Abstract:

The development of a bankruptcy prediction model, i.e. a model capable of identifying in advance companies threatened by bankruptcy with a high degree of accuracy, is a difficult process in view of the absence of data on companies that have gone into bankruptcy. This leads to the use of models that have already been developed, for which their authors have declared a high degree of prediction accuracy. The subject of the research presented in this paper is the testing of the accuracy of such models in a period or environment other than that for which they were designed. The ability of these models to differentiate between companies threatened by bankruptcy and prospering companies and their identification error, i.e. indicating companies that go into bankruptcy as prospering companies and vice versa, were tested. Testing was performed on data on companies in the manufacturing industry operating in the Czech Republic in the years 1999–2013 with the use of three models. The first of the models used is the Altman model, which originated in a different environment and in a different period of time to the tested companies. The second model tested was the Neumaier model, which originated in a similar environment, though at a different time. The last of the models tested is a model developed by the authors of this paper, which originated on the basis of data from the same field, though in a different period. The use of these models over a long time period also enabled testing of the predication capability of the individual models in advance, i.e. one to nine years before bankruptcy. The results of this research clearly demonstrate that the accuracy of such models falls markedly when used in a different environment and different time period. The best results were obtained by the use of a model created with data on the same field, though in a different period of time.