Abstract
Using large amounts of data from small and medium-sized industrial firms, this study examines two aspects of bankruptcy prediction: the influence of the year prior to failure selected for model building and the effects in a period of economic decline. The results show that especially models generated from the final annual report published prior to bankruptcy were less successful in the timely prediction of failure. Furthermore, it was found that economic decline coincided with the deterioration of a model's performance. With respect to the methods used, we found that neural networks had a somewhat better overall performance than multiple discriminant analysis. Copyright © 2005 John Wiley & Sons, Ltd.
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