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Financial distress of companies in Poland

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  • Marek Gruszczynski

    (Department of Applied Econometrics, Warsaw School of Economics)

Abstract

The study examines main determinants of financial distress of companies in Poland during the recent transformation period. The data compose a sample of 1995-97 annual financial statements of 200 unlisted companies in Poland. The sample was collected by the Institute of Economics of the Polish Academy of Sciences. Degree of financial distress is expressed either by the binomial variable with the following states: (1) the company in financial distress, (2) the company financially sound, or by the trinomial ordered variable with the inconclusive state between (1) and (2). The attempted models ex-plain the distress variable (binomial or trinomial) for 1997 by the financial indicators evaluated on the basis of financial statements from previous years (1995 and 1996). The models applied to the data are binomial logit model and trinomial ordered logit model. The results of the research are presented in a number of estimated binomial and trinomial logit models. The results are sensitive to the choice of explanatory variables. The forecast accuracy of the estimated models lies in the range of 80-90 percent. Paper gives some evidence to the idea that in the second half of the nineties the financial condition of companies in Poland was determined by the degree of liquid-ity, profitability and the level of financial leverage.

Suggested Citation

  • Marek Gruszczynski, 2004. "Financial distress of companies in Poland," Working Papers 22, Department of Applied Econometrics, Warsaw School of Economics.
    • Handle: RePEc:wse:wpaper:22
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    References listed on IDEAS

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    1. Hunter, John & Isachenkova, Natalia, 2001. "Failure risk: A comparative study of UK and Russian firms," Journal of Policy Modeling, Elsevier, vol. 23(5), pages 511-521, July.
    2. Lennox, Clive, 1999. "Identifying failing companies: a re-evaluation of the logit, probit and DA approaches," Journal of Economics and Business, Elsevier, vol. 51(4), pages 347-364, July.
    3. Ran Barniv & Anurag Agarwal & Robert Leach, 2002. "Predicting Bankruptcy Resolution," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 29(3‐4), pages 497-520, April.
    4. Lubom??r L??zal, 2002. "Determinants of Financial Distress: What Drives Bankruptcy in a Transition Economy? The Czech Republic Case," William Davidson Institute Working Papers Series 451, William Davidson Institute at the University of Michigan.
    5. 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.
    6. 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.
    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. Kaiser, Ulrich, 2001. "Moving in and out of financial distress: evidence for newly founded service sector firms," ZEW Discussion Papers 01-09, ZEW - Leibniz Centre for European Economic Research.
    9. Ran Barniv & Anurag Agarwal & Robert Leach, 2002. "Predicting Bankruptcy Resolution," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 29(3&4), pages 497-520.
    10. Johnsen, Thomajean & Melicher, Ronald W., 1994. "Predicting corporate bankruptcy and financial distress: Information value added by multinomial logit models," Journal of Economics and Business, Elsevier, vol. 46(4), pages 269-286, October.
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    Cited by:

    1. Elena Gregova & Katarina Valaskova & Peter Adamko & Milos Tumpach & Jaroslav Jaros, 2020. "Predicting Financial Distress of Slovak Enterprises: Comparison of Selected Traditional and Learning Algorithms Methods," Sustainability, MDPI, vol. 12(10), pages 1-17, May.
    2. Vladislav V. Afanasev & Yulia A. Tarasova, 2022. "Default Prediction for Housing and Utilities Management Firms Using Non-Financial Data," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 6, pages 91-110, December.
    3. Misankova Maria & Zvarikova Katarina & Kliestikova Jana, 2017. "Bankruptcy Practice in Countries of Visegrad Four," Economics and Culture, Sciendo, vol. 14(1), pages 108-118, June.
    4. Selcuk Caner & Mehmet Baha Karan, 2012. "Screening Creditworthiness of SME's: The Case of Small Business Assistance in Turkey," Multinational Finance Journal, Multinational Finance Journal, vol. 16(1-2), pages 1-20, March - J.
    5. A. N. Adi & Z. Baridwan & E. Mardiati, 2018. "Profitability, Liquidity, Leverage and Corporate Governance Impact on Financial Statement Fraud and Financial Distress as Intervening Variable," Вестник Киевского национального университета имени Тараса Шевченко. Экономика., Socionet;Киевский национальный университет имени Тараса Шевченко, vol. 5(200), pages 66-74.

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    More about this item

    Keywords

    financial distress; financial indicators; binomial logit; trinomial ordered logit;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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