Bekirova, Olga and Zubarev, Andrey (2022): Эконометрический анализ факторов банкротств российских компаний в обрабатывающем секторе.
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Abstract
This work is devoted to the analysis of the factors influencing the bankruptcy of the Russian manufacturing industry companies for the period from 2012 to 2020. Logistic regression was used as an econometric tool for the modelling the probability of companies’ default. According to the results, financial indicators of profitability, liquidity and business activity play a significant role in explaining the probability of default of Russian manufacturing companies. Special attention was paid to the impact on the probability of bankruptcy of corporate governance and ownership structure factors. First, including these indicators into the model led to an increase in its predictive power. Secondly, CEO-duality increases the stability of the company, and too high maximum share of ownership increases the likelihood of bankruptcy.
Item Type: | MPRA Paper |
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Original Title: | Эконометрический анализ факторов банкротств российских компаний в обрабатывающем секторе |
English Title: | Econometric Analysis of Bankruptcy Factors for Russian Companies in the Manufacturing Industry |
Language: | Russian |
Keywords: | probability of default; logistic regression; corporate governance |
Subjects: | C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C25 - Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; Proportions ; Probabilities C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation G - Financial Economics > G3 - Corporate Finance and Governance > G32 - Financing Policy ; Financial Risk and Risk Management ; Capital and Ownership Structure ; Value of Firms ; Goodwill G - Financial Economics > G3 - Corporate Finance and Governance > G33 - Bankruptcy ; Liquidation G - Financial Economics > G3 - Corporate Finance and Governance > G34 - Mergers ; Acquisitions ; Restructuring ; Corporate Governance L - Industrial Organization > L6 - Industry Studies: Manufacturing > L60 - General |
Item ID: | 114969 |
Depositing User: | Olga Bekirova |
Date Deposited: | 20 Oct 2022 07:47 |
Last Modified: | 21 Oct 2022 21:51 |
References: | Демешев Б. Б., Тихонова А. С. Прогнозирование банкротства российских компаний: межотраслевое сравнение // Экономический журнал Высшей школы экономики. 2014. Т. 18. № 3. С. 359–386. Донец С. А., Могилат А. Н. Кредитование и финансовая устойчивость российских промышленных компаний: микроэкономические аспекты анализа // Деньги и кредит. 2017. № 7. С. 41–51. Карминский А. М., Рыбалка А. И. Дыры в капитале компаний обрабатывающей промышленности: корпоративное управление и отраслевые ожидания // Журнал Новой экономической ассоциации. 2018. № 2 (38). С. 76–103. Рыбалка А. И. Факторы риска отраслей обрабатывающей промышленности // Экономическая наука современной России. 2018. № 3. С. 93–113. Сальников В. А., Могилат А. Н., Маслов И. Ю. Стресс-тестирование компаний реального сектора для России: первый подход (методологические аспекты) // Журнал Новой экономической ассоциации. 2012. № 4 (16). С. 46–70. Тотьмянина К. М. Оценка вероятности дефолта промышленных компаний на основе финансовых показателей // Финансовая аналитика: проблемы и решения. 2011. № 11. С. 59–68. Alman E. I. Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy // The Journal of Finance. 1968. Vol. 23. No 4. P. 589–609. Andrade G., Kaplan S. N. How Costly Is Financial (Not Economic) Distress? Evidence from Highly Leveraged Transactions that Became Distressed // The Journal of Finance. 1998. Vol. 53. No 5. P. 1443–1493. Barboza F., Kimura H., Altman E. Machine Learning Models and Bankruptcy Prediction // Expert Systems with Applications. 2017. Vol. 83(C). P. 405–417. Beaver W. H. Financial Ratios As Predictors of Failure // Journal of Accounting Research. 1966. Vol. 4. P. 71–111. Cardoso G. F., Peixoto F. M., Barboza F. Board Structure and Financial Distress in Brazilian Firms // International Journal of Managerial Finance. 2019. Vol. 15. No 5. P. 813–828. Darrat A. F., Gray S., Chul Park J., Wu Y. Corporate Governance and Bankruptcy Risk // Journal of Accounting, Auditing & Finance. 2016. Vol. 31. No 2. P. 163–202. Lohmann C., Ohliger T. Using Accounting‐Based Information on Young Firms to Predict Bankruptcy // Journal of Forecasting. 2019. Vol. 38. No 8. P. 803–819. Nehrebecka N. COVID-19: Stress-Testing Non-Financial Companies: A Macroprudential Perspective. The Experience of Poland // Eurasian Economic Review. 2021. Vol. 11. No 2. P. 283–319. Ohlson J. A. Financial Ratios and the Probabilistic Prediction of Bankruptcy // Journal of Accounting Research. 1980. Vol. 18. No 1. P. 109–131. Tinoco M. H., Wilson N. Financial Distress and Bankruptcy Prediction Among Listed Companies Using Accounting, Market and Macroeconomic Variables // International Review of Financial Analysis. 2013. Vol. 30(C). P. 394–419. Virolainen K. Macro Stress Testing with a Macroeconomic Credit Risk Model for Finland. Bank of Finland Research Discussion Paper. No 18. 2004. Wruck K. H. Financial Distress, Reorganization, and Organizational Efficiency // Journal of Financial Economics. 1990. Vol. 27. No 2. P. 419–444. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/114969 |