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The Potential of Dynamic Indicator in Development of the Bankruptcy Prediction Models: the Case of Construction Companies

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  • Michal Karas

    (Department of Finances, Faculty of Business and Management, Brno University of Technology, Kolejní 2906/4, 612 00 Brno, Czech Republic)

  • Mária Režňáková

    (Department of Finances, Faculty of Business and Management, Brno University of Technology, Kolejní 2906/4, 612 00 Brno, Czech Republic)

Abstract

The current development of bankruptcy models usually goes in the direction of testing different classification algorithms, while the potential hidden in financial indicators is given less attention. Their analysis is often only restricted to the comparison between their respective statuses in bankrupt and healthy companies, while the dynamics of the indicators, i.e. the change in their values in time, is not paid much attention. The aim or our research is to analyse partial potential of financial ratios for predicting bankruptcy. Twenty-eight indicators were examined in a sample of 1,355 construction companies operating in the Czech Republic, as well as their development over the past five periods. A non-parametric chi-square test was used to evaluate the significance of predictors. The variables were categorised for the application of the test. Our research confirmed the assumption as to the importance of using the indicators in dynamic (change) form. Indicators that are significant only in their change form were identified. Moreover, the use of the dynamic form of the indicators can increase the significance of the bankruptcy model. This was tested by using the stepwise version of linear discrimination analysis.

Suggested Citation

  • Michal Karas & Mária Režňáková, 2017. "The Potential of Dynamic Indicator in Development of the Bankruptcy Prediction Models: the Case of Construction Companies," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 65(2), pages 641-652.
  • Handle: RePEc:mup:actaun:actaun_2017065020641
    DOI: 10.11118/actaun201765020641
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    References listed on IDEAS

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    1. Wu, Y. & Gaunt, C. & Gray, S., 2010. "A comparison of alternative bankruptcy prediction models," Journal of Contemporary Accounting and Economics, Elsevier, vol. 6(1), pages 34-45.
    2. Deakin, Eb, 1972. "Discriminant Analysis Of Predictors Of Business Failure," Journal of Accounting Research, Wiley Blackwell, vol. 10(1), pages 167-179.
    3. 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.
    4. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    5. Libuše Svobodová, 2013. "Development in the Insolvency Procedure in the Czech Republic," Ekonomika a Management, Prague University of Economics and Business, vol. 2013(1), pages 44-55.
    6. 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.
    7. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    8. Niemann, Martin & Schmidt, Jan Hendrik & Neukirchen, Max, 2008. "Improving performance of corporate rating prediction models by reducing financial ratio heterogeneity," Journal of Banking & Finance, Elsevier, vol. 32(3), pages 434-446, March.
    9. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    10. Tian, Shaonan & Yu, Yan & Guo, Hui, 2015. "Variable selection and corporate bankruptcy forecasts," Journal of Banking & Finance, Elsevier, vol. 52(C), pages 89-100.
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