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

In recent years, the assessment of corporate financial risk has become increasingly significant for banks. Therefore, studying models for corporate financial risk assessment holds substantial practical importance. This paper combines the Logistic model and the Lasso model based on their basic principles to construct an improved Lasso-Logistic regression model. Immediately after that, this paper selects 15 representative indexes from the four aspects of the enterprise’s profitability, solvency, operating ability, and growth ability as the indexes to respond to the company’s financial situation and extracts 4 public factors after factor analysis and analyzes them using the Lasso-Logistic regression model designed in this paper with these 4 public factors as the variables. The results show that the coefficients of public factors F1, F2, F3, and F4 are -2.9513, -1.8347, -1.9659 and -2.2714, respectively, and the coefficients of the four public factors are negative, and the classification accuracy of the Lasso-Logistic combination model in this paper is 89.46%, the misclassification rate of the first category is 6.21%, and the F1 score, RS2 score and AUC values are overall better than the two single models of Lasso and Logistic, the Lasso-Logistic model designed in this paper can well help enterprises assess their own financial risk and make targeted decisions.
</abstract>

In recent years, the assessment of corporate financial risk has become increasingly significant for banks. Therefore, studying models for corporate financial risk assessment holds substantial practical importance. This paper combines the Logistic model and the Lasso model based on their basic principles to construct an improved Lasso-Logistic regression model. Immediately after that, this paper selects 15 representative indexes from the four aspects of the enterprise’s profitability, solvency, operating ability, and growth ability as the indexes to respond to the company’s financial situation and extracts 4 public factors after factor analysis and analyzes them using the Lasso-Logistic regression model designed in this paper with these 4 public factors as the variables. The results show that the coefficients of public factors F1, F2, F3, and F4 are -2.9513, -1.8347, -1.9659 and -2.2714, respectively, and the coefficients of the four public factors are negative, and the classification accuracy of the Lasso-Logistic combination model in this paper is 89.46%, the misclassification rate of the first category is 6.21%, and the F1 score, RS2 score and AUC values are overall better than the two single models of Lasso and Logistic, the Lasso-Logistic model designed in this paper can well help enterprises assess their own financial risk and make targeted decisions.

Research on Corporate Governance and Internal Audit Wisdom Building under Financial Sharing Model Based on Logistic Modeling

Applied Mathematics and Nonlinear Sciences

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

In recent years, the assessment of corporate financial risk has become increasingly significant for banks. Therefore, studying models for corporate financial...

Research on Corporate Governance and Internal Audit Wisdom Building under Financial Sharing Model Based on Logistic Modeling

Published Online: Aug 05, 2024

Page range: -

Received: Apr 09, 2024

Accepted: Jul 03, 2024

DOI: https://doi.org/10.2478/amns-2024-1920

Keywords
Logistic, Lasso, Regression model, Financial risk, Factor analysis

© 2024 Hongli Wu, published by Sciendo

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

Research on Corporate Governance and Internal Audit Wisdom Building under Financial Sharing Model Based on Logistic Modeling

Published Online: Aug 05, 2024

Page range: -

Received: Apr 09, 2024

Accepted: Jul 03, 2024

DOI: https://doi.org/10.2478/amns-2024-1920

KeywordsLogistic, Lasso, Regression model, Financial risk, Factor analysis

© 2024 Hongli Wu, published by Sciendo

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

Keywords
Logistic, Lasso, Regression model, Financial risk, Factor analysis