The Evaluation of Enterprise Financing Structure Capability Based on RF-CART Integrated Algorithm

Authors

  • Zhaofen Meng School of Accounting, Yanshan College Shandong University of Finance and Economics, Ji’nan, 250000, China
  • Min Liu School ofAccounting, Yanshan College Shandong University of Finance and Economics, Ji’nan, 250000, China
  • Bingjie Li School of International Business,Yanshan College Shandong University of Finance and Economics, Ji’nan, 250000, China

Abstract

The continuous advancement of information technology and the expansion of application scenarios have highlighted the advantages of data fusion and personalized evaluation of computer technology, which has great potential for application in the financing field of small and medium-sized enterprises. Based on the current information asymmetry and limitations of subjective evaluation, an evaluation model to determine the capability of a financing structure is proposed, based on the RF-CART integrated algorithm. Firstly, the impact indicators of financing structure for small and medium-sized enterprises were collected, a principal component analysis was conducted, and the characteristic variables for the indicators were constructed. Secondly, the CART decision tree was used as a weak learner to construct a credit evaluation model under the RF model, and the classification results processed by multiple decision trees were integrated to obtain the optimal classification result. By using indicator testing and
cross validation to analyze the effectiveness of the model algorithm, it was found that there is a negative correlation between the tax effect and financing capacity of small and medium-sized enterprises. The testing and training time of ensemble learning algorithms are both less than 21 seconds, with an average recognition accuracy of over 95%. The accuracy difference of other comparative calculation methods is 4%, and their AUC area for recognition performance is 0.915, indicating good sample discrimination ability. The proposed method can effectively determine the operational status of enterprises and provide indicators and warnings related to financing risks and any decisions pertaining to growth.

Keywords: integrated learning; small and medium-sized enterprises; financing structure; KS; random forest algorithm

Cite As

Z. Meng, M. Liu, B. Li, " The Evaluation of Enterprise Financing Structure Capability Based on RF-CART Integrated Algorithm", Engineering Intelligent Systems, vol. 33 no. 2, pp. 213-221, 2025.






Downloads

Published

2025-03-01

Issue

Vol. 33 No. 2 (2025): International Journal of Engineering Intelligent Systems

Section

Article

License

The submission of a paper implies that, if accepted for publication, it will not be published elsewhere in the same form, in any language, without the prior consent of the publisher. Before publication, authors are requested to assign copyright to CRL Publishing Ltd. This allows CRL to sanction photocopying, and to authorize the reprinting of issues or volumes according to demand. Authors' traditional rights will not be jeopardized by assigning Copyright in this way, as they retain the right to reuse the material following publication, and to veto third-party publication.