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

<p>From the generation of defective capital contributions to the transfer of transactions, a series of civil disputes and liability responsibilities still need to be explored for a better solution. In this paper, based on the current research status of the defective capital contribution assessment model of enterprise shareholders, we combine the XG Boost model with good classification ability and the logistic regression model with good interpretability and construct a discrete regression (XG Boost-Logistic) model for the evaluation of the defective capital contribution of enterprises. Combined with the data of 123 shareholders’ capital contributions from enterprises’ financial audit reports, the XG Boost discrete model, the Logistic regression model, and the XG Boost-Logistic evaluation combination model were used to conduct empirical analysis and comparative experimental analysis with the evaluation indexes of the model. The research results show that the accuracy rate based on the XG Boost-Logistic evaluation combination model is 87.39%; the efficiency of liability assessment is improved by 7.35% compared with the XG Boost model and 12.38% compared with the Logistic regression model. XG Boost-Logistic evaluation combination model can effectively improve the liability prediction of capital contribution defects classification accuracy and provide a good explanation of shareholder liability classification at the same time and can help companies to avoid financial risks plays a key role.</p>
</abstract>

From the generation of defective capital contributions to the transfer of transactions, a series of civil disputes and liability responsibilities still need to be explored for a better solution. In this paper, based on the current research status of the defective capital contribution assessment model of enterprise shareholders, we combine the XG Boost model with good classification ability and the logistic regression model with good interpretability and construct a discrete regression (XG Boost-Logistic) model for the evaluation of the defective capital contribution of enterprises. Combined with the data of 123 shareholders’ capital contributions from enterprises’ financial audit reports, the XG Boost discrete model, the Logistic regression model, and the XG Boost-Logistic evaluation combination model were used to conduct empirical analysis and comparative experimental analysis with the evaluation indexes of the model. The research results show that the accuracy rate based on the XG Boost-Logistic evaluation combination model is 87.39%; the efficiency of liability assessment is improved by 7.35% compared with the XG Boost model and 12.38% compared with the Logistic regression model. XG Boost-Logistic evaluation combination model can effectively improve the liability prediction of capital contribution defects classification accuracy and provide a good explanation of shareholder liability classification at the same time and can help companies to avoid financial risks plays a key role.

Analysis of Liability for Defective Capital Contribution of Company Shareholders Based on Discrete Regression Algorithm

Applied Mathematics and Nonlinear Sciences

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

From the generation of defective capital contributions to the transfer of transactions, a series of civil disputes and liability responsibilities still need to...

Analysis of Liability for Defective Capital Contribution of Company Shareholders Based on Discrete Regression Algorithm

Data publikacji: 19 lip 2023

Zakres stron: -

Otrzymano: 19 kwi 2022

Przyjęty: 13 lis 2022

DOI: https://doi.org/10.2478/amns.2023.2.00062

Słowa kluczowe
Defective capital contribution, Civil dispute, XG Boost, Logistic, financial risk

© 2023 Weixia Wang, published by Sciendo

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

Analysis of Liability for Defective Capital Contribution of Company Shareholders Based on Discrete Regression Algorithm

Data publikacji: 19 lip 2023

Zakres stron: -

Otrzymano: 19 kwi 2022

Przyjęty: 13 lis 2022

DOI: https://doi.org/10.2478/amns.2023.2.00062

Słowa kluczoweDefective capital contribution, Civil dispute, XG Boost, Logistic, financial risk

© 2023 Weixia Wang, published by Sciendo

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

Słowa kluczowe
Defective capital contribution, Civil dispute, XG Boost, Logistic, financial risk