Bankruptcy Risk Factors of Russian Companies
The bankruptcy of Russian companies in the existing environment has become rather common. Determination of bankruptcy risk factors allows predicting the prospects for business development. The authors set the task to determine the relative influenceof individual financial and non-financial factors o...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | Russian |
| Published: |
Government of the Russian Federation, Financial University
2022-12-01
|
| Series: | Финансы: теория и практика |
| Subjects: | |
| Online Access: | https://financetp.fa.ru/jour/article/view/1870 |
| _version_ | 1797878687144607744 |
|---|---|
| author | A. A. Zhukov E. D. Nikulin D. A. Shchuchkin |
| author_facet | A. A. Zhukov E. D. Nikulin D. A. Shchuchkin |
| author_sort | A. A. Zhukov |
| collection | DOAJ |
| description | The bankruptcy of Russian companies in the existing environment has become rather common. Determination of bankruptcy risk factors allows predicting the prospects for business development. The authors set the task to determine the relative influenceof individual financial and non-financial factors on the probability of a company’s bankruptcy. To study risk factors, the authors analyzed 3184 large Russian companies (with revenues of more than 2 billion rubles per year and more than 250 employees) of various industries operating from 2009 to 2020. The total number of observations is 38,208. For analysis, 30 factors were selected and divided into five groups: profitability, liquidity, turnover, financial stability and general (non-financial) factors. For the study, one of the machine learning methods was used – the random forest method. The sample consists of companies from seven industries, including manufacturing, retail, construction, electric power, mining, agricultural production, and water supply, as well as other industries, which include companies in education, healthcare, agriculture, and hospitality. The analysis was carried out both in aggregate for the entire sample without being distributed by industry, and for samples distributed by manufacturing, retail, and service industries. In the sample as a whole, the tested model in 86% of cases correctly predicted the possibility of a company going bankrupt for the period under review. This result confirmed that machine learning methods (in particular, the random forest algorithm) are highly effective in solving the problem of bankruptcy prediction for a company. Based on the data obtained, the paper concludes that profitability factors have the most significant impact on the probability of bankruptcy for manufacturing and retail companies. For service companies, it is financial stability factors. Solving the problem of determining the bankruptcy risk factors of Russian companies will ensure a reduction in the number of bankrupt enterprises, which, in turn, will contribute to the recovery and development of the national economy. |
| first_indexed | 2024-04-10T02:37:08Z |
| format | Article |
| id | doaj.art-ab08b98728b142c1b6c4cce54b73f67a |
| institution | Directory Open Access Journal |
| issn | 2587-5671 2587-7089 |
| language | Russian |
| last_indexed | 2024-04-10T02:37:08Z |
| publishDate | 2022-12-01 |
| publisher | Government of the Russian Federation, Financial University |
| record_format | Article |
| series | Финансы: теория и практика |
| spelling | doaj.art-ab08b98728b142c1b6c4cce54b73f67a2023-03-13T07:49:31ZrusGovernment of the Russian Federation, Financial UniversityФинансы: теория и практика2587-56712587-70892022-12-0126613115510.26794/2587-5671-2022-26-6-131-155989Bankruptcy Risk Factors of Russian CompaniesA. A. Zhukov0E. D. Nikulin1D. A. Shchuchkin2Санкт-Петербургский государственный университетСанкт-Петербургский государственный университетСанкт-Петербургский государственный университет телекоммуникаций им. профессора М. А. Бонч-БруевичаThe bankruptcy of Russian companies in the existing environment has become rather common. Determination of bankruptcy risk factors allows predicting the prospects for business development. The authors set the task to determine the relative influenceof individual financial and non-financial factors on the probability of a company’s bankruptcy. To study risk factors, the authors analyzed 3184 large Russian companies (with revenues of more than 2 billion rubles per year and more than 250 employees) of various industries operating from 2009 to 2020. The total number of observations is 38,208. For analysis, 30 factors were selected and divided into five groups: profitability, liquidity, turnover, financial stability and general (non-financial) factors. For the study, one of the machine learning methods was used – the random forest method. The sample consists of companies from seven industries, including manufacturing, retail, construction, electric power, mining, agricultural production, and water supply, as well as other industries, which include companies in education, healthcare, agriculture, and hospitality. The analysis was carried out both in aggregate for the entire sample without being distributed by industry, and for samples distributed by manufacturing, retail, and service industries. In the sample as a whole, the tested model in 86% of cases correctly predicted the possibility of a company going bankrupt for the period under review. This result confirmed that machine learning methods (in particular, the random forest algorithm) are highly effective in solving the problem of bankruptcy prediction for a company. Based on the data obtained, the paper concludes that profitability factors have the most significant impact on the probability of bankruptcy for manufacturing and retail companies. For service companies, it is financial stability factors. Solving the problem of determining the bankruptcy risk factors of Russian companies will ensure a reduction in the number of bankrupt enterprises, which, in turn, will contribute to the recovery and development of the national economy.https://financetp.fa.ru/jour/article/view/1870корпоративные финансыкрупные компаниибизнесфинансовый анализфинансовая устойчивостьпрогнозирование банкротствафакторы риска банкротстваметоды машинного обученияслучайный лес |
| spellingShingle | A. A. Zhukov E. D. Nikulin D. A. Shchuchkin Bankruptcy Risk Factors of Russian Companies Финансы: теория и практика корпоративные финансы крупные компании бизнес финансовый анализ финансовая устойчивость прогнозирование банкротства факторы риска банкротства методы машинного обучения случайный лес |
| title | Bankruptcy Risk Factors of Russian Companies |
| title_full | Bankruptcy Risk Factors of Russian Companies |
| title_fullStr | Bankruptcy Risk Factors of Russian Companies |
| title_full_unstemmed | Bankruptcy Risk Factors of Russian Companies |
| title_short | Bankruptcy Risk Factors of Russian Companies |
| title_sort | bankruptcy risk factors of russian companies |
| topic | корпоративные финансы крупные компании бизнес финансовый анализ финансовая устойчивость прогнозирование банкротства факторы риска банкротства методы машинного обучения случайный лес |
| url | https://financetp.fa.ru/jour/article/view/1870 |
| work_keys_str_mv | AT aazhukov bankruptcyriskfactorsofrussiancompanies AT ednikulin bankruptcyriskfactorsofrussiancompanies AT dashchuchkin bankruptcyriskfactorsofrussiancompanies |