Proceedings of the 2026 2nd International Conference on Financial Innovation and Marketing Management (FIMM 2026)

2026 2nd International Conference on Financial Innovation and Marketing Management (FIMM 2026)

📍Singapore, Singapore🗓️ 2-4 July 2026
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The Application of the Logistic Regression Model in Credit Risk Assessment of Listed Companies

Authors
Kexuan Xu1, *
1School of Mathematics and Physics, Xi’an Jiaotong-Liverpool University, 215123, Suzhou, China
*Corresponding author. Email: Kexuan.Xu24@student.xjtlu.edu.cn
Corresponding Author
Kexuan Xu
Available Online 23 October 2026.
DOI
10.2991/978-94-6239-801-6_129How to use a DOI?
Keywords
Credit Risk; Logistic Regression Model; Financial Indicator
Abstract

Nowadays, since the capital market is expanding increasingly, more and more listed companies are coming to show their promise. However, the number of their credit risk exposures has surged; this phenomenon not only influences the reputation and financial performance of these listed companies but also affects the stability and credibility of the whole trading market. This article used a logistic regression model as a tool to conduct research about listed companies’ credit risk assessment. This model is suitable for binary classification evaluation of credit risk. The study results show that the ratios of asset liability, return on equity, and total asset turnover are the most influential factors for the companies’ credit risk. Among these indicators, the asset liability ratio is directly proportional to risk, while the ratios of return on equity and total asset turnover are inversely proportional to risk. The logistic regression model built in this article can offer more proper and trustworthy suggestions for loan approval of a financial institution and investment in an enterprise or personally. By using this model, it can discover and avoid problems easily, which makes up for the objective and subjective disadvantages of traditional risk assessment.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2026 2nd International Conference on Financial Innovation and Marketing Management (FIMM 2026)
Series
Advances in Economics, Business and Management Research
Publication Date
23 October 2026
ISBN
978-94-6239-801-6
ISSN
2352-5428
DOI
10.2991/978-94-6239-801-6_129How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Kexuan Xu
PY  - 2026
DA  - 2026/10/23
TI  - The Application of the Logistic Regression Model in Credit Risk Assessment of Listed Companies
BT  - Proceedings of the 2026 2nd International Conference on Financial Innovation and Marketing Management (FIMM 2026)
PB  - Atlantis Press
SP  - 1247
EP  - 1254
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-801-6_129
DO  - 10.2991/978-94-6239-801-6_129
ID  - Xu2026
ER  -