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Comparative analysis of failure prediction methods: the Finnish case

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  • 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.

Suggested Citation

  • Teija Laitinen & Maria Kankaanpaa, 1999. "Comparative analysis of failure prediction methods: the Finnish case," European Accounting Review, Taylor & Francis Journals, vol. 8(1), pages 67-92.
    • Handle: RePEc:taf:euract:v:8:y:1999:i:1:p:67-92
    DOI: 10.1080/096381899336159
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