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

Yu Cao, Guangyu Wan () and Fuqiang Wang ()
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Yu Cao: School of Business, Central South University, Changsha 410083, P. R. China
Guangyu Wan: School of Business, Central South University, Changsha 410083, P. R. China
Fuqiang Wang: School of Business, Central South University, Changsha 410083, P. R. China

Asia-Pacific Journal of Operational Research (APJOR), 2011, vol. 28, issue 01, 95-109

Abstract: 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.

Keywords: Financial distress; support vector machine; rough set theory; prediction; Chinese listed companies (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (3)

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DOI: 10.1142/S0217595911003077

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