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COMPARISON OF STATISTICAL MODELS FOR ASSESSING THE PROBABILITY OF BANKRUPTCY OF ENTERPRISES

Victoria F. Turygina

First published: 2019-06-20https://doi.org/10.5593/sgem2019/2.1/s07.023View metrics

Abstract

The article is devoted to the study of the effectiveness of various statistical models of the probability of bankruptcy of enterprises based on the examples of the modified Altman model and the model of Sayfulin and Kadykov. A comparison of the results of applying these models showed that in many cases they give opposite results. The reason for the differences is that the Altman model is based on statistics of bankruptcies of American enterprises in 1946-1965, and the model of Saifulin and Kadykov is based on statistics of bankruptcies of Russian enterprises of the 2000s. In this regard, the end results of using these models for Russian enterprises may be different, in particular: 1) In the case of underestimation of the probability of bankruptcy - the acceleration of the bankruptcy of the enterprise as a result of the lack of timely developed anti-crisis measures; 2) In the event of a reassessment of the probability of bankruptcy - the unjustified conduct of anti-crisis measures that provide for the reduction of entrepreneurial risk and may lead to a decrease in the potential profit of the enterprise. Thus, enterprises need a more accurate diagnosis of bankruptcy, based on statistical data, taking into account the specifics of the economic activities of a particular country or region. For Russian enterprises, the local Saifulin and Kadykov model, based on the Russian specifics of bankruptcies, is more preferable than the more general standard Altman model.

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Publication details

Title
COMPARISON OF STATISTICAL MODELS FOR ASSESSING THE PROBABILITY OF BANKRUPTCY OF ENTERPRISES
Authors
Victoria F. Turygina
Proceedings
SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings; 19th International Multidisciplinary Scientific GeoConference SGEM2019, Informatics, Geoinformatics and Remote Sensing
Publisher
STEF92 Technology
Year
2019
Pages
175-180
SWS Citekey
Turygina20197175180
ISSN
1314-2704
ISBN
978-619-7408-79-9
Language
en
Publication type
Conference Paper
Keywords
References9
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