Are Failure Prediction Models Widely Usable? An Empirical Study Using a Belgian Dataset
44 Pages Posted: 26 Jun 2015
Date Written: June 26, 2015
Faced with the question as to whether failure prediction models can easily be transferred and applied to a new data setting, this study examines the performance of seven models on a dataset of Belgian company failures after re-estimation of the coefficients. The validation results indicate that some models are widely usable: they are strongly predictive when applied to the new data set. The Gloubos-Grammatikos models and Keasey-McGuinness appear among the best performing models, and also Ooghe-Joos-De Vos and Zavgren seem to be widely usable, respectively for failure prediction 1 and 3 years prior to failure. At the same time, the Altman and Bilderbeek models show very poor results when applied to the Belgian dataset.
Keywords: failure prediction model; international comparison; validation; annual accounts; re-estimation
JEL Classification: G33, M49
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