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The comparison of enterprise bankruptcy forecasting method

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  • Xu Xiaosi
  • Chen Ying
  • Zheng Haitao

Abstract

The enterprise bankruptcy forecasting is vital to manage credit risk, which can be solved through classifying method. There are three typical classifying methods to forecast enterprise bankruptcy: the statistics method, the Artificial Neural Network method and the kernel-based learning method. The paper introduces the first two methods briefly, and then introduces Support Vector Machine (SVM) of the kernel-based learning method, and lastly compares the bankruptcy forecasting accuracies of the three methods by building the corresponding models with the data of China's stock exchange data. From the positive analysis, we can draw a conclusion that the SVM method has a higher adaptability and precision to forecast enterprise bankruptcy.

Suggested Citation

  • Xu Xiaosi & Chen Ying & Zheng Haitao, 2011. "The comparison of enterprise bankruptcy forecasting method," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(2), pages 301-308, September.
    • Handle: RePEc:taf:japsta:v:38:y:2011:i:2:p:301-308
    DOI: 10.1080/02664760903406470
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    References listed on IDEAS

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    1. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    2. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
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    Cited by:

    1. Rong Guan & Huiwen Wang & Haitao Zheng, 2020. "Improving accuracy of financial distress prediction by considering volatility: an interval-data-based discriminant model," Computational Statistics, Springer, vol. 35(2), pages 491-514, June.

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