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

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...

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Bibliographic Details
Published in:Sustainability 2020-05, Vol.12 (10), p.3954
Main Authors: Gregova, Elena, Valaskova, Katarina, Adamko, Peter, Tumpach, Milos, Jaros, Jaroslav
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Bankruptcy
Economic analysis
Equity
Financial analysis
Learning algorithms
Legislation
Liability
Literature reviews
Model accuracy
Neural networks
Prediction models
Regression analysis
Stability analysis
Statistical analysis
Statistical methods
Sustainability
Transition economies
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su12103954