Finance and Credit

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

Vol. 24, Iss. 1, JANUARY 2018

Received: 28 November 2017

Received in revised form: 12 December 2017

Accepted: 26 December 2017

Available online: 29 January 2018

Subject Heading: THEORY OF FINANCE

JEL Classification: G33

Pages: 95–110

Makushina E.Yu. National Research University Higher School of Economics, Moscow, Russian Federation

Shikhlyarova I.A. National Research University Higher School of Economics, Moscow, Russian Federation

ORCID id: not available

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.

Keywords: default forecast, logistic regression, decision tree, non-financial companies


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