Implementation of standards into predictors of financial stability

Abstract

This article proposes a methodology for modifying bankruptcy models. The authors focused on the role of partial predictors in predicting companýs bankruptcy. According to the authors, it is necessary to analyse the anomalies that occur in companies that are losing financial stability. It has been hypothesized that some anomalies in the form of extreme values ​​may deviate the overall final value of the model to such an extent that they lead to an erroneous evaluation of the company by the bankruptcy model. As a result, companies in bankruptcy may be incorrectly classified as 'financially stable' or, conversely, financially sound companies with certain extreme values ​​could be mistaken for companies in bankruptcy. To verify the hypothesis, the authors analysed the probability distribution of partial predictors of the bankruptcy Model 1 on a test and verification sample of more than 1,100 companies. Limits were set to eliminate extreme values ​​and, as a result, the accuracy of the model has been increased. The introduction of limits has increased the accuracy of the model, especially in ​​bankruptcy prediction. For bankruptcies, the accuracy increased from 85.82% to 88.65% for the test sample and from 84.00% to 88.00% for the verification sample.