The Integrated Methodology of Rough Set Theory and Support Vector Machine for Credit Risk Assessment

2006 ◽  
Author(s):  
Jianguo Zhou ◽  
Zhaoming Wu ◽  
Chenguang Yang ◽  
Qi Zhao
Keyword(s):  
Risk Assessment ◽  
Support Vector Machine ◽  
Credit Risk ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Support Vector ◽  
Credit Risk Assessment ◽  
Integrated Methodology

PREDICTING FINANCIAL DISTRESS OF CHINESE LISTED COMPANIES USING ROUGH SET THEORY AND SUPPORT VECTOR MACHINE

2011 ◽  
Vol 28 (01) ◽  
pp. 95-109 ◽  
Author(s):  
YU CAO ◽  
GUANGYU WAN ◽  
FUQIANG WANG
Keyword(s):  
Support Vector Machine ◽  
Set Theory ◽  
Rough Set ◽  
Financial Distress ◽  
Rough Set Theory ◽  
Listed Companies ◽  
Support Vector ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Chinese Listed Companies

Effectively predicting corporate financial distress is an important and challenging issue for companies. The research aims at predicting financial distress using the integrated model of rough set theory (RST) and support vector machine (SVM), in order to find a better early warning method and enhance the prediction accuracy. After several comparative experiments with the dataset of Chinese listed companies, rough set theory is proved to be an effective approach for reducing redundant information. Our results indicate that the SVM performs better than the BPNN when they are used for corporate financial distress prediction.

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