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The Impact of Incorporating the Cost of Errors into Bankruptcy Prediction Models

Author

Listed:
  • Vedran Capkun

    (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - HEC Paris - Ecole des Hautes Etudes Commerciales - CNRS - Centre National de la Recherche Scientifique)

  • L. A. Weiss

Abstract

The current methodology to evaluate default and bankruptcy prediction models is to determine their precision - the percentage of firms predicted correctly. In this study we develop a framework for incorporating Type I (the amount lost from lending to a firm which goes bankrupt) and Type II (the opportunity cost of not lending to a firm which does not go bankrupt) error costs into the evaluation of prediction models. We then test this new framework by comparing the prediction model with a naive model of lending to all firms in the population based on the net profit each would generate. Our results indicate that prediction models can outperform naive models or other models only under certain conditions. This supports our hypothesis that the usefulness of prediction models cannot be fully assessed independently of the costs of forecast errors.

Suggested Citation

  • Vedran Capkun & L. A. Weiss, 2012. "The Impact of Incorporating the Cost of Errors into Bankruptcy Prediction Models," Working Papers hal-00675309, HAL.
    • Handle: RePEc:hal:wpaper:hal-00675309
    as

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