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Non-Statistical Methods of Analysing of Bankruptcy Risk

Author

Listed:
  • Pisula Tomasz
  • Mentel Grzegorz
  • Brożyna Jacek

    (Department of Quantitative Methods Faculty of Management Rzeszow University of Technology Powstańców Warszawy 8, 35-959 Rzeszów, Poland)

Abstract

The article focuses on assessing the effectiveness of a non-statistical approach to bankruptcy modelling in enterprises operating in the logistics sector. In order to describe the issue more comprehensively, the aforementioned prediction of the possible negative results of business operations was carried out for companies functioning in the Polish region of Podkarpacie, and in Slovakia. The bankruptcy predictors selected for the assessment of companies operating in the logistics sector included 28 financial indicators characterizing these enterprises in terms of their financial standing and management effectiveness. The purpose of the study was to identify factors (models) describing the bankruptcy risk in enterprises in the context of their forecasting effectiveness in a one-year and two-year time horizon. In order to assess their practical applicability the models were carefully analysed and validated. The usefulness of the models was assessed in terms of their classification properties, and the capacity to accurately identify enterprises at risk of bankruptcy and healthy companies as well as proper calibration of the models to the data from training sample sets.

Suggested Citation

  • Pisula Tomasz & Mentel Grzegorz & Brożyna Jacek, 2015. "Non-Statistical Methods of Analysing of Bankruptcy Risk," Folia Oeconomica Stetinensia, Sciendo, vol. 15(1), pages 7-21, June.
    • Handle: RePEc:vrs:foeste:v:15:y:2015:i:1:p:7-21:n:11
    DOI: 10.1515/foli-2015-0029
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    References listed on IDEAS

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    1. Lean Yu & Shouyang Wang & Kin Keung Lai & Ligang Zhou, 2008. "Bio-Inspired Credit Risk Analysis," Springer Books, Springer, number 978-3-540-77803-5, September.
    2. Thomas, Lyn C., 2009. "Consumer Credit Models: Pricing, Profit and Portfolios," OUP Catalogue, Oxford University Press, number 9780199232130.
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