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Predicting Financial Distress Of Chinese Listed Companies Using Rough Set Theory And Support Vector Machine

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yu Cao & Guangyu Wan & Fuqiang Wang, 2011. "Predicting Financial Distress Of Chinese Listed Companies Using Rough Set Theory And Support Vector Machine," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 28(01), pages 95-109.
    • Handle: RePEc:wsi:apjorx:v:28:y:2011:i:01:n:s0217595911003077
    DOI: 10.1142/S0217595911003077
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    Citations

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    Cited by:

    1. Mattia Iotti & Giuseppe Bonazzi, 2018. "Analysis of the Risk of Bankruptcy of Tomato Processing Companies Operating in the Inter-Regional Interprofessional Organization “OI Pomodoro da Industria Nord Italia”," Sustainability, MDPI, vol. 10(4), pages 1-23, March.
    2. Chen, Li-Fei & Tsai, Chih-Tsung, 2016. "Data mining framework based on rough set theory to improve location selection decisions: A case study of a restaurant chain," Tourism Management, Elsevier, vol. 53(C), pages 197-206.
    3. Jian Luo & Shu-Cherng Fang & Zhibin Deng & Xiaoling Guo, 2016. "Soft Quadratic Surface Support Vector Machine for Binary Classification," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 33(06), pages 1-22, December.

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