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Bankruptcy Prediction Models Based on Value Measures

Author

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  • Andrzej Jaki

    (College of Management and Quality Sciences, Cracow University of Economics, Rakowicka 27, 31-510 Kraków, Poland)

  • Wojciech Ćwięk

    (College of Management and Quality Sciences, Cracow University of Economics, Rakowicka 27, 31-510 Kraków, Poland)

Abstract

In the existing studies devoted to predicting bankruptcy, the authors of such models only used book measures. Considering the fact that the evolution of corporate measure efficiency (in addition to book measures) brought into existence and exposed the importance of cash measures, market measures, and measures based on the economic profit concept, it is justified to carry out research into the possibility of using these measures as variables within the discriminant function. The studied dataset was divided into a training set and a testing set based on two variants of the sample division. The assessment of the statistical significance of the built discriminant functions as well as the diagnostic variables was conducted using the STATISTICA package. The research was conducted separately for each variant. In the first step, a total of 30 discriminant models were created. This enabled us to select 20 diagnostic variables that were considered within the two models that were characterised by the highest predictive abilities—one for each variant. The discriminant function that was estimated for the first variant was based on the use of eight diagnostic variables, and 13 diagnostic variables were used in the function that was estimated for the second variant. The conducted analysis has proven that shareholder value measures are a useful tool that can be applied for the needs of corporate risk management in the area of the assessment of a firm’s bankruptcy risk. Using two variants of the division of the research sample into the training and testing sets, it turned out that the division affects the predictive efficiency of the discriminant functions. At the same time, the obtained findings tend to claim that the presence of the value measures from all four of the studied groups in the output set of the diagnostic variables is necessary for possibly building the most efficient tool for the early warning signs of bankruptcy risk.

Suggested Citation

  • Andrzej Jaki & Wojciech Ćwięk, 2020. "Bankruptcy Prediction Models Based on Value Measures," JRFM, MDPI, vol. 14(1), pages 1-14, December.
  • Handle: RePEc:gam:jjrfmx:v:14:y:2020:i:1:p:6-:d:467914
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    References listed on IDEAS

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    1. Nicoleta Bărbuță-Mișu & Mara Madaleno, 2020. "Assessment of Bankruptcy Risk of Large Companies: European Countries Evolution Analysis," JRFM, MDPI, vol. 13(3), pages 1-28, March.
    2. Fernandez, Pablo, 2003. "Three residual income valuation methods and discounted cash flow valuation," IESE Research Papers D/487, IESE Business School.
    3. Beata Gavurova & Miroslava Packova & Maria Misankova & Lubos Smrcka, 2017. "Predictive potential and risks of selected bankruptcy prediction models in the Slovak business environment," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 18(6), pages 1156-1173, November.
    4. Takahashi, Kichinosuke & Kurokawa, Yukiharu & Watase, Kazunori, 1984. "Corporate bankruptcy prediction in Japan," Journal of Banking & Finance, Elsevier, vol. 8(2), pages 229-247, June.
    5. 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.
    6. Sumaira Ashraf & Elisabete G. S. Félix & Zélia Serrasqueiro, 2019. "Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    7. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    8. 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.
    9. 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.
    10. Tomasz Korol, 2019. "Dynamic Bankruptcy Prediction Models for European Enterprises," JRFM, MDPI, vol. 12(4), pages 1-15, December.
    11. Daniela Mancini & Giuseppina Piscitelli, 2018. "Performance measurement systems in business networks: a literature review," International Journal of Business Performance Management, Inderscience Enterprises Ltd, vol. 19(1), pages 87-104.
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