Abstract

The purpose of the article is to study the features of the application and approval of foreign and domestic multifactor discriminant models for assessing the probability of bankruptcy on the example of the joint-stock company "Ukrzaliznytsia" in order to determine the level of their reliability and justify the possibilities of their practical use.A comparative analysis of discriminant models developed by foreign and domestic scientists was conducted. The leading indicators of assessing the probability of bankruptcy of the joint-stockcompany "Ukrzaliznytsia" were calculated using the models of E.Altman, R.Lees, G.Springate, R. Tuffler, G.Tishou, V.Biver, O.Tereshchenko, O.Matviychuk.The models chosen for analysis generally assess the enterprise's financial state with a reasonably high degree of accuracy, which is a determining factor in anti-crisis management. A comparative analysis of the financial condition of JSC "Ukrzaliznytsia" for the years 2018-2020 and the results of assessing the probability of bankruptcy with the helpof the researched discriminant models of foreign and domestic scientists showed that the most reliable result, which reflects the actual state of the enterprise, obtained according to the coefficient of V.Beaver and the models of R.Tuffler and G.Tishou, O.Tereshchenko.While researching the possibilities of using various multifactorial discriminant models, it was established that their main advantage is the express diagnosis of the enterprise's financial state based on a limited group of indicators to promptly identify the threat of bankruptcy. It was found that the studied models also have certain shortcomings. However, despite the identified shortcomings, using multivariate discriminant models to assess the probability of bankruptcy allows us to monitor changes in the enterprise's financial condition. Improving and developing new models for diagnosing a crisis state and assessing the probability of bankruptcy of enterprises, searching for and substantiating optimal factors to adapt already developed discriminatory models and increase their reliability remains a relevant direction of further research.

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