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Business Failure Prediction for Slovak Small and Medium-Sized Companies

doi: 10.3390/su12114572
Prediction of the financial difficulties of companies has been dealt with over the last years by scientists and economists worldwide. Several prediction models mostly focused on a particular sector of the national economy, have been created also in Slovakia. The main purpose of this paper is to create new prediction models for small and medium-sized companies in Slovakia, based on real data from the Amadeus database from the years 2016–2018. We created prediction models of financial difficulties of companies for 1 year in advance and also a model for 2 years prediction. These models are based on the combination of two methods, discriminant analysis and logistic regression that belong, among others, to the group of the most commonly used methods to derive prediction models of financial difficulties of the companies. The overall prediction powers of the combined model are 90.6%, 93.8% and 90.4%. The results of this analysis can be used for early prediction of the financial difficulties of the company, that could be very useful for all the stakeholders.
- University of Žilina Slovakia
- University of Žilina Slovakia
Environmental effects of industries and plants, logistic regression, SME, prediction models, TJ807-830, discriminant analysis, TD194-195, Renewable energy sources, Environmental sciences, failure prediction, GE1-350, company’s non-prosperity, bankruptcy prediction
Environmental effects of industries and plants, logistic regression, SME, prediction models, TJ807-830, discriminant analysis, TD194-195, Renewable energy sources, Environmental sciences, failure prediction, GE1-350, company’s non-prosperity, bankruptcy prediction
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).33 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
