Predicting bankruptcy under alternative conditions: the effect of a change in industry and time period on the accuracy of the model

Abstract

According to literature bankruptcy prediction models are less accurate if applied in under alternative conditions. In our previous research we created our own bankruptcy prediction model. When creating the model we tried to applicate an approach different to previous ones. For creating the model we used the traditional method of linear discrimination analysis, but we employed only transformed variables with approximately normal distribution. What is more, the variables pairs are mostly negatively correlated. According to literature such factors should positively influence the model accuracy. However there is a very limited literacy how such application affects the stability of model’s accuracy. The aim of this paper is to analyse the stability of model’s accuracy in application in different time period or different line of business. Moreover, we aim to examine and discuss the effectiveness of the procedure which was used to create the model.

According to literature bankruptcy prediction models are less accurate if applied in under alternative conditions. In our previous research we created our own bankruptcy prediction model. When creating the model we tried to applicate an approach different to previous ones. For creating the model we used the traditional method of linear discrimination analysis, but we employed only transformed variables with approximately normal distribution. What is more, the variables pairs are mostly negatively correlated. According to literature such factors should positively influence the model accuracy. However there is a very limited literacy how such application affects the stability of model’s accuracy. The aim of this paper is to analyse the stability of model’s accuracy in application in different time period or different line of business. Moreover, we aim to examine and discuss the effectiveness of the procedure which was used to create the model.