The Bankruptcy Forecasting Model of Hungarian Enterprises

Abstract

The SME sector is really important for the Hungarian economy. In our analysis, we had a closer look at the publicly accessible version of Altman's Z-score bankruptcy forecast model for companies not quoted on the Stock Exchange together with the original and the modified, adjusted Springate bankruptcy prediction model. The adjusted Springate model regarded only 37% of the companies having gone bankrupt in real as insolvent, while the justified Altman Z-score model was able to identify only 46% of the stable ongoing firms. The variance analysis could not detect any correlations between the phenomenon of bankruptcy and financial types. By means of logistic regression, we managed to create a model that can forecast solvency for the examined enterprises with a probability of 78%. In the last part of our research, we were dealing with teaching artificial intelligence and creating decision trees based on neural network. Even by means of the first bankruptcy forecast model based on decision trees, a more efficient predicting system was gained than by using any other methods. We assume that only the decision tree made up by using artificial intelligence is efficient in forecasting bankruptcy of all the examined models.