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Financial distress of companies in Poland

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  • Marek Gruszczynski

Abstract

The study examines main determinants of financial distress of companies in Poland during the recent transformation period. Data compose a sample of 1995–97 annual financial statements of 200 unlisted companies in Poland. Degree of financial distress is expressed either by the binomial variable or by the trinomial ordered variable. The attempted models (binomial and trinomial logit) explain the distress variable for 1997 by the financial indicators evaluated on the basis of financial statements from previous years. The results are sensitive to the choice of explanatory variables in the models. The forecast accuracy of the estimated models lies in the range of 80–90 percent. In the second half of the 1990's, the financial condition of companies in Poland was determined by the degree of liquidity, profitability, and the financial leverage variables. Copyright International Atlantic Economic Society 2004

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  • Marek Gruszczynski, 2004. "Financial distress of companies in Poland," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 10(4), pages 249-256, November.
    • Handle: RePEc:kap:iaecre:v:10:y:2004:i:4:p:249-256:10.1007/bf02295137
    DOI: 10.1007/BF02295137
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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.

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    More about this item

    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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