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LOGISTIC REGRESSION AS A TOOL FOR DETERMINATION OF THE PROBABILITY OF DEFAULT FOR ENTERPRISES

Erika Spuchlakova () and Maria Kovacova ()
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Erika Spuchlakova: University of Zilina, Faculty of Operation and Economics of Transport and Communications, Slovak Republic
Maria Kovacova: University of Zilina, Faculty of Operation and Economics of Transport and Communications, Slovak Republic

Scientific Bulletin - Economic Sciences, 2017, vol. 16, issue 2, 41-47

Abstract: In a rapidly changing world it is necessary to adapt to new conditions. From a day to day approaches can vary. For the proper management of the company it is essential to know the financial situation. Assessment of the company financial health can be carried out by financial analysis which provides a number of methods how to evaluate the company financial health. Analysis indicators are often included in the company assessment, in obtaining bank loans and other financial resources to ensure the functioning of the company. As company focuses on the future and its planning, it is essential to forecast the future financial situation. According to the results of company´s financial health prediction, the company decides on the extension or limitation of its business. It depends mainly on the capabilities of company´s management how they will use information obtained from financial analysis in practice. The findings of logistic regression methods were published firstly in the 60s, as an alternative to the least squares method. The essence of logistic regression is to determine the relationship between being explained (dependent) variable and explanatory (independent) variables. The basic principle of this static method is based on the regression analysis, but unlike linear regression, it can predict the probability of a phenomenon that has occurred or not. The aim of this paper is to determine the probability of bankruptcy enterprises.

Keywords: Enterprise; Logistic regression; Probability of default. (search for similar items in EconPapers)
JEL-codes: G21 G24 G28 (search for similar items in EconPapers)
Date: 2017
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