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Modele predykcji bankructwa i ich zastosowanie dla rynku NewConnect

Author

Listed:
  • Łukasz Postek
  • Michał Thor

Abstract

Niniejsza praca podejmuje problematykę modelowania bankructwa spółek notowanych na rynku NewConnect. Dokonano przeglądu prac empirycznych związanych z predykcją upadłości przedsiębiorstw w Polsce i za pomocą regresji logistycznej zbudowano modele przewidujące bankructwa spółek notowanych na rynku NewConnect w rocznym horyzoncie czasowym. Brak odporności uzyskanych oszacowań podaje w wątpliwość istnienie stabilnej i monotonicznej relacji pomiędzy wskaźnikami finansowymi a prawdopodobieństwem bankructwa na NewConnect. Równocześnie zarówno oszacowane modele, jak i najpopularniejsze polskie modele predykcji bankructwa cechują się brakiem zdolności prognostycznych out-of-sample. Mimo to modele predykcji bankructwa okazują się być potencjalnie przydatne w wyborze celów inwestycyjnych i ustaleniu wag poszczególnych akcji w portfelu inwestycyjnym – portfele skonstruowane na podstawie modeli oszacowanych w niniejszej pracy oraz najpopularniejsze polskie modele predykcji bankructwa cechują się wyższą stopą zwrotu niż portfel z równymi wagami dla wszystkich spółek.

Suggested Citation

  • Łukasz Postek & Michał Thor, 2020. "Modele predykcji bankructwa i ich zastosowanie dla rynku NewConnect," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 1, pages 109-137.
    • Handle: RePEc:sgh:gosnar:y:2020:i:1:p:109-137
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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. Deniz Anginer & Çelim Yıldızhan, 2018. "Is There a Distress Risk Anomaly? Pricing of Systematic Default Risk in the Cross-section of Equity Returns [The risk-adjusted cost of financial distress]," Review of Finance, European Finance Association, vol. 22(2), pages 633-660.
    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. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    5. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 59-82.
    6. 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.
    7. 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.
    8. 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.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    logit; prognozowanie bankructwa; NewConnect; portfel inwestycyjny;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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