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Dynamic Bankruptcy Prediction Models for European Enterprises

Faculty of Management and Economics, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland
J. Risk Financial Manag. 2019, 12(4), 185; https://doi.org/10.3390/jrfm12040185
Received: 8 November 2019 / Revised: 4 December 2019 / Accepted: 5 December 2019 / Published: 9 December 2019
This manuscript is devoted to the issue of forecasting corporate bankruptcy. Determining a firm’s bankruptcy risk is one of the most interesting topics for investors and decision-makers. The aim of the paper is to develop and to evaluate dynamic bankruptcy prediction models for European enterprises. To conduct this objective, four forecasting models are developed with the use of four different methods—fuzzy sets, recurrent and multilayer artificial neural network, and decision trees. Such a research approach will answer the question of whether changes in indicators are relevant predictors of a company’s coming financial crisis because declines or increases in values do not immediately indicate that the company’s economic situation is deteriorating. The research relies on two samples of firms—the learning sample of 50 bankrupt and 50 non-bankrupt enterprises and the testing sample of 250 bankrupt and 250 non-bankrupt firms. View Full-Text
Keywords: corporate bankruptcy; forecasting; fuzzy sets; artificial neural networks; decision trees corporate bankruptcy; forecasting; fuzzy sets; artificial neural networks; decision trees
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Korol, T. Dynamic Bankruptcy Prediction Models for European Enterprises. J. Risk Financial Manag. 2019, 12, 185.

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