Simulation of the Bankruptcy Event of Companies Associated with a Business Group

Abstract

The purpose of the study is to determine the influence of a business group on the assessment of the borrower’s creditworthiness, as well as to identify the most significant credit risk factors. Despite the fact that creditworthiness assessment is widely disseminated in both domestic and foreign literature, the impact of the consolidated group in the context of this problem is practically not mentioned. The authors use a statistical modeling method using logistic regression. The variable models are based on the annual financial statements of both individual companies and business groups. To select factors and build a model, approaches used in statistics and machine learning were used to obtain unbiased and effective estimates, independent of the sample generating these estimates. Analyzed data of 8691 companies providing annual financial statements in accordance with Russian accounting standards from 2015 to 2021. The total sample size was 22 201 observations. The number of bankruptcy events in the sample is 238 observations. Variables calculated from consolidated financial statements in accordance with international standards were used as information about the group. Various views on the concepts of “business group” and “holding” in the domestic literature are considered and systematized. Features of the behavior of companies united in groups are given. Variables associated with the business group that are significant in assessing the probability of bankruptcy of individual companies have been identified. Various specific aspects of the activities of companies associated with the group are mentioned. A statistical model is constructed to confirm a number of hypotheses, which is subject to verification and analysis. The bankruptcy event is used to determine the significant deterioration of a company’s creditworthiness. It is concluded that the use of group reporting data can improve the quality of model prediction for companies associated with a business group.

Keywords

• credit risk
• bankruptcy
• bankruptcy risk factors
• business group
• holding
• ifrs
• logistic regression
• machine learning