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Bankruptcy Prediction Model Development and its Implications on Financial Performance in Slovakia

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  • Gajdosikova Dominika

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

  • Valaskova Katarina

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

Abstract

Research purpose. Financial distress being a global phenomenon makes it impact firms in all sectors of the economy and predicting corporate bankruptcy has become a crucial issue in economics. At the beginning of the last century, the first studies aimed to predict corporate bankruptcy were published. In Slovakia, however, several prediction models were developed with a significant delay. The main aim of this paper is to develop a model for predicting bankruptcy based on the financial information of 3,783 Slovak enterprises operating in the manufacturing and construction sectors in 2020 and 2021.

Suggested Citation

  • Gajdosikova Dominika & Valaskova Katarina, 2023. "Bankruptcy Prediction Model Development and its Implications on Financial Performance in Slovakia," Economics and Culture, Sciendo, vol. 20(1), pages 30-42, June.
  • Handle: RePEc:vrs:ecocul:v:20:y:2023:i:1:p:30-42:n:3
    DOI: 10.2478/jec-2023-0003
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    References listed on IDEAS

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    1. Ingram, F. Jerry & Frazier, Emma L., 1982. "Alternative Multivariate Tests in Limited Dependent Variable Models: An Empirical Assessment," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 17(2), pages 227-240, June.
    2. Blum, M, 1974. "Failing Company Discriminant-Analysis," Journal of Accounting Research, Wiley Blackwell, vol. 12(1), pages 1-25.
    3. 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.
    4. İbrahim Bozkurt & Muhammed Veysel Kaya, 2023. "Foremost features affecting financial distress and Bankruptcy in the acute stage of COVID-19 crisis," Applied Economics Letters, Taylor & Francis Journals, vol. 30(8), pages 1112-1123, May.
    5. Francesco Ciampi & Alessandro Giannozzi & Giacomo Marzi & Edward I. Altman, 2021. "Rethinking SME default prediction: a systematic literature review and future perspectives," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2141-2188, March.
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    More about this item

    Keywords

    Bankruptcy; Prediction model; Multiple discriminant analysis; Manufacturing and construction sector; Slovakia;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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