Logit business failure prediction in V4 countries

Durica Marek (), Katarina Valaskova and Janoskova Katarina ()
Additional contact information
Durica Marek: The University of Zilina, ZilinaSlovakia
Janoskova Katarina: The University of Zilina, ZilinaSlovakia

Engineering Management in Production and Services, 2019, vol. 11, issue 4, 54-64

Abstract: The paper presents the creation of the model that predicts the business failure of companies operating in V4 countries. Based on logistic regression analysis, significant predictors are identified to forecast potential business failure one year in advance. The research is based on the data set of financial indicators of more than 173 000 companies operating in V4 countries for the years 2016 and 2017. A stepwise binary logistic regression approach was used to create a prediction model. Using a classification table and ROC curve, the prediction ability of the final model was analysed. The main result is a model for business failure prediction of companies operating under the economic conditions of V4 countries. Statistically significant financial parameters were identified that reflect the impending failure situation. The developed model achieves a high prediction ability of more than 88%. The research confirms the applicability of the logistic regression approach in business failure prediction. The high predictive ability of the created model is comparable to models created by especially sophisticated artificial intelligence approaches. The created model can be applied in the economies of V4 countries for business failure prediction one year in advance, which is important for companies as well as all stakeholders.

Keywords: logit; business failure; financial ratios; prediction model; V4 countries (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://doi.org/10.2478/emj-2019-0033 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vrs:ecoman:v:11:y:2019:i:4:p:54-64:n:5

DOI: 10.2478/emj-2019-0033

Access Statistics for this article

Engineering Management in Production and Services is currently edited by Joanna Ejdys

More articles in Engineering Management in Production and Services from Sciendo
Bibliographic data for series maintained by Peter Golla ().