Enterprise Credit Rating Method Based on Stochastic Dominance Under Linguistic Distribution Assessments Context
- DOI
- 10.2991/978-94-6463-256-9_32How to use a DOI?
- Keywords
- Credit rating; Consensus reaching; Linguistic distribution assessment; Stochastic dominance
- Abstract
In the process of enterprise risk management, credit rating is an important and effective method, which has been widely used in many fields. However, current credit rating methods rarely consider linguistic distribution assessment, which is often given by many experts. Inspired by this, in this paper, we developed a corporate credit rating method based on the stochastic dominance theory in the context of linguistic distribution assessment. In this method, the stochastic dominance theory and the minimum adjustment model are combined to establish a minimum adjustment cost model to achieve consensus in the process of credit rating. Then, we propose a dominance method to calculate the dominance degree of the distribution evaluation of any two languages, and then determine the ranking results of enterprises.
- Copyright
- © 2024 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
TY - CONF AU - Hui Hu AU - Haiming Liang PY - 2023 DA - 2023/10/09 TI - Enterprise Credit Rating Method Based on Stochastic Dominance Under Linguistic Distribution Assessments Context BT - Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023) PB - Atlantis Press SP - 302 EP - 308 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-256-9_32 DO - 10.2991/978-94-6463-256-9_32 ID - Hu2023 ER -