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Liczba wyników
2015 | 15 | nr 1 | 7--21
Tytuł artykułu

Non-Statistical Methods of Analysing of Bankruptcy Risk

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.(original abstract)
Rocznik
Tom
15
Numer
Strony
7--21
Opis fizyczny
Twórcy
  • Rzeszow University of Technology, Poland
  • Rzeszow University of Technology, Poland
  • Rzeszow University of Technology, Poland
Bibliografia
  • Atiya A.F. (2001). Bankruptcy prediction for credit risk using neural networks: A survey and new results. IEEE Transactions on Neural Networks, 12 (4): 929-935.
  • Choi W.S., Lee, S (2013). A multi-industry bankruptcy prediction model using back-propagation neural network and multivariate discriminant analysis. Expert Systems with Applications, 40 (8): 2941-2946. [CrossRef] [Web of Science]
  • Fedorova E., Gilenko E., Dovzhenko, S. (2013). Bankruptcy prediction for Russian companies: Application of combined classifiers. Expert Systems with Applications, 40: 7285-7293. [CrossRef] [Web of Science]
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  • Kaski S., Sinkkonen J., Peltonen J. (2001). Bankruptcy analysis with self-organizing maps in learning metrics. IEEE Transactions on Neural Networks, 12 (4): 936-947.
  • Kiviluoto K. (1998). Predicting bankruptcies with self organizing map. Neurocomputing, 21: 191-201. [CrossRef]
  • Korol T. (2012). Early warning models against bankruptcy risk for Central European and Latin American enterprises. Economic Modelling, 31: 22-30. [Web of Science] [CrossRef]
  • Lam M. (2004). Neural networks techniques for financial performance prediction: integrating fundamental and technical analysis. Decision Support Systems, 37: 567-581. [CrossRef]
  • Lee K., Booth D., Alam, P. (2005). A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms. Expert Systems with Applications, 29: 1-16. [CrossRef]
  • Leshno M., Spector Y. (1996). Neural network prediction analysis: The bankruptcy case. Neurocomputing, 10: 125-147. [CrossRef]
  • Löffler G., Posch P.N. (2007). Credit risk modeling using Excel and VBA. Chichester, West Sussex: Wiley (pp. 156).
  • Prusak B. (2005). Nowoczesne metody prognozowania zagrożenia finansowego przedsiębiorstw. Warszawa: Difin (pp. 50).
  • Serrano-Cinca C. (1996). Self organizing neural networks for financial diagnosis. Decision Support Systems, 17: 227-238. [CrossRef]
  • Tam K.Y., Kiang M. (1992). Predicting bank failures: A neural network approach. Decision Sciences, 23: 926-947.
  • Thomas L.C. (2009). Consumer credit models. Pricing, Profit and Portfolios. Oxford: Oxford University Press (pp. 111).
  • Tseng F.M., Hu Y.C. (2010). Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks. Expert Systems with Applications, 37 (3): 1846-1853. [Web of Science] [CrossRef]
  • Wilson R.L., Sharda R. (1994). Bankruptcy prediction using neural networks. Decision Support Systems, 11: 545-557. [CrossRef] [Web of Science]
  • Witkowska D. (2002). Sztuczne sieci neuronowe i metody statystyczne. Wybrane zagadnienia finansowe. Warszawa: C.H. Beck (pp. 86-87).
  • Yu L., Wang S., Lai K.K., Zhou, L. (2008). Bio-Inspired Credit Risk Analysis. Computational Intelligence with Support Vector Machines. Berlin Heidelberg: Springer-Verlag (pp. 14-15).
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171405913

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