PREDICTION OF THE BANKRUPTCY OF SLOVAK COMPANIES USING NEURAL NETWORKS WITH SMOTE

• Authors: Tumpach Miloš , Surovičová Adriana , Juhászová Zuzana , Marci Anton , Kubaščíková Zuzana
• Languages of publication: EN

Abstracts

EN

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.

Keywords

EN

ARTIFICIAL NEURAL NETWORKS; BANKRUPTCY MODEL; OVERSAMPLING; SMOTE

• Publisher: Institute of Slovak and World Economics and Institute for Forecasting, Slovak Academy of Sciences
• Journal: Ekonomický časopis (Journal of Economics)
• Year: 2020
• Volume: 68
• Issue: 10
• Pages: 1021 – 1039

Contributors

author

Tumpach Miloš

• University of Economics in Bratislava, Faculty of Economic Informatics, Dolnozemská cesta 1, 852 35 Bratislava, Slovak Republic

author

Surovičová Adriana

author

Juhászová Zuzana

author

Marci Anton

author

Kubaščíková Zuzana

Identifiers

DOI

https://doi.org/10.31577/ekoncas.2020.10.03