Abstract
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.
Highlights
The measurement of corporate bankruptcy risk is one of the major challenges of modern economic and financial research
To answer this research question, the main objective of this study is to develop dynamic bankruptcy prediction models for European enterprises with the use of four methods—fuzzy sets, artificial neural networks, and decision trees
This paper presents how to improve the effectiveness of forecasting corporate distress risk models in both the short and long horizon, exceeding five years before the announcement of bankruptcy
Summary
The measurement of corporate bankruptcy risk is one of the major challenges of modern economic and financial research. To answer this research question, the main objective of this study is to develop dynamic bankruptcy prediction models for European enterprises with the use of four methods—fuzzy sets, artificial neural networks (multilayer and recurrent), and decision trees. It implements a dynamic approach to financial ratios describing the economic situation of enterprises It verifies the influence of the dynamic approach on effectiveness of models developed with the use of four different forecasting techniques. It allows the analysis of which method has the smallest decrease in effectiveness in extending the forecast horizon from one to 10 years.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
