Prediction of the Bankruptcy of Slovak Companies Using Neural Networks with SMOTE
Prediction of the Bankruptcy of Slovak Companies Using Neural Networks with SMOTE
Author(s): Miloš Tumpach, Adriana Surovičová, Zuzana Juhászová, Anton Marci, Zuzana KubaščíkováSubject(s): Micro-Economics, Policy, planning, forecast and speculation, Accounting - Business Administration, ICT Information and Communications Technologies
Published by: Ekonomický ústav SAV a Prognostický ústav SAV
Keywords: artificial neural networks; bankruptcy model; oversampling; SMOTE; imbalanced data;
Summary/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 (Synthetic 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.
Journal: Ekonomický časopis
- Issue Year: 68/2020
- Issue No: 10
- Page Range: 1021-1039
- Page Count: 19
- Language: English
