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|Title:||Applicability of forecasted bankruptcy models to Russian industrial companies|
|Authors:||Chebotareva, G. S.|
Gafurov, N. S.
|Publisher:||South Ural State University|
|Citation:||Chebotareva G. S. Applicability of forecasted bankruptcy models to Russian industrial companies / G. S. Chebotareva, W. Strielkowski, N. S. Gafurov. — DOI 10.14529/mmp200311 // Bulletin of the South Ural State University, Series: Mathematical Modelling, Programming and Computer Software. — 2020. — Vol. 13. — Iss. 3. — P. 98-102.|
|Abstract:||The application of effective methods for forecasting of the bankruptcy of industrial companies is always an urgent task for businesses, especially at the present stage which is characterized by an extremely high uncertainty. The paper presents the main techniques of bankruptcy modelling used in the world's practice: logit, probit and MDA-models, as well as the special private methods developed on their basis. These tools constitute the methodological foundation of our research. To assess the practical applicability of these methods to the contemporary Russian market, two sectorial companies (bankrupt and nonbankrupt) are selected as the object of study. A feature of the research is the use of financial statements of companies developed according to Russian and international standards. In the course of the calculations, we apply external and internal restrictions related to the key rate, credit history characteristics, age and regional affiliation of companies. Based on the dynamic assessment, we draw conclusions about the practical applicability and inapplicability of certain forecast models for the Russian economy. We investigate the relationship between the assessment results and the type of source data used. Research veracity is confirmed using generally recognized models and methods, as well as the practical implementation of the results obtained. We can recommend to use these results for improving the existing models for predicting bankruptcy and developing new ones, as well as for owners and investors of companies who need to make strategic decisions. © 2020 South Ural State University. All rights reserved.|
|metadata.dc.description.sponsorship:||The work was supported by Act 211 of the Government of the Russian Federation (contract no. 02.A03.21.0006) and GSEM UrFU Development Fund.|
|Appears in Collections:||Научные публикации, проиндексированные в SCOPUS и WoS CC|
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