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Forecasting business failure in China using case-based reasoning with hybrid case respresentation

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  • Hui Li

    (School of Business Administration, Zhejiang Normal University, PR China)

  • Jie Sun

    (School of Business Administration, Zhejiang Normal University, PR China)

Abstract

Case-based reasoning (CBR) is a very effective and easily understandable method for solving real-world problems. Business failure prediction (BFP) is a forecasting tool that helps people make more precise decisions. CBR-based BFP is a hot topic in today's global financial crisis. Case representation is critical when forecasting business failure with CBR. This research describes a pioneer investigation on hybrid case representation by employing principal component analysis (PCA), a feature extraction method, along with stepwise multivariate discriminant analysis (MDA), a feature selection approach. In this process, sample cases are represented with all available financial ratios, i.e., features. Next, the stepwise MDA is used to select optimal features to produce a reduced-case representation. Finally, PCA is employed to extract the final information representing the sample cases. All data signified by hybrid case representation are recorded in a case library, and the k -nearest-neighbor algorithm is used to make the forecasting. Thus we constructed a hybrid CBR (HCBR) by integrating hybrid case representation into the forecasting tool. We empirically tested the performance of HCBR with data collected for short-term BFP of Chinese listed companies. Empirical results indicated that HCBR can produce more promising prediction performance than MDA, logistic regression, classical CBR, and support vector machine. Copyright © 2009 John Wiley & Sons, Ltd.

Suggested Citation

  • Hui Li & Jie Sun, 2010. "Forecasting business failure in China using case-based reasoning with hybrid case respresentation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(5), pages 486-501.
  • Handle: RePEc:jof:jforec:v:29:y:2010:i:5:p:486-501
    DOI: 10.1002/for.1149
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    References listed on IDEAS

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    1. Sumit Sarkar & Ram S. Sriram, 2001. "Bayesian Models for Early Warning of Bank Failures," Management Science, INFORMS, vol. 47(11), pages 1457-1475, November.
    2. Sun, Lili & Shenoy, Prakash P., 2007. "Using Bayesian networks for bankruptcy prediction: Some methodological issues," European Journal of Operational Research, Elsevier, vol. 180(2), pages 738-753, July.
    3. Premachandra, I.M. & Bhabra, Gurmeet Singh & Sueyoshi, Toshiyuki, 2009. "DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique," European Journal of Operational Research, Elsevier, vol. 193(2), pages 412-424, March.
    4. Yang, Z. R. & Platt, Marjorie B. & Platt, Harlan D., 1999. "Probabilistic Neural Networks in Bankruptcy Prediction," Journal of Business Research, Elsevier, vol. 44(2), pages 67-74, February.
    5. Dimitras, A. I. & Slowinski, R. & Susmaga, R. & Zopounidis, C., 1999. "Business failure prediction using rough sets," European Journal of Operational Research, Elsevier, vol. 114(2), pages 263-280, April.
    6. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    7. Thomas E. McKee, 2003. "Rough sets bankruptcy prediction models versus auditor signalling rates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(8), pages 569-586.
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    Cited by:

    1. repec:eee:touman:v:33:y:2012:i:3:p:622-634 is not listed on IDEAS
    2. Amankwah-Amoah, Joseph & Zhang, Hongxu, 2015. "Business failure research," MPRA Paper 67848, University Library of Munich, Germany.
    3. Li, Hui & Hong, Lu-Yao & He, Jia-Xun & Xu, Xuan-Guo & Sun, Jie, 2013. "Small sample-oriented case-based kernel predictive modeling and its economic forecasting applications under n-splits-k-times hold-out assessment," Economic Modelling, Elsevier, vol. 33(C), pages 747-761.

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