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

Author

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  • Elena Gregova

    (Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia)

  • Katarina Valaskova

    (Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia)

  • Peter Adamko

    (Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia)

  • Milos Tumpach

    (Faculty of Economic Informatics, University of Economics in Bratislava, Dolnozemska cesta 1, 852 35 Bratislava, Slovakia)

  • Jaroslav Jaros

    (University Science Park, Center for Technology Transfer, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia)

Abstract

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.

Suggested Citation

  • Elena Gregova & Katarina Valaskova & Peter Adamko & Milos Tumpach & Jaroslav Jaros, 2020. "Predicting Financial Distress of Slovak Enterprises: Comparison of Selected Traditional and Learning Algorithms Methods," Sustainability, MDPI, vol. 12(10), pages 1-17, May.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:10:p:3954-:d:357046
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    3. García, C. José & Herrero, Begoña, 2021. "Female directors, capital structure, and financial distress," Journal of Business Research, Elsevier, vol. 136(C), pages 592-601.
    4. Dawen Yan & Guotai Chi & Kin Keung Lai, 2020. "Financial Distress Prediction and Feature Selection in Multiple Periods by Lassoing Unconstrained Distributed Lag Non-linear Models," Mathematics, MDPI, vol. 8(8), pages 1-27, August.
    5. Yehui Tong & Ramon Saladrigues, 2022. "An analysis of factors affecting the profits of new firms in Spain: Evidence from the food industry," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 68(1), pages 28-38.
    6. Youssef Zizi & Mohamed Oudgou & Abdeslam El Moudden, 2020. "Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach," Risks, MDPI, vol. 8(4), pages 1-21, October.
    7. Katarina Valaskova & Dominika Gajdosikova & Jaroslav Belas, 2023. "Bankruptcy prediction in the post-pandemic period: A case study of Visegrad Group countries," Oeconomia Copernicana, Institute of Economic Research, vol. 14(1), pages 253-293, March.
    8. Alexandra Horobet & Stefania Cristina Curea & Alexandra Smedoiu Popoviciu & Cosmin-Alin Botoroga & Lucian Belascu & Dan Gabriel Dumitrescu, 2021. "Solvency Risk and Corporate Performance: A Case Study on European Retailers," JRFM, MDPI, vol. 14(11), pages 1-34, November.
    9. Minhas Akbar & Ammar Hussain & Marcela Sokolova & Tanazza Sabahat, 2022. "Financial Distress, Firm Life Cycle, and Corporate Restructuring Decisions: Evidence from Pakistan’s Economy," Economies, MDPI, vol. 10(7), pages 1-12, July.
    10. Piotr Staszkiewicz & Aleksander Werner, 2021. "Reporting and Disclosure of Investments in Sustainable Development," Sustainability, MDPI, vol. 13(2), pages 1-15, January.
    11. Zhichao Luo & Pingyu Hsu & Ni Xu, 2020. "SME Default Prediction Framework with the Effective Use of External Public Credit Data," Sustainability, MDPI, vol. 12(18), pages 1-18, September.

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