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Article

Predicting Financial Distress of Slovak Enterprises: Comparison of Selected Traditional and Learning Algorithms Methods

1
Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia
2
Faculty of Economic Informatics, University of Economics in Bratislava, Dolnozemska cesta 1, 852 35 Bratislava, Slovakia
3
University Science Park, Center for Technology Transfer, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(10), 3954; https://doi.org/10.3390/su12103954
Received: 26 March 2020 / Revised: 8 May 2020 / Accepted: 9 May 2020 / Published: 12 May 2020
(This article belongs to the Special Issue Company Assessment: Basis of Its Sustainable Development)
Predicting the risk of financial distress of enterprises is an inseparable part of financial-economic analysis, helping investors and creditors reveal the performance stability of any enterprise. The acceptance of national conditions, proper use of financial predictors and statistical methods enable achieving relevant results and predicting the future development of enterprises as accurately as possible. The aim of the paper is to compare models developed by using three different methods (logistic regression, random forest and neural network models) in order to identify a model with the highest predictive accuracy of financial distress when it comes to industrial enterprises operating in the specific Slovak environment. The results indicate that all models demonstrated high discrimination accuracy and similar performance; neural network models yielded better results measured by all performance characteristics. The outputs of the comparison may contribute to the development of a reputable prediction model for industrial enterprises, which has not been developed yet in the country, which is one of the world’s largest car producers. View Full-Text
Keywords: default; enterprise in crisis; bankruptcy; financial distress; prediction models default; enterprise in crisis; bankruptcy; financial distress; prediction models
MDPI and ACS Style

Gregova, E.; Valaskova, K.; Adamko, P.; Tumpach, M.; Jaros, J. Predicting Financial Distress of Slovak Enterprises: Comparison of Selected Traditional and Learning Algorithms Methods. Sustainability 2020, 12, 3954. https://doi.org/10.3390/su12103954

AMA Style

Gregova E, Valaskova K, Adamko P, Tumpach M, Jaros J. Predicting Financial Distress of Slovak Enterprises: Comparison of Selected Traditional and Learning Algorithms Methods. Sustainability. 2020; 12(10):3954. https://doi.org/10.3390/su12103954

Chicago/Turabian Style

Gregova, Elena, Katarina Valaskova, Peter Adamko, Milos Tumpach, and Jaroslav Jaros. 2020. "Predicting Financial Distress of Slovak Enterprises: Comparison of Selected Traditional and Learning Algorithms Methods" Sustainability 12, no. 10: 3954. https://doi.org/10.3390/su12103954

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