An Analysis of the effectiveness of bankruptcy prediction models – an industry approach
An Analysis of the effectiveness of bankruptcy prediction models – an industry approach
Author(s): Bartłomiej PilchSubject(s): Economy, Business Economy / Management, Financial Markets, Socio-Economic Research
Published by: Wydawnictwo Naukowe Uniwersytetu Szczecińskiego
Keywords: financial ratios; sectors of the economy; discriminant analysis
Summary/Abstract: Research background: Bankruptcy prediction models are frequently used in research. However, an industry approach is not often carried out. Due to this, this study included trends observable between the number of bankruptcies and its prediction by models. Purpose: The aim of the paper is to verify if changes in the number of actual bankruptcy in individual industries are properly predicted by the models. Also, if analyzed models are providing consistent information according to the risk of bankruptcy between industries. Research methodology: The data were collected from the Orbis database and the Coface reports. The period included in the study is 2014–2019. 5 Polish bankruptcy prediction models were used: these by D. Hadasik, E. Mączyńska and M. Zawadzki, M. Pogodzińska and S. Sojak, D. Wierzba and the Poznan one. Results: The analyzed models do not properly predict changes in the number of bankruptcy in individual industries, however, 3 out of 5 correctly predicted the trend for the entire sample. Analyzed models often provide inconsistent information. Hence, it seems sensible to use more than a few models in any further analyzes. Novelty: In the literature of the subject, there are often carried out analyses focused on the effectiveness of bankruptcy prediction models regarding individual companies. This research is focused on the prediction of changes in the number of companies to be considered as at bankruptcy risk between industries, and also on comparing these models.
Journal: Folia Oeconomica Stetinensia
- Issue Year: 21/2021
- Issue No: 2
- Page Range: 76-96
- Page Count: 21
- Language: English