This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Comparative analysis of failure prediction methods: the Finnish case

Author info | Abstract | Publisher info | Download info | Related research | Statistics
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.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://taylorandfrancis.metapress.com/link.asp?target=contribution&id=GJWLRQYX4KNRY7H1
File Format: text/html
File Function:
Download Restriction: Access to full text is restricted to subscribers.

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

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

Volume (Year): 8 (1999)
Issue (Month): 1 (May)
Pages: 67-92
Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Handle: RePEc:taf:euract:v:8:y:1999:i:1:p:67-92

Contact details of provider:
Web page: http://taylorandfrancis.metapress.com/link.asp?target=journal&id=100166

Order Information:
Web: http://www.tandf.co.uk/journals/subscription.html

For technical questions regarding this item, or to correct its listing, contact: (Christopher F. Baum).

Related research
Keywords:

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.:

  1. Edmister, Robert O., 1972. "An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 7(02), pages 1477-1493, March. [Downloadable!]
  2. Tam, KY, 1991. "Neural network models and the prediction of bank bankruptcy," Omega, Elsevier, vol. 19(5), pages 429-445. [Downloadable!] (restricted)
  3. 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)
  4. 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)
  5. Luoma, M & Laitinen, EK, 1991. "Survival analysis as a tool for company failure prediction," Omega, Elsevier, vol. 19(6), pages 673-678. [Downloadable!] (restricted)
  6. 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)
  7. 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)
Full references

Cited by:
(explanations, 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. 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!]
    Other versions:
  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!]
Statistics
Access and download statistics

Did you know? A few items listed on IDEAS are over 2000 years old!

This page was last updated on 2009-11-25.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.