Proceedings of the 7th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention

Research on Credit Risk Early-Warning for Listed Companies in Chengyu Economic Zone Based on Best Fuzzy Support Vector Machine

Authors
Kai Xu, Zongfang Zhou
Corresponding Author
Kai Xu
Available Online November 2016.
DOI
https://doi.org/10.2991/rac-16.2016.82How to use a DOI?
Keywords
enterprise credit risk; FSVM; kernel function; Chengyu economic zone; early- warning
Abstract
Taking listed companies in Chengyu Economic Zone as an example, this paper introduces the fuzzy algorithm into support vector machine (SVM), constructing the model of fuzzy support vector machine (FSVM) for Credit risk early-warning, which based on four different kernel functions (linear, polynomial, sigmoid and Gauss radial basis) are compared as well as compared with traditional statistical models and other artificial intelligent models. The result of investigation illustrates that FSVM based on Gauss radial basis kernel function is not only superior to that based on other three kernel functions, but also better significantly than traditional statistical models and other artificial intelligent models.
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Proceedings
7th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC-2016)
Part of series
Advances in Intelligent Systems Research
Publication Date
November 2016
ISBN
978-94-6252-242-8
ISSN
1951-6851
DOI
https://doi.org/10.2991/rac-16.2016.82How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Kai Xu
AU  - Zongfang Zhou
PY  - 2016/11
DA  - 2016/11
TI  - Research on Credit Risk Early-Warning for Listed Companies in Chengyu Economic Zone Based on Best Fuzzy Support Vector Machine
BT  - 7th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC-2016)
PB  - Atlantis Press
SP  - 513
EP  - 518
SN  - 1951-6851
UR  - https://doi.org/10.2991/rac-16.2016.82
DO  - https://doi.org/10.2991/rac-16.2016.82
ID  - Xu2016/11
ER  -