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Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries

Author

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  • Michal Pavlicko

    (Department of Quantitative Methods and Economic Informatics, The Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, Slovakia)

  • Marek Durica

    (Department of Quantitative Methods and Economic Informatics, The Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, Slovakia)

  • Jaroslav Mazanec

    (Department of Quantitative Methods and Economic Informatics, The Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, Slovakia)

Abstract

The issue of prediction of financial state, or especially the threat of the financial distress of companies, is very topical not only for the management of the companies to take the appropriate actions but also for all the stakeholders to know the financial health of the company and its possible future development. Therefore, the main aim of the paper is ensemble model creation for financial distress prediction. This model is created using the real data on more than 550,000 companies from Central Europe, which were collected from the Amadeus database. The model was trained and validated using 27 selected financial variables from 2016 to predict the financial distress statement in 2017. Five variables were selected as significant predictors in the model: current ratio, return on equity, return on assets, debt ratio, and net working capital. Then, the proposed model performance was evaluated using the values of the variables and the state of the companies in 2017 to predict financial status in 2018. The results demonstrate that the proposed hybrid model created by combining methods, namely RobustBoost, CART, and k-NN with optimised structure, achieves better prediction results than using one of the methods alone. Moreover, the ensemble model is a new technique in the Visegrad Group (V4) compared with other prediction models. The proposed model serves as a one-year-ahead prediction model and can be directly used in the practice of the companies as the universal tool for estimation of the threat of financial distress not only in Central Europe but also in other countries. The value-added of the prediction model is its interpretability and high-performance accuracy.

Suggested Citation

  • Michal Pavlicko & Marek Durica & Jaroslav Mazanec, 2021. "Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries," Mathematics, MDPI, vol. 9(16), pages 1-26, August.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:16:p:1886-:d:610532
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    Cited by:

    1. Sebastian Klaudiusz Tomczak, 2023. "General bankruptcy prediction models for the Visegrád Group. The stability over time," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 33(4), pages 171-187.
    2. Roman Blazek & Pavol Durana & Jakub Michulek, 2023. "Renaissance of Creative Accounting Due to the Pandemic: New Patterns Explored by Correspondence Analysis," Stats, MDPI, vol. 6(1), pages 1-20, March.
    3. Dominika Gajdosikova & Katarina Valaskova & Tomas Kliestik & Maria Kovacova, 2023. "Research on Corporate Indebtedness Determinants: A Case Study of Visegrad Group Countries," Mathematics, MDPI, vol. 11(2), pages 1-30, January.
    4. Michal Pavlicko & Jaroslav Mazanec, 2022. "Minimalistic Logit Model as an Effective Tool for Predicting the Risk of Financial Distress in the Visegrad Group," Mathematics, MDPI, vol. 10(8), pages 1-22, April.

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