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Business Failure Prediction for Slovak Small and Medium-Sized Companies

Author

Listed:
  • Lucia Svabova

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

  • Lucia Michalkova

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

  • Marek Durica

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

  • Elvira Nica

    (Bucharest Academy of Economic Studies, Faculty of Administration and Public Management, 6 Piata Romana, 010374 Bucharest, Romania)

Abstract

Prediction of the financial difficulties of companies has been dealt with over the last years by scientists and economists worldwide. Several prediction models mostly focused on a particular sector of the national economy, have been created also in Slovakia. The main purpose of this paper is to create new prediction models for small and medium-sized companies in Slovakia, based on real data from the Amadeus database from the years 2016–2018. We created prediction models of financial difficulties of companies for 1 year in advance and also a model for 2 years prediction. These models are based on the combination of two methods, discriminant analysis and logistic regression that belong, among others, to the group of the most commonly used methods to derive prediction models of financial difficulties of the companies. The overall prediction powers of the combined model are 90.6%, 93.8% and 90.4%. The results of this analysis can be used for early prediction of the financial difficulties of the company, that could be very useful for all the stakeholders.

Suggested Citation

  • Lucia Svabova & Lucia Michalkova & Marek Durica & Elvira Nica, 2020. "Business Failure Prediction for Slovak Small and Medium-Sized Companies," Sustainability, MDPI, vol. 12(11), pages 1-14, June.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:11:p:4572-:d:366968
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    References listed on IDEAS

    as
    1. Maria Kovacova & Tomas Kliestik & Katarina Valaskova & Pavol Durana & Zuzana Juhaszova, 2019. "Systematic review of variables applied in bankruptcy prediction models of Visegrad group countries," Oeconomia Copernicana, Institute of Economic Research, vol. 10(4), pages 743-772, December.
    2. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    3. Chunling Li & Khansa Pervaiz & Muhammad Asif Khan & Faheem Ur Rehman & Judit Oláh, 2019. "On the Asymmetries of Sovereign Credit Rating Announcements and Financial Market Development in the European Region," Sustainability, MDPI, vol. 11(23), pages 1-14, November.
    4. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    5. Sami Ben Jabeur & Youssef Fahmi, 2018. "Forecasting financial distress for French firms: a comparative study," Empirical Economics, Springer, vol. 54(3), pages 1173-1186, May.
    6. Zoričák, Martin & Gnip, Peter & Drotár, Peter & Gazda, Vladimír, 2020. "Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets," Economic Modelling, Elsevier, vol. 84(C), pages 165-176.
    7. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    8. Alina Mihaela Dima & Simona Vasilache, 2016. "Credit Risk modeling for Companies Default Prediction using Neural Networks," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 127-143, September.
    9. Błażej Prusak, 2018. "Review of Research into Enterprise Bankruptcy Prediction in Selected Central and Eastern European Countries," IJFS, MDPI, vol. 6(3), pages 1-28, June.
    10. Zhongbo Jing & Yi Fang, 2018. "Predicting US bank failures: A comparison of logit and data mining models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(2), pages 235-256, March.
    11. Ivana Tomas Žiković, 2018. "Challenges in Predicting Financial Distress in Emerging Economies: The Case of Croatia," Eastern European Economics, Taylor & Francis Journals, vol. 56(1), pages 1-27, January.
    12. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    13. Dimitras, A. I. & Zanakis, S. H. & Zopounidis, C., 1996. "A survey of business failures with an emphasis on prediction methods and industrial applications," European Journal of Operational Research, Elsevier, vol. 90(3), pages 487-513, May.
    14. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    15. Chyan-long Jan, 2018. "An Effective Financial Statements Fraud Detection Model for the Sustainable Development of Financial Markets: Evidence from Taiwan," Sustainability, MDPI, vol. 10(2), pages 1-14, February.
    16. Jana Kliestikova & Maria Misankova & Tomas Kliestik, 2017. "Bankruptcy In Slovakia: International Comparison Of The Creditor´S Position," Oeconomia Copernicana, Institute of Economic Research, vol. 8(2), pages 221-237, June.
    17. Mossman, Charles E, et al, 1998. "An Empirical Comparison of Bankruptcy Models," The Financial Review, Eastern Finance Association, vol. 33(2), pages 35-53, May.
    18. Stewart Jones & David Johnstone & Roy Wilson, 2017. "Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 44(1-2), pages 3-34, January.
    19. Maria Kovacova & Tomas Kliestik, 2017. "Logit and Probit application for the prediction of bankruptcy in Slovak companies," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 12(4), pages 775-791, December.
    20. Tomas Kliestik & Jaromir Vrbka & Zuzana Rowland, 2018. "Bankruptcy prediction in Visegrad group countries using multiple discriminant analysis," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 13(3), pages 569-593, September.
    21. Edward I. Altman & Małgorzata Iwanicz-Drozdowska & Erkki K. Laitinen & Arto Suvas, 2020. "A Race for Long Horizon Bankruptcy Prediction," Applied Economics, Taylor & Francis Journals, vol. 52(37), pages 4092-4111, July.
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    Cited by:

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    6. 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.
    7. Mattia Iotti & Giuseppe Bonazzi, 2023. "Financial Sustainability in Agri-Food Companies: The Case of Members of the PDO Parma Ham Consortium," Sustainability, MDPI, vol. 15(5), pages 1-31, February.
    8. 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.
    9. Giuseppe Bonazzi & Paolo Camanzi & Giovanni Ferri & Elisa Manghi & Mattia Iotti, 2021. "Economic Sustainability of Pig Slaughtering Firms in the Production Chain of Denomination of Origin Hams in Italy," Sustainability, MDPI, vol. 13(14), pages 1-18, July.

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