Survival of Russian bankshow efficient are the control measures?

  1. Angel Barajas 1
  2. Victor Krakovich 2
  3. Felix J. Lopez-Iturriaga 3
  1. 1 Universidade de Vigo
    info

    Universidade de Vigo

    Vigo, España

    ROR https://ror.org/05rdf8595

  2. 2 HSE University Saint Petersburg
  3. 3 Universidad de Valladolid
    info

    Universidad de Valladolid

    Valladolid, España

    ROR https://ror.org/01fvbaw18

Mostrar afiliacións +
Revista:
European journal of management and business economics

ISSN: 2444-8494,  2444-8451

Ano de publicación: 2023

Volume: 32

Número: 3

Páxinas: 320-341

Tipo: Artigo

DOI: 10.1108/EJMBE-12-2021-0329 DIALNET GOOGLE SCHOLAR lock_openAcceso aberto editor

Outras publicacións en: European journal of management and business economics

Resumo

Purpose – In this paper, the authors study the failure of Russian banks between 2012 and 2019. Design/methodology/approach – The authors analyze the entire population of Russian banks and combine a logit model with the survival analysis. Findings – In addition to the usual determinants, the authors find that not-failed banks have higher levels of fulfillment of the Central Bank requirements of solvency, liquidity, provide fewer loans to their shareholders and own more shares of other banks. The results of this study suggest an asymmetric effect of the strategic orientation of banks: whereas the proportion of deposits from firms is negatively related to the probability of failure, the loans to firms are positively related to bankruptcies. According to this research, the fact of being controlled by a foreign bank has a significant negative relationship with the likelihood of failure and moderates the effect of bank size, performance and growth on the bankruptcy likelihood. Practical implications – On the whole, the results of this study support the new Central Bank rules, but show that the thresholds imposed by the Russian regulator actually do not make a difference between failed and not failed banks in the short and medium term. Originality/value – The authors specially focus on the effectiveness of new rules issued by the Central Bank of Russia in 2013.

Referencias bibliográficas

  • Alaka, H.A., Oyedele, L.O., Owolabi, H.A., Kumar, V., Ajayi, S.O., Akinade, O.O. and Bilal, M. (2018), “Systematic review of bankruptcy prediction models: towards a framework for tool selection”, Expert Systems with Applications, Vol. 94, pp. 164-184, doi: 10.1016/j.eswa.2017.10.040.
  • Bellovary, J.L., Giacomino, D.E. and Akers, M.D. (2007), “A review of bankruptcy prediction studies: 1930 to present”, Journal of Financial Education, Vol. 33, pp. 1-42.
  • Belousova, V., Karminsky, A., Myachin, N. and Kozyr, I. (2021), “Bank ownership and efficiency of Russian banks”, Emerging Markets Finance and Trade, Vol. 57 No. 10, pp. 2870-2887, doi: 10.1080/1540496x.2019.1668764.
  • Betz, F., Oprică, S., Peltonen, T.A. and Sarlin, P. (2014), “Predicting distress in European banks”, Journal of Banking and Finance, Vol. 45, pp. 225-241, doi: 10.1016/j.jbankfin.2013.11.041.
  • Bochenkova, P. (2017), “What determines a bank failure? Survival in the Russian banking sector”, Student Undergraduate Research E-journal.
  • Caselli, S., Corbetta, G., Cucinelli, D. and Rossolini, M. (2021), “A survival analysis of public guaranteed loans: does financial intermediary matter?”, Journal of Financial Stability, Vol. 54, 100880, doi: 10.1016/j.jfs.2021.100880.
  • Claeys, S. and Schoors, K. (2007), “Bank supervision Russian style: evidence of conflicts between micro-and macro-prudential concerns”, Journal of Comparative Economics, Vol. 35 No. 3, pp. 630-657.
  • Etudaiye-Muhtar, O.F. and Abdul-Baki, Z. (2021), “Market structure, institutional quality and bank capital ratios: evidence from developing countries”, European Journal of Management and Business Economics, Vol. 30 No. 1, pp. 92-107, doi: 10.1108/ejmbe-09-2019-0158.
  • Fidrmuc, J. and Süss, P. (2011), “The outbreak of the Russian banking crisis”, AUCO Czech Economic Review, Vol. 5 No. 1, pp. 46-64.
  • Fojcik, B. (2019), “Fitch: Russia spent $70B on banking sector cleanup, with more funds still needed”, S&P Global Market Inteligence, 1st April 2019.
  • Fungáčová, Z. and Weill, L. (2013), “Does competition influence bank failures? Evidence from Russia”, Economics of Transition, Vol. 21 No. 2, pp. 301-322.
  • Isik, I. and Uygur, O. (2021), “Financial crises, bank efficiency and survival: theory, literature and emerging market evidence”, International Review of Economics and Finance, Vol. 76, pp. 952-987, doi: 10.1016/j.iref.2021.07.016.
  • Jing, Z. and Fang, Y. (2018), “Predicting US bank failures: a comparison of logit and data mining models”, Journal of Forecasting, Vol. 37 No. 2, pp. 235-256, doi: 10.1002/for.2487.
  • Karas, A. and Vernikov, A. (2019), “Russian bank data: birth and death, location, acquisitions, deposit insurance participation, state and foreign ownership”, Data in Brief, Vol. 27, 104560, doi: 10.1016/j.dib.2019.104560.
  • Karas, A., Schoors, K. and Weill, L. (2010), “Are private banks more efficient than public banks?”, Economics of Transition and Institutional Change, Vol. 18 No. 1, pp. 209-244, doi: 10.1111/j.1468-0351.2009.00364.x.
  • Karminsky, A. and Kostrov, A. (2013), “Modeling the default probabilities of Russian banks: extended abillities”, Journal of the New Economic Association, Vol. 17 No. 1, pp. 64-86.
  • Karminsky, A. and Kostrov, A. (2014), “The probability of default in Russian banking”, Eurasian Economic Review, Vol. 4 No. 1, pp. 81-98, doi: 10.1007/s40822-014-0005-2.
  • Karminsky, A. and Kostrov, A. (2017), “The back side of banking in Russia: forecasting bank failures with negative capital”, International Journal of Computational Economics and Econometrics, Vol. 7 Nos 1-2, pp. 170-209.
  • Konstandina, N.V. (2007), “Measuring efficiency and explaining failures in banking: application to the Russian banking sector”, PhD Dissertation.
  • Kovacova, M., Kliestik, T., Valaskova, K., Durana, P. and Juhaszova, Z. (2019), “Systematic review of variables applied in bankruptcy prediction models of Visegrad group countries”, Oeconomia Copernicana, Vol. 10 No. 4, pp. 743-772.
  • Lanine, G. and Vennet, R.V. (2006), “Failure prediction in the Russian bank sector with logit and trait recognition models”, Expert Systems with Applications, Vol. 30 No. 3, pp. 463-478, doi: 10.1016/j.eswa.2005.10.014.
  • Le, H.H. and Viviani, J.-L. (2018), “Predicting bank failure: an improvement by implementing a machine-learning approach to classical financial ratios”, Research in International Business and Finance, Vol. 44, pp. 16-25, doi: 10.1016/j.ribaf.2017.07.104.
  • Lin, C.-C. and Yang, S.-L. (2016), “Bank fundamentals, economic conditions, and bank failures in East Asian countries”, Economic Modelling, Vol. 52, pp. 960-966, doi: 10.1016/j.econmod.2015.10.035.
  • Mäkinen, M. and Solanko, L. (2018), “Determinants of bank closures: do levels or changes of CAMEL variables matter”, Russian Journal of Money and Finance, Vol. 77 No. 2, pp. 3-21.
  • Malyutina, M. and Parilova, S. (2001), “The determinants of excessive risk-taking by banks in transition”, Economics Education and Research Consortium-Russia and CIS.
  • Mamonov, M.E. (2019), “Depleting net worth of Russian banks: changes in banks' risk-taking and the interest rate policy of the Bank of Russia”, Voprosy Ekonomiki, No. 6, pp. 30-55.
  • Moreno, I., Parrado-Martínez, P. and Trujillo-Ponce, A. (2022), “Using the Z-score to analyze the financial soundness of insurance firms”, European Journal of Management and Business Economics, Vol. 31 No. 1, pp. 22-39, doi: 10.1108/EJMBE-09-2020-0261.
  • Otero, L., Alaraj, R. and Lado-Sestayo, R. (2020), “How corporate governance and ownership affect banks' risk-taking in the MENA countries?”, European Journal of Management and Business Economics, Vol. 29 No. 2, pp. 182-198, doi: 10.1108/ejmbe-01-2019-0010.
  • Peresetsky, A.A., Karminsky, A.A. and Golovan, S.V. (2011), “Probability of default models of Russian banks”, Economic Change and Restructuring, Vol. 44 No. 4, pp. 297-334, doi: 10.1007/s10644-011-9103-2.
  • Pessarossi, P., Thevenon, J.-L. and Weill, L. (2020), “Does high profitability improve stability for European banks?”, Research in International Business and Finance, Vol. 53, 101220, doi: 10.1016/j.ribaf.2020.101220.
  • Petropoulos, A., Siakoulis, V., Stavroulakis, E. and Vlachogiannakis, N.E. (2020), “Predicting bank insolvencies using machine learning techniques”, International Journal of Forecasting, Vol. 36 No. 3, pp. 1092-1113, doi: 10.1016/j.ijforecast.2019.11.005.
  • Studenmund, A.H. (1997), Using Econometrics: A Practical Approach, Addison-Wesley, Boston.
  • Styrin, K. (2005), “X-inefficiency, moral hazard, and bank failures”, 01-258e-2, Economic Education and Research ConsortiumResearch Network, Russia and CIS.
  • The Economist (2017), “A purge of Russia's banks is not finished yet”.
  • Tseng, F.-M. and Hu, Y.-C. (2010), “Comparing four bankruptcy prediction models: logit, quadratic interval logit, neural and fuzzy neural networks”, Expert Systems with Applications, Vol. 37 No. 3, pp. 1846-1853, doi: 10.1016/j.eswa.2009.07.081.
  • Wu, Y., Gaunt, C. and Gray, S. (2010), “A comparison of alternative bankruptcy prediction models”, Journal of Contemporary Accounting and Economics, Vol. 6 No. 1, pp. 34-45, doi: 10.1016/j.jcae.2010.04.002.
  • Zakirova, D.F., Panteleev, D.S. and Zakirova, E.F. (2018), “Estimating bankruptcy probability of credit organizations”, International Transaction Journal of Engineering, Management, and Applied Sciences and Technologies, Vol. 9 No. 4, pp. 307-315, doi: 10.14456/ITJEMAST.2018.27.
  • Zhivaikina, A. and Peresetsky, A. (2017), “Russian bank credit ratings and bank license withdrawal 2012-2016”, Journal of the New Economic Association, Vol. 36 No. 4, pp. 49-80.
  • Zubarev, A. and Bekirova, O. (2020), “Analysis of bank default factors in 2013-2019 [Анализ Факторов Банковских Дефолтов 2013-2019 Годов]”, Economic Policy, Vol. 3, pp. 106-133.
Fonte de datos: Dialnet