Bankruptcy prediction with the use of data envelopment analysis: An empirical study of Slovak businesses

Artikel

The paper deals with methods of predicting bankruptcy of a business with the aim of choosing a prediction method which will have exact results. Existing bankruptcy prediction models are a suitable tool for predicting the financial difficulties of businesses. However, such tools are based on strictly defined financial indicators. Therefore, the Data Envelopment Analysis (DEA) method has been applied, as it allows for the free choice of financial indicators. The research sample consisted of 343 businesses active in the heating industry in Slovakia. Analysed businesses have a significant relatively stable position in the given industry. The research was based on several studies which also used the DEA method to predict future financial difficulties and bankruptcies of studied businesses. The estimation accuracy of the Additive DEA model (ADD model) was compared with the Logit model to determine the reliability of the DEA method. Also, an optimal cut-off point for the ADD model and Logit model was determined. The main conclusion is that the DEA method is a suitable alternative for predicting the failure of the analysed sample of businesses. In contrast to the Logit model, its results are independent of any assumptions. The paper identified the key indicators of the future success of businesses in the analysed sample. These results can help businesses to improve their financial health and competitiveness.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 13 ; Year: 2020 ; Issue: 9 ; Pages: 1-15 ; Basel: MDPI

Classification and Topics

• Classification: Wirtschaft
• Subject: bankruptcy ; data envelopment analysis ; logit ; model

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Štefko, Róbert ; Horváthová, Jarmila ; Mokrišová, Martina
• Event: Veröffentlichung
• (who): MDPI
• (where): Basel
• (when): 2020

Further information

• DOI: doi:10.3390/jrfm13090212
• Handle: http://hdl.handle.net/10419/239282
• Last update: 10.03.2025, 11:43 AM CET