IDEAS home Printed from https://ideas.repec.org/a/nea/journl/y2025i66p76-96.html
   My bibliography  Save this article

Dynamic pattern-analysis of the behavior of Russian banks in the period 2017-2021

Author

Listed:
  • Surova, K.

    (International Centre of Decision Choice and Analysis, Moscow, Russia)

  • Aleskerov, F.

    (HSE University, Moscow, Russia
    The Department of Mathematics is a division of the HSE Faculty of Economics, Moscow, Russia)

  • Solodkov, V.

    (HSE University, Moscow, Russia)

  • Sukhov, M.

    (Analytical Credit Rating Agency (ACRA), Moscow, Russia)

  • Chubarova, D.

    (International Centre of Decision Choice and Analysis, Moscow, Russia)

Abstract

The models of behavior of Russian banks in the period before and during the coronavirus pandemic are studied. The time series of indicators according to the CAMEL model in the period 2017-2021 are investigated. As a result of clustering, bank patterns are identified that are identic in characteristics of objects. This approach makes possible to assess the degree of heterogeneity in the development of the banking sector, as well as to identify trends in its development. The analysis of the largest patterns determining the behavioral patterns of banks in the period before and during the pandemic is carried out, for each bank in the sample, the main pattern of behavior for the entire period under consideration is highlighted. A dynamic analysis was carried out to reveal the degree of stability of banks in choosing a business model. The banks that changed the main pattern after the outbreak of the pandemic were identified, that made it possible to comprehensively assess the volatility of the banking system during the financial shock. The dynamics of indicators of the most unstable financial organizations is analyzed.

Suggested Citation

  • Surova, K. & Aleskerov, F. & Solodkov, V. & Sukhov, M. & Chubarova, D., 2025. "Dynamic pattern-analysis of the behavior of Russian banks in the period 2017-2021," Journal of the New Economic Association, New Economic Association, vol. 66(1), pages 76-96.
    • Handle: RePEc:nea:journl:y:2025:i:66:p:76-96
    DOI: 10.31737/22212264_2025_1_76-96
    as

    Download full text from publisher

    File URL: http://www.econorus.org/repec/journl/2025-66-76-96r.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.31737/22212264_2025_1_76-96?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Didier, Tatiana & Huneeus, Federico & Larrain, Mauricio & Schmukler, Sergio L., 2021. "Financing firms in hibernation during the COVID-19 pandemic," Journal of Financial Stability, Elsevier, vol. 53(C).
    2. Goodell, John W., 2020. "COVID-19 and finance: Agendas for future research," Finance Research Letters, Elsevier, vol. 35(C).
    3. Fuad Aleskerov & C. Emre Alper, 2000. "A Clustering Approach to Some Monetary Facts: A Long-Run Analysis of Cross-Country Data," The Japanese Economic Review, Japanese Economic Association, vol. 51(4), pages 555-567, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guo, Wen-Chung & Tseng, Ping-Lun, 2023. "COVID-19, bank risk, and capital regulation: The aggregate shock and social distancing," The Quarterly Review of Economics and Finance, Elsevier, vol. 92(C), pages 155-173.
    2. Srivastava, Jagriti & Sampath, Aravind & Gopalakrishnan, Balagopal, 2022. "Is ESG the key to unlock debt financing during the COVID-19 pandemic? International evidence," Finance Research Letters, Elsevier, vol. 49(C).
    3. Anastasiou, Dimitris & Ballis, Antonis & Drakos, Konstantinos, 2022. "Constructing a positive sentiment index for COVID-19: Evidence from G20 stock markets," International Review of Financial Analysis, Elsevier, vol. 81(C).
    4. Bouteska, Ahmed & Sharif, Taimur & Abedin, Mohammad Zoynul, 2023. "COVID-19 and stock returns: Evidence from the Markov switching dependence approach," Research in International Business and Finance, Elsevier, vol. 64(C).
    5. Heyden, Kim J. & Heyden, Thomas, 2021. "Market reactions to the arrival and containment of COVID-19: An event study," Finance Research Letters, Elsevier, vol. 38(C).
    6. Hale, Galina & Leer, John & Nechio, Fernanda, 2025. "Fiscal policy design and inflation: The COVID-19 pandemic experience," Journal of International Money and Finance, Elsevier, vol. 150(C).
    7. Goodell, John W. & Goutte, Stephane, 2021. "Co-movement of COVID-19 and Bitcoin: Evidence from wavelet coherence analysis," Finance Research Letters, Elsevier, vol. 38(C).
    8. Quan-Hoang Vuong & Huyen Thanh T. Nguyen & Thanh-Hang Pham & Manh-Toan Ho & Minh-Hoang Nguyen, 2021. "Assessing the ideological homogeneity in entrepreneurial finance research by highly cited publications," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-11, December.
    9. Jiang, Jie & Hou, Jack & Wang, Cangyu & Liu, HaiYue, 2021. "COVID-19 impact on firm investment—Evidence from Chinese publicly listed firms," Journal of Asian Economics, Elsevier, vol. 75(C).
    10. Brada, Josef C. & Gajewski, Paweł & Kutan, Ali M., 2021. "Economic resiliency and recovery, lessons from the financial crisis for the COVID-19 pandemic: A regional perspective from Central and Eastern Europe," International Review of Financial Analysis, Elsevier, vol. 74(C).
    11. Pagano, Michael S. & Sedunov, John & Velthuis, Raisa, 2021. "How did retail investors respond to the COVID-19 pandemic? The effect of Robinhood brokerage customers on market quality," Finance Research Letters, Elsevier, vol. 43(C).
    12. Ashok, Shruti & Corbet, Shaen & Dhingra, Deepika & Goodell, John W. & Kumar, Satish & Yadav, Miklesh Prasad, 2022. "Are energy markets informationally smarter than equity markets? Evidence from the COVID-19 experience," Finance Research Letters, Elsevier, vol. 47(PB).
    13. Wang, Yi-Ran & Ma, Chao-Qun & Ren, Yi-Shuai, 2022. "A model for CBDC audits based on blockchain technology: Learning from the DCEP," Research in International Business and Finance, Elsevier, vol. 63(C).
    14. Mahata, Ajit & Rai, Anish & Nurujjaman, Md. & Prakash, Om, 2021. "Modeling and analysis of the effect of COVID-19 on the stock price: V and L-shape recovery," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    15. Galil, Koresh & Varon, Eva, 2024. "National culture and banks stock volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    16. Al-Maadid, Alanoud & Alhazbi, Saleh & Al-Thelaya, Khaled, 2022. "Using machine learning to analyze the impact of coronavirus pandemic news on the stock markets in GCC countries," Research in International Business and Finance, Elsevier, vol. 61(C).
    17. Wang, Fengrong & Mbanyele, William & Muchenje, Linda, 2022. "Economic policy uncertainty and stock liquidity: The mitigating effect of information disclosure," Research in International Business and Finance, Elsevier, vol. 59(C).
    18. Diniz-Maganini, Natalia & Diniz, Eduardo H. & Rasheed, Abdul A., 2021. "Bitcoin’s price efficiency and safe haven properties during the COVID-19 pandemic: A comparison," Research in International Business and Finance, Elsevier, vol. 58(C).
    19. Syed Moudud-Ul-Huq & Md. Shahriar Rahman, 2025. "Stock Market Efficiency of the BRICS Countries Pre-, During, and Post Covid-19 Pandemic: A Multifractal Detrended Fluctuation Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1643-1705, March.
    20. Athira, A. & Ramesh, Vishnu K., 2023. "COVID-19 and corporate tax avoidance: International evidence," International Business Review, Elsevier, vol. 32(4).

    More about this item

    Keywords

    CAMEL model; pattern; pattern analysis; stability of the banking system; dynamic analysis; volatility of the banking system;
    All these keywords.

    JEL classification:

    • C - Mathematical and Quantitative Methods

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nea:journl:y:2025:i:66:p:76-96. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Alexey Tcharykov (email available below). General contact details of provider: https://edirc.repec.org/data/nearuea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.