. A sort-response panel regression function model with random effects (including random intercept terms and random coefficients) is proposed, and the rationality of the existing bank credit rating system is tested and analyzed. The experimental results show that the model’s prediction accuracy rate is 48.27%, and the percentage of prediction level error within two levels reaches 86.8%. The application of this model can find the redundant indicators of the existing system, which are highly fitted with the actual situation and can achieve quite ideal forecasting results, and provide a forecasting reference for the weight setting, which provides a basis for the revision of the bank’s credit rating system.</p>
</abstract>

This paper studies the financial risk management of resource allocation of listed commercial banks in my country, starting from the fundamental influence of lender credit rating in the internal rating system of commercial banks, and analyzes the influence of a large number of subjective factors contained in the bank’s credit rating system on the rating effect. Influence. A sort-response panel regression function model with random effects (including random intercept terms and random coefficients) is proposed, and the rationality of the existing bank credit rating system is tested and analyzed. The experimental results show that the model’s prediction accuracy rate is 48.27%, and the percentage of prediction level error within two levels reaches 86.8%. The application of this model can find the redundant indicators of the existing system, which are highly fitted with the actual situation and can achieve quite ideal forecasting results, and provide a forecasting reference for the weight setting, which provides a basis for the revision of the bank’s credit rating system.

Application of regression function model based on panel data in financial risk management of bank resource allocation

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper studies the financial risk management of resource allocation of listed commercial banks in my country, starting from the fundamental influence of...

Application of regression function model based on panel data in financial risk management of bank resource allocation

Publié en ligne: 15 juil. 2022

Pages: 1849 - 1860

Reçu: 21 févr. 2022

Accepté: 17 avr. 2022

DOI: https://doi.org/10.2478/amns.2022.2.0097

Mots clés
regression function, credit rating, panel data model, ordinal response model, random effects

© 2023 Cheng Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1

Figure 2

Application of regression function model based on panel data in financial risk management of bank resource allocation

Publié en ligne: 15 juil. 2022

Pages: 1849 - 1860

Reçu: 21 févr. 2022

Accepté: 17 avr. 2022

DOI: https://doi.org/10.2478/amns.2022.2.0097

Mots clésregression function, credit rating, panel data model, ordinal response model, random effects

© 2023 Cheng Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1

Figure 2

Mots clés
regression function, credit rating, panel data model, ordinal response model, random effects