Modeling of company´s default probability in relation to its credit risk

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Published Nov 4, 2016
DOI https://doi.org/10.18844/gjbem.v6i2.1378

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Ivana Weissova
University of Zilina, Faculty of Operation and Economics of Transport and Communications, Department of Economics, Univerzitná 1, 010 26 Žilina, Slovak Republic * ADDRESS FOR CORRESPONDENCE: Ivana, Weissova, University of Zilina, Faculty of Operation and Economics of Transport and Communications, Department of Economics, Univerzitná 1, 010 26 Žilina, Slovak Republic E-mail address: [email protected]
Anna Siekelova
University of Zilina, Faculty of Operation and Economics of Transport and Communications, Department of Economics, Univerzitná 1, 010 26 Žilina, Slovak Republic
Katarina Kramarova
University of Zilina, Faculty of Operation and Economics of Transport and Communications, Department of Economics, Univerzitná 1, 010 26 Žilina, Slovak Republic

Abstract

The issue of bankruptcy is very discussed in the modern theory of the company. There are currently many companies that must deal with this issue. They obtain useful information and also use the appropriate tools in order to avoid bankruptcy. Therefore financial analysts are still looking for appropriate ways to predict the bankruptcy of the company. The practical part of the following contribution consists of three steps. At the beginning we randomly selected five Slovak companies. Next we chose four predictive models which are calculated for the last one year. Then we chose the method of Economic Value Added as a method by which we can measure the value of the company. We calculate the Economic Value Added for the last one year in selected companies. Finally, we compare the results of predictive models with the results of Economic Value Added to evaluate the risk of bankruptcy in selected companies. The aim of this paper will be captured the dependence between selected predictive models and Economic Value Added and based on these calculation capture credit risk of these companies.

 

Keywords: credit risk, predictive model, economic value added

 

 

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Vol 6 No 2 (2016): November
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