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Modele predykcji bankructwa i ich zastosowanie dla rynku NewConnect

Author

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  • Postek, Łukasz
  • Thor, Michał

Abstract

This paper deals with modeling the default of enterprises listed on Poland’s NewConnect market. The study covers an overview of the empirical literature on default prediction in Poland and proposes logit models to predict the default of enterprises listed on the NewConnect market over a one-year horizon. The lack of robustness of the estimates suggests there is no stable or monotonic relation between the financial indicators and default probability on the NewConnect market. Moreover, the models estimated in the study as well as those proposed in the literature suffer from a lack of out-of-sample predictive capabilities. Despite this, default prediction models seem to be potentially useful in the selection of stocks and in weighing them in the investment portfolio. Portfolios constructed on the basis of default prediction models, both those estimated in this paper and those proposed in the literature, are more profitable than a market portfolio with equal weights in each stock.

Suggested Citation

  • Postek, Łukasz & Thor, Michał, 2020. "Modele predykcji bankructwa i ich zastosowanie dla rynku NewConnect," Gospodarka Narodowa-The Polish Journal of Economics, Szkoła Główna Handlowa w Warszawie / SGH Warsaw School of Economics, vol. 2020(1), March.
  • Handle: RePEc:ags:polgne:359204
    DOI: 10.22004/ag.econ.359204
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    References listed on IDEAS

    as
    1. 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.
    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. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 18(1), pages 109-131.
    4. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 22, pages 59-82.
    5. 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.
    6. Sudheer Chava & Amiyatosh Purnanandam, 2010. "Is Default Risk Negatively Related to Stock Returns?," The Review of Financial Studies, Society for Financial Studies, vol. 23(6), pages 2523-2559, June.
    7. Frydman, Halina & Altman, Edward I & Kao, Duen-Li, 1985. "Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress," Journal of Finance, American Finance Association, vol. 40(1), pages 269-291, March.
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