The Quality of Bank Risk Management: Triggers of Financial Problems
DOI:
https://doi.org/10.21638/spbu18.2018.202Abstract
The significant decrease in the number of banks in the Russian Federation observed recently and arising high social costs of liquidation and sanitation procedures underpin the need for continuous improvement of early-warning systems of bankruptcy. The aim of the article is to identify the key leading indicators of financial insolvency of banks. The study was conducted on a sample of 49 banks from the cluster of medium and small banks in the Moscow region with a license revoked for economic reasons in the period from 2015 to the first half of 2016. The results were tested on a sample of 32 banks, both active and with withdrawn license. We identified the dependence of chosen leading indicators from a number of financial indicators displaying the quality of capital, assets, yield, profitability and liquidity. The results could shed additional light at the causes of financial problems of banks and improve the quality of risk management.
Keywords:
banks, financial insolvency, early-warning systems, leading indicators, efficiency, stability, risk management
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References
ЛИТЕРАТУРА НА РУССКОМ ЯЗЫКЕ
Андросов И. А. 2016. Оценка достаточности и структуры экономического капитала российских банков на основе анализа исторической волатильности чистой прибыли. Управление финансовыми рисками (2): 82–92.
Вестник Банка России. 2015. Сообщения об отзыве лицензий. Центральный банк Российской Федерации. [Электронный ресурс]. http://www.cbr.ru/publ/?prtid=vestnik&PageYear=2015 (дата обращения: 18.09.2016).
Вестник Банка России. 2016. Сообщения об отзыве лицензий. Центральный банк Российской Федерации. [Электронный ресурс]. http://www.cbr.ru/publ/?prtid=vestnik&PageYear=2016 (дата обращения: 03.02.2017).
Воронова Н. С., Мирошниченко О. С. 2017. Региональные банки России: проблемы роста и перспективы развития. Финансы: теория и практика (4): 40–53.
Дробышевский С. М., Зубарев А. В. 2011. Факторы устойчивости российских банков в 2007–2009 годах. Научные труды № 155Р. Институт экономической политики им. Е.Т.Гайдара. М.: Издво Института Гайдара.
Живайкина А. Д., Пересецкий А. А. 2017. Кредитные рейтинги российских банков и отзывы банковских лицензий 2012–2016 гг. Журнал Новой экономической ассоциации (4): 49–80.
Карминский А. М., Костров А. В. 2013. Моделирование вероятности дефолта российских банков: расширенные возможности. Журнал Новой экономической ассоциации (1): 64–86.
Мамонов М. Е. 2011. Влияние кризиса на прибыльность российского банковского сектора. Банковское дело (12): 15–27.
Мамонов М. Е. 2013. О методологии построения опережающих индикаторов. Центр макроэкономического анализа и краткосрочного прогнозирования, Москва. http://www.forecast.ru/SOI/Metodologja/MetSOI_Mamonov.pdf
Мамонов М. Е. 2017. Спрятанные «дыры» в капитале еще не обанкротившихся российских банков: оценка масштаба возможных потерь. Вопросы экономики (7): 42–61.
Обзор банковского сектора Российской Федерации. 2018. Центральный банк Российской Федерации. [Электронный ресурс]. http://www.cbr.ru/analytics/bank_system/obs_183.pdf (дата обращения: 13.02.2018).
Отчет о развитии банковского сектора и банковского надзора в 2006 г. 2007. Центральный банк Российской Федерации. [Электронный ресурс]. http://www.cbr.ru/publ/bsr/bsr_2006.pdf (дата обращения: 28.04.2017).
Отчет о развитии банковского сектора и банковского надзора в 2015 г. 2016. Центральный банк Российской Федерации. [Электронный ресурс]. http://www.cbr.ru/Eng/publ/bsr_e/bsr_e_2015.pdf (дата об ращения: 28.04.2017).
Пересецкий А. А. 2007. Методы оценки вероятности дефолта банков. Экономика и математические методы 43 (3): 37–62.
Пересецкий А. А. 2013. Модели причин отзыва лицензий российских банков. Влияние неучтенных факторов. Прикладная эконометрика (2): 49–64.
Поляков К. Л., Полякова М. В. 2013. Специфика оценки устойчивости коммерческих банков в российских условиях. Вопросы статистики (12): 35–44.
Раскрытие информации кредитными организациями. 2017. Центральный банк Российской Федерации. [Электронный ресурс]. http://www.cbr.ru/credit/transparent.asp (дата обращения: 23.03.2016).
Рудько-Силиванов В. В., Наумов А. А., Якухный Е. М. 2013. Прогнозирование финансовых показателей деятельности кредитных организаций. Деньги и кредит (2): 52–58.
Тотьмянина К. М. 2011. Обзор моделей вероятности дефолта. Управление финансовыми рисками (1): 12–24.
Федорова Е. А., Гиленко Е. В. 2013. Применение моделей бинарного выбора для прогнозирования банкротства банков. Экономика и математические методы 49 (1): 106–118.
Щепелева М. А. 2016. Взаимосвязь корпоративного управления и финансовых рисков: обзор эмпирических исследований. Стратегический менеджмент (1): 94–103.
REFERENCES IN LATIN ALPHABET
Ahmadyan A. 2016. Design of early warning system for predicting exposure to failure time of banks. Quarterly Journal of Applied Theories of Economics 2 (4): 119–144.
Alam P., Booth D., Lee K., Thordarson T. 2000. The use of fuzzy clustering algorithm and selforganizing neural network for identifying potentially failing banks: An experiment study. Expert Systems with Applications 18 (3): 185–199.
Bennett R. L., Unal H. 2014. The effects of resolution methods and industry stress on the loss on assets from bank failures. Journal of Financial Stability 15: 18–31.
Canbas S., Cabuk A., Kilic S. 2005. Prediction of commercial bank failure via multivariate statistical analysis of financial structure: The Turkish case. European Journal of Operational Research 166 (2): 528–546.
Cielen L., Peeters K. 2004. Bankruptcy prediction using a data envelopment analysis. European Journal of Operational Research 154 (2): 526–532.
Coats P., Fant L. 1993. Recognizing financial distress patterns using a neural network tool. Financial Management 22 (3): 142–155.
Compilation Guide of Financial Soundness Indices. 2007. International Monetary Fund. [Electronic resource]. http://www.imf.org/external/index.htm (accessed: 16.10.2017).
DeYoung R., Torna G. 2013. Nontraditional banking activities and bank failures during the financial crisis. Journal of Financial Intermediation 22 (3): 397–421.
Granja J., Matvos G., Seru A. 2017. Selling failed banks. Journal of Finance 72 (4): 1723–1784.
Heckman J. 1979. Sample selection bias as a specification error. Econometrica 47 (1): 153–161.
Huang D.T., Chang B., Liu Z. 2012. Bank failure prediction models: For the developing and developed countries. Quality & Quantity 46 (2): 553–558.
Jagtiani J., Kolari J., Lemieux C., Shin H. 2003. Early warning models for bank supervision: Simper could be better. Economic Perspectives 327 (3): 49–60.
Kaminsky G. L., Reinhart C. M. 1999. The twin crises: The causes of banking and balanceofpayments problems. American Economic Review 89 (3): 473–500.
Karminsky A. M., Kostrov A. V. 2017. The back side of banking in Russia: Forecasting bank failures with negative capital. International Journal of Computational Economics and Econometrics 7 (1/2): 170–209.
Kolari J., Glennon D., Shin H., Caputo M. 2002. Predicting large US commercial bank failures. Journal of Economics and Business 54 (4): 361–387.
Lanine G., Vennet R. V. 2006. Failure prediction in the Russian bank sector with logit and trait recognition models. Expert Systems with Applications 30 (3): 463–478.
Louzada F., FerreiraSilva P. H., Diniz C. A. R. 2012. On the impact of disproportionate samples in credit scoring models: An application to a Brazilian bank data. Expert Systems with Application 39 (9): 8071–8078.
Peresetsky A. A., Karminsky A. M., Golovan S. V. 2011. Probability of default models of Russian banks. Economic Change and Restructuring 44 (4): 297–334.
Saeed M., Izzeldin M. 2016. Examining the relationship between default risk and efficiency in Islamic and сonventional banks. Journal of Economic Behavior and Organization 132 (Special issue on Islamic finance): 127–154.
Wang Y.S., Jiang X., Liu Z.J. 2016. Bank failure prediction models for the developing and developed countries: Identifying the economic value added for predicting failure. Asian Economic and Financial Review 6 (9): 522–533.
Translation of references in Russian into English
Androsov I. A. 2016. Methodological aspects of internal risk control system in small enterprises. Financial Risk Management [Upravlenie Finansovymi Riskami] (2): 82–92. (In Russian)
Bulletin of the Bank of Russia. 2015. Messages on revocation of licenses. The Central Bank of the Russian Federation. [Electronic resource]. http://www.cbr.ru/publ/?prtid=vestnik&PageYear=2015 (accessed: 18.09.2016). (In Russian)
Bulletin of the Bank of Russia. 2016. Messages on revocation of licenses. The Central Bank of the Russian Federation. [Electronic resource]. http://www.cbr.ru/publ/?prtid=vestnik&PageYear=2016 (accessed: 03.02.2017). (In Russian)
Voronova N. S., Miroshnichenko O. S. 2017. Regional banks of Russia: Problems of growth and development prospects. Finance: Theory and practice [Finansy: Teoriya i Praktika] (4): 40–53. (In Russian)
Drobyshevsky S. M., Zubarev A. V. 2011. Sustainability of Russian Banks in 2007–2009. Working paper, issue 155Р. Gaidar Institute for Economic Policy. Moscow: Gaidar Institute Publishing House. (In Russian)
Zhivaikina A. D., Peresetsky A. A. 2017. Russian bank credit ratings and bank license withdrawal 2012–2016. Journal of the New Economic Association [Zhurnal Novoi Ekonomicheskoi Assotsiatsii] (4): 49–80. (In Russian)
Karminsky A. M., Kostrov A. V. 2013. Modeling the default probabilities of Russian banks: Extended abillities. Journal of the New Economic Association [Zhurnal Novoi Ekonomicheskoi Assotsiatsii] (1): 64–86. (In Russian)
Mamonov M. Ye. 2011. The impact of crisis on the profitability of the Russian banking sector. Banking [Bankovskoe Delo] (12): 15–27. (In Russian)
Mamonov M. Ye. E. 2013. On the Methodology of Leading Indicators Construction. Center for Macroeconomic Analysis and ShortTerm Forecasting, Moscow. http://www.forecast.ru/SOI/Metodologja/MetSOI_Mamonov.pdf (In Russian)
Mamonov M. Ye. 2017. Hidden “holes” in the capital of not yet failed banks in Russia: An estimate of the scope of potential losses. The Issues of Economics [Voprosy Ekonomiki] (7): 42–61.
Review of the Banking Sector of the Russian Federation. 2018. The Central Bank of the Russian Federation. [Electronic resource]. http://www.cbr.ru/eng/analytics/bank_system/obs_eng_183.pdf (accessed: 13.02.2018). (English translation)
Banking Supervision Report 2006. 2007. The Central Bank of the Russian Federation. [Electronic resource]. http://www.cbr.ru/Eng/publ/bsr_e/bsr_e_2006.pdf (accessed: 28.04.2017). (English translation)
Banking Supervision Report 2015. 2016. The Central Bank of the Russian Federation. [Electronic resource]. http://www.cbr.ru/Eng/publ/bsr_e/bsr_e_2015.pdf (accessed: 28.04.2017). (English translation)
Peresetsky A. A. 2007. Probability default models for the banks. Economics and Mathematical Methods [Ekonomika i Matematicheskie Metody] 43 (3): 37–62. (In Russian)
Peresetsky A. A. 2013. Modeling reasons for Russian bank license withdrawal: Unaccounted factors. Applied Econometrics [Prikladnaya Ekonometrika] (2): 49–64. (In Russian)
Polyakov K. L., Polyakova M. V. 2013. The specifics of assessing the sustainability of commercial banks in the Russian conditions. The Issues of Statistics [Voprosy Statistiki] (12): 35–44. (In Russian)
On Information Disclosure by Credit Institutions. 2017. The Central Bank of the Russian Federation. [Electronic resource]. http://www.cbr.ru/credit/transparent.asp (accessed: 23.03.2016). (In Russian)
RudkoSilivanov V. V., Naumov A. A., Iakukhniy E. M. 2013. Forecasting financial indicators of the activity of credit organizations. Money and Credit [Dengi i kredit] (2): 52–58. (In Russian)
Totmianina K. M. 2011. Review of models of default probability. Financial Risk Management [Upravlenie Finansovymi Riskami] (1): 12–24. (In Russian)
Fedorova E. A., Gilenko E. V. 2013. The use of binary choice models for predicting bank failures. Economics and Mathematical Methods [Ekonomika i Matematicheskie Metody] 49 (1): 106–118. (In Russian)
Shchepeleva M. A. 2016. Сorporate governance and financial risks: Empirical research overview. Strategic Management [Strategicheskij menedzhment] (1): 94–103. (In Russian)
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Published
2018-07-17
How to Cite
Khasyanova, S. Y., & Tsyganova, V. V. (2018). The Quality of Bank Risk Management: Triggers of Financial Problems. Russian Management Journal, 16(2), 187–204. https://doi.org/10.21638/spbu18.2018.202
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Theoretical and Empirical Studies
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Articles of the Russian Management Journal are open access distributed under the terms of the License Agreement with Saint Petersburg State University, which permits to the authors unrestricted distribution and self-archiving free of charge.
