Forecasting business failure in China using case-based reasoning with hybrid case respresentation
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.
Volume (Year): 29 (2010)
Issue (Month): 5 ()
|Contact details of provider:|| Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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, 09.
- Sumit Sarkar & Ram S. Sriram, 2001. "Bayesian Models for Early Warning of Bank Failures," Management Science, INFORMS, vol. 47(11), pages 1457-1475, November.
- 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.
- 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.
- 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.
- 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.
When requesting a correction, please mention this item's handle: RePEc:jof:jforec:v:29:y:2010:i:5:p:486-501. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing)or (Christopher F. Baum)
If references are entirely missing, you can add them using this form.