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Modeling of the probability of bankruptcy of Russian non-financial companies

Makushina E.Yu. National Research University Higher School of Economics, Moscow, Russian Federation ( emakushina@hse.ru )

Shikhlyarova I.A. National Research University Higher School of Economics, Moscow, Russian Federation ( Ilona.shlrv@gmail.com )

Journal: Finance and credit, #1, 2018

Importance This article discusses the impact of the company's financial performance on the likelihood of its bankruptcy, considering Russian non-financial companies as a case study.
Objectives The article aims to build a model of forecasting bankruptcy of Russian companies of non-financial sector, which will 80-percent-least-reliable predict the bankruptcy of the company a year before its onset.
Methods For the study, we used the methods of logit analysis and decision tree.
Results The article presents a model that with more than 80-percent-reliability predicted bankruptcy of the company one year before its onset through both statistical method (logistic regression) and methods of machine learning (decision trees).
Conclusions and Relevance The models obtained can be used to determine the probability of the company's bankruptcy one year before its onset. For further research in this area, it is possible to include market indicators to improve the quality of the predictive model.


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