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Prediction of Default of Small Companies in the Slovak Republic

Svabova Lucia (), Durica Marek () and Podhorska Ivana ()
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Podhorska Ivana: Department of Economics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, Zilina, Slovakia

Economics and Culture, 2018, vol. 15, issue 1, 88-95

Abstract: From the time of Altman and the first bankruptcy prediction models, the prediction of default of companies is in the centre of interest of many economists and scientists all over the world. For companies, early detection of the possible threat of imminent financial difficulties or even bankruptcy is a very important part of financial analysis. Over the last few years, many predictive models have been created in the world. However, it has been shown that these models are not very well transferable to the conditions of the economy of another country and their prediction or rating power in another country is lower. Therefore, it is best to create a specific predictive model in the country that takes into account the situation of companies on the basis of real data on their financial situation. This paper is focused on creating a model of failure prediction of small companies in Slovakia using a well-known and widely used method of multivariate discriminant analysis. Discriminant analysis is one of the oldest multivariate statistical methods and sometimes it is difficult to fulfil certain assumptions for data. However, its results are easily interpretable and can be used to classify a company to the group of companies with risk of financial difficulties or, on the contrary, between well-prosperous companies. Prediction model is created based on real data on Slovak enterprises and has a strong classification ability in the specific conditions of the Slovak Republic.

Keywords: prediction of default; bankruptcy prediction models; financial distress; multivariate discriminant analysis (search for similar items in EconPapers)
JEL-codes: C38 G33 (search for similar items in EconPapers)
Date: 2018
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DOI: 10.2478/jec-2018-0010

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