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Prediction of Default of Small Companies in the Slovak Republic

Author

Listed:
  • Svabova Lucia

    (Department of Economics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, Zilina, Slovakia)

  • Durica Marek

    (Department of Economics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, Zilina, Slovakia)

  • Podhorska Ivana

    (Department of Economics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, Zilina, Slovakia)

Abstract

From the time of Altman and the first bankruptcy prediction models, the prediction of default of companies is in the centre of interest of many economists and scientists all over the world. For companies, early detection of the possible threat of imminent financial difficulties or even bankruptcy is a very important part of financial analysis. Over the last few years, many predictive models have been created in the world. However, it has been shown that these models are not very well transferable to the conditions of the economy of another country and their prediction or rating power in another country is lower. Therefore, it is best to create a specific predictive model in the country that takes into account the situation of companies on the basis of real data on their financial situation. This paper is focused on creating a model of failure prediction of small companies in Slovakia using a well-known and widely used method of multivariate discriminant analysis. Discriminant analysis is one of the oldest multivariate statistical methods and sometimes it is difficult to fulfil certain assumptions for data. However, its results are easily interpretable and can be used to classify a company to the group of companies with risk of financial difficulties or, on the contrary, between well-prosperous companies. Prediction model is created based on real data on Slovak enterprises and has a strong classification ability in the specific conditions of the Slovak Republic.

Suggested Citation

  • Svabova Lucia & Durica Marek & Podhorska Ivana, 2018. "Prediction of Default of Small Companies in the Slovak Republic," Economics and Culture, Sciendo, vol. 15(1), pages 88-95, June.
  • Handle: RePEc:vrs:ecocul:v:15:y:2018:i:1:p:88-95:n:10
    DOI: 10.2478/jec-2018-0010
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    References listed on IDEAS

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    1. Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
    2. Bhanu Pratap Singh & Alok Kumar Mishra, 2016. "Re-estimation and comparisons of alternative accounting based bankruptcy prediction models for Indian companies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-28, December.
    3. Katarina Zvarikova & Erika Spuchlakova & Gabriela Sopkova, 2017. "International Comparison Of The Relevant Variables In The Chosen Bankruptcy Models Used In The Risk Management," Oeconomia Copernicana, Institute of Economic Research, vol. 8(1), pages 145-157, March.
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    5. J. Efrim Boritz & Duane B. Kennedy & Jerry Y. Sun, 2007. "Predicting Business Failures in Canada," Accounting Perspectives, John Wiley & Sons, vol. 6(2), pages 141-165, May.
    6. 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.
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    Cited by:

    1. Katarina Valaskova & Tomas Kliestik & Lucia Svabova & Peter Adamko, 2018. "Financial Risk Measurement and Prediction Modelling for Sustainable Development of Business Entities Using Regression Analysis," Sustainability, MDPI, vol. 10(7), pages 1-15, June.
    2. Daniel Ogachi & Richard Ndege & Peter Gaturu & Zeman Zoltan, 2020. "Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya," JRFM, MDPI, vol. 13(3), pages 1-14, March.

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    More about this item

    Keywords

    prediction of default; bankruptcy prediction models; financial distress; multivariate discriminant analysis;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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