Prediction of the bankruptcy of slovak companies using neural networks with SMOTE

Abstract

Although the bankruptcy prediction models can be a stabilizing element on both macro and microeconomic levels, they are rather a domain of academic research than an instrument, widely applied in a business practice. It is especially true if the models are reflecting the conditions of countries of their origin, rather than countries of their intended uses. Besides, few of the models contain inherent flaws, including the absence of a methodical approach addressing this problem of the severely imbalanced representation of bankrupt companies in financial datasets. The article is focused on the use of oversampling with SMOTE (Syn-thetic Minority Oversampling Technique) algorithm under the condition of extremely imbalanced data sets of Slovak companies. While the model does not provide a single answer in many (if not most) of the situations, it still could be used for the selection of companies for which the more detailed (and expensive) analysis is not required.