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Comparative analysis of failure prediction methods: the Finnish case

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Author Info
Teija Laitinen, Maria Kankaanpaa
Abstract

This paper first briefly discusses six alternative methods that have been applied to financial failure prediction: linear discriminant analysis, logit analysis, recursive partitioning, survival analysis, neural networks and the human information processing approach. The main objective was to study empirically whether the results stemming from the use of alternative methods differ from each other. This was conducted using the Finnish data one, two and three years prior to failure in empirical analysis. The results indicated that there was a statistically significant difference in prediction accuracy only between logistic analysis and survival analysis one year prior to failure. Two and three years prior to failure statistically significant differences were not found. The results indicate, with the three variables employed in this study, that no superior method has been found. Even one of the latest applications, neural networks, is in its present form only as effective as discriminant analysis was as early as thirty years ago.

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Article provided by Taylor and Francis Journals in its journal European Accounting Review.

Volume (Year): 8 (1999)
Issue (Month): 1 (May)
Pages: 67-92
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Handle: RePEc:taf:euract:v:8:y:1999:i:1:p:67-92

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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.:

  1. Hamer, Michelle M., 1983. "Failure prediction: Sensitivity of classification accuracy to alternative statistical methods and variable sets," Journal of Accounting and Public Policy, Elsevier, vol. 2(4), pages 289-307. [Downloadable!] (restricted)
  2. Tam, KY, 1991. "Neural network models and the prediction of bank bankruptcy," Omega, Elsevier, vol. 19(5), pages 429-445. [Downloadable!] (restricted)
  3. Luoma, M & Laitinen, EK, 1991. "Survival analysis as a tool for company failure prediction," Omega, Elsevier, vol. 19(6), pages 673-678. [Downloadable!] (restricted)
  4. Frydman, Halina & Altman, Edward I & Kao, Duen-Li, 1985. " Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress," Journal of Finance, American Finance Association, vol. 40(1), pages 269-91, March. [Downloadable!] (restricted)
  5. Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November. [Downloadable!] (restricted)
  6. Peel, MJ & Peel, DA & Pope, PF, 1986. "Predicting corporate failure-- Some results for the UK corporate sector," Omega, Elsevier, vol. 14(1), pages 5-12. [Downloadable!] (restricted)
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  1. Balcaen S. & Ooghe H., 2004. "Alternative methodologies in studies on business failure: do they produce better results than the classic statistical methods?," Vlerick Leuven Gent Management School Working Paper Series 2004-16, Vlerick Leuven Gent Management School. [Downloadable!]
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  2. Aaro Hazak & Kadri Männasoo, 2007. "Indicators of corporate default : an EU based empirical study," Bank of Estonia Working Papers 2007-10, Bank of Estonia, revised 04 Sep 2007. [Downloadable!]
  3. Constantin Zopounidis, Michael Doumpos, Fotios Pasiouras, 2007. "Multicriteria Framework for the Prediction of Corporate Failure in the UK," Frontiers in Finance and Economics, Lille Graduate School of Management, vol. 4(1), pages 65-90, June. [Downloadable!]
  4. Hubert Ooghe & Sofie Balcaen, 2002. "Are failure prediction models transferable from one country to another? An empirical study using financial statements," Vlerick Leuven Gent Management School Working Paper Series 2002-3, Vlerick Leuven Gent Management School. [Downloadable!]
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