Retail default prediction by using sequential minimal optimization technique
This paper employed sequential minimal optimization (SMO) to develop default prediction model in the US retail market. Principal components analysis is used for variable reduction purposes. Four standard credit scoring techniques-naïve Bayes, logistic regression, recursive partitioning and artificial neural network-are compared to SMO, using a sample of 195 healthy firms and 51 distressed firms over five time periods between 1994 and 2002. The five techniques perform well in predicting default particularly one year before financial distress. Furthermore, the prediction still remains sound even 5 years before default. No single methodology has the absolute best classification ability, as the model performance varies in terms of different time periods and variable groups. External influences have greater impacts on the naïve Bayes than other techniques. In terms of similarity with Moody's ranking, SMO excelled over other techniques in most of the time periods. Copyright © 2008 John Wiley & Sons, Ltd.
Volume (Year): 28 (2009)
Issue (Month): 8 ()
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- 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.
- Hu, Yu-Chiang & Ansell, Jake, 2007. "Measuring retail company performance using credit scoring techniques," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1595-1606, December.
- repec:bla:joares:v:18:y:1980:i:1:p:109-131 is not listed on IDEAS
- Cecilio Mar-Molinero & Carlos Serrano-Cinca, 2001. "Bank failure: a multidimensional scaling approach," The European Journal of Finance, Taylor & Francis Journals, vol. 7(2), pages 165-183.
- repec:bla:joares:v:22:y:1984:i::p:87-114 is not listed on IDEAS
- Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
- repec:bla:joares:v:4:y:1966:i::p:71-111 is not listed on IDEAS
- 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.
- Sanger, Gary C. & McConnell, John J., 1986. "Stock Exchange Listings, Firm Value, and Security Market Efficiency: The Impact of NASDAQ," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 21(01), pages 1-25, March.
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