IDEAS home Printed from https://ideas.repec.org/a/aiy/jnjaer/v22y2023i3p710-738.html
   My bibliography  Save this article

Russian Market of Online Microloans to the Population: Credit Risks Analysis

Author

Listed:
  • Yuliya S. Evlakhova
  • Alexandra A. Tregubova

Abstract

The credit bubble and the accompanying credit risks in the microfinance market could potentially threaten financial stability, as a crisis in the microfinance market could trigger an influx of high-risk clients into the banking sector. The purpose of the study is to assess the level of credit risk that creates threats of financial instability in the microloan market for the population, including its online segment. The hypothesis of the study is that during periods of crisis there are critical deviations of the key characteristics of the credit risk of the microloan market for the population, including its online segment. An original approach to assessing credit risk in the market of microloans to the population is proposed, which includes: (1) selection of indicators of the microfinance market that characterize credit risk at the macrolevel; (2) exponential smoothing models that make it possible to obtain an estimate of the boundaries of the corridor of acceptable values of market indicators. The information base of the study was the official statistical data of the Bank of Russia on the activities of microfinance institutions. The results of the study made it possible to identify the emergence of several "bubbles" of online microloans in the microfinance market: the largest of them was recorded on March 31, 2021, and was due to an increase in the issuance of both online PDL and online IL microloans, the second largest was recorded on June 30, 2022, and was caused exclusively by the growth of online PDL microloans. It was revealed that the level of credit activity of microfinance organizations shifted from high to moderate, while their credit activity did not provoke an excessive increase in credit risks of the microfinance market. In order to timely prevent crises in the microfinance market, the necessity of monitoring the values of outstanding overdue debts of microfinance organizations and interest arrears on microloans issued online IL is substantiated. The practical significance of the study lies in the possibility of using the results obtained in the prudential regulation of the microfinance market, in assessing the risks of the activities of microfinance organizations and their clients – individual borrowers. The theoretical significance of the study lies in the expanding of the analysis of credit risks in the microfinance market through the study of the segment of online microloans.

Suggested Citation

  • Yuliya S. Evlakhova & Alexandra A. Tregubova, 2023. "Russian Market of Online Microloans to the Population: Credit Risks Analysis," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 22(3), pages 710-738.
    • Handle: RePEc:aiy:jnjaer:v:22:y:2023:i:3:p:710-738
    DOI: https://doi.org/10.15826/vestnik.2023.22.3.029
    as

    Download full text from publisher

    File URL: https://journalaer.ru//fileadmin/user_upload/site_15934/2023/10_Evlakhova_Tregubova.pdf
    Download Restriction: no
    File URL: https://libkey.io/https://doi.org/10.15826/vestnik.2023.22.3.029?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. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
    2. Sydney Chikalipah, 2018. "Credit risk in microfinance industry: Evidence from sub-Saharan Africa," Review of Development Finance Journal, Chartered Institute of Development Finance, vol. 8(1), pages 38-48.
    3. Holt, Charles C., 2004. "Author's retrospective on 'Forecasting seasonals and trends by exponentially weighted moving averages'," International Journal of Forecasting, Elsevier, vol. 20(1), pages 11-13.
    4. Spyros Makridakis & Robert L. Winkler, 1983. "Averages of Forecasts: Some Empirical Results," Management Science, INFORMS, vol. 29(9), pages 987-996, September.
    5. Hartoyo & Fermico Karambut & Rita Nurmalina & Mukhamad Najib, 2019. "The Intention in Online Submission of Micro Credit," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 186-200.
    6. Daniel Kandie & Khan Jahirul Islam, 2022. "A new era of microfinance: The digital microcredit and its impact on poverty," Journal of International Development, John Wiley & Sons, Ltd., vol. 34(3), pages 469-492, April.
    7. Nitin Navin & Pankaj Sinha, 2019. "Market Structure and Competition in the Indian Microfinance Sector," Vikalpa: The Journal for Decision Makers, , vol. 44(4), pages 167-181, December.
    8. Lawrence, Michael J. & Edmundson, Robert H. & O'Connor, Marcus J., 1985. "An examination of the accuracy of judgmental extrapolation of time series," International Journal of Forecasting, Elsevier, vol. 1(1), pages 25-35.
    9. Wang, Bo & Yu, Yunjun & Yang, Ziying & Zhang, Xiaomei, 2021. "Microfinance institutions and Peer-to-Peer lending: What does microfinance competition bring?," Pacific-Basin Finance Journal, Elsevier, vol. 67(C).
    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. Theodosiou, Marina, 2011. "Forecasting monthly and quarterly time series using STL decomposition," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1178-1195, October.
    2. J. S. Armstrong & R. Brodie & S. McIntyre, 2005. "Forecasting Methods for Marketing:* Review of Empirical Research," General Economics and Teaching 0502023, University Library of Munich, Germany.
    3. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    4. Zhenni Ding & Huayou Chen & Ligang Zhou, 2023. "Using shapely values to define subgroups of forecasts for combining," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 905-923, July.
    5. Andrea Kolková, 2024. "Data Analysis in Demand Forecasting: A Case Study of Poetry Book Sales in the European Area," Central European Business Review, Prague University of Economics and Business, vol. 2024(5), pages 51-69.
    6. Fieger, Peter & Rice, John, 2016. "Modelling Chinese Inbound Tourism Arrivals into Christchurch," MPRA Paper 75468, University Library of Munich, Germany.
    7. Albrecht, Tobias & Rausch, Theresa Maria & Derra, Nicholas Daniel, 2021. "Call me maybe: Methods and practical implementation of artificial intelligence in call center arrivals’ forecasting," Journal of Business Research, Elsevier, vol. 123(C), pages 267-278.
    8. Krembsler, Jonas & Spiegelberg, Sandra & Hasenfelder, Richard & Kämpf, Nicki Lena & Winter, Thomas & Winter, Nicola & Knappe, Robert, 2024. "Fare revenue forecast in public transport: A comparative case study," Research in Transportation Economics, Elsevier, vol. 105(C).
    9. Kosuke Kawakami & Hirokazu Kobayashi & Kazuhide Nakata, 2021. "Seasonal Inventory Management Model for Raw Materials in Steel Industry," Interfaces, INFORMS, vol. 51(4), pages 312-324, July.
    10. Andrea Kolková & Petr Rozehnal, 2022. "Hybrid demand forecasting models: pre-pandemic and pandemic use studies," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 17(3), pages 699-725, September.
    11. Feng Xu & Mohamad Sepehri & Jian Hua & Sergey Ivanov & Julius N. Anyu, 2018. "Time-Series Forecasting Models for Gasoline Prices in China," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(12), pages 1-43, December.
    12. Theresa Maria Rausch & Tobias Albrecht & Daniel Baier, 2022. "Beyond the beaten paths of forecasting call center arrivals: on the use of dynamic harmonic regression with predictor variables," Journal of Business Economics, Springer, vol. 92(4), pages 675-706, May.
    13. Xi Wu & Adam Blake, 2023. "The Impact of the COVID-19 Crisis on Air Travel Demand: Some Evidence From China," SAGE Open, , vol. 13(1), pages 21582440231, January.
    14. Veiga, Claudimar Pereira da & Veiga, Cássia Rita Pereira da & Puchalski, Weslly & Coelho, Leandro dos Santos & Tortato, Ubiratã, 2016. "Demand forecasting based on natural computing approaches applied to the foodstuff retail segment," Journal of Retailing and Consumer Services, Elsevier, vol. 31(C), pages 174-181.
    15. Hill, Arthur V. & Zhang, Weiyong & Burch, Gerald F., 2015. "Forecasting the forecastability quotient for inventory management," International Journal of Forecasting, Elsevier, vol. 31(3), pages 651-663.
    16. Zhen Zeng & Rachneet Kaur & Suchetha Siddagangappa & Saba Rahimi & Tucker Balch & Manuela Veloso, 2023. "Financial Time Series Forecasting using CNN and Transformer," Papers 2304.04912, arXiv.org.
    17. Maia, André Luis Santiago & de Carvalho, Francisco de A.T., 2011. "Holt's exponential smoothing and neural network models for forecasting interval-valued time series," International Journal of Forecasting, Elsevier, vol. 27(3), pages 740-759, July.
    18. Ye, Lili & Xie, Naiming & Boylan, John E. & Shang, Zhongju, 2024. "Forecasting seasonal demand for retail: A Fourier time-varying grey model," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1467-1485.
    19. Wang, Zheng-Xin & Wang, Zhi-Wei & Li, Qin, 2020. "Forecasting the industrial solar energy consumption using a novel seasonal GM(1,1) model with dynamic seasonal adjustment factors," Energy, Elsevier, vol. 200(C).
    20. Isra Al-Turaiki & Fahad Almutlaq & Hend Alrasheed & Norah Alballa, 2021. "Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia," IJERPH, MDPI, vol. 18(16), pages 1-19, August.

    More about this item

    Keywords

    microfinance market; online microloans; risk; credit risk; exponential smoothing; risk assessment; financial instability.;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

    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:aiy:jnjaer:v:22:y:2023:i:3:p:710-738. 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: Natalia Starodubets (email available below). General contact details of provider: https://edirc.repec.org/data/seurfru.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.