Assessing the stability of the banking system based on fuzzy logic methods

  • Received August 17, 2020;
    Accepted September 29, 2020;
    Published October 7, 2020
  • Author(s)
  • DOI
  • Article Info
    Volume 15 2020, Issue #3, pp. 171-183
  • Cited by
    9 articles

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This work is licensed under a Creative Commons Attribution 4.0 International License

The functioning of the country’s banking system is the basis for ensuring its economic development and stability. The state of the banking system often causes financial crises; therefore, ensuring its stable work is one of the main tasks of monetary policy. Meanwhile, it is important to find approaches to a comprehensive assessment and forecasting of the stability of the banking system that would allow obtaining adequate results.
Based on a sample of data generated for the period from 2008 to the 1st quarter of 2020 with a quarterly breakdown, an integrated stability index of Ukraine’s banking system was estimated. The analysis was based on 23 variables that characterize certain aspects of the functioning of the Ukrainian banking system.
Using the principal component analysis, five factors have been identified that have the greatest impact on ensuring the stability of the banking system. They were used to form an integrated index based on the application of the Mamdani fuzzy logic method. The results obtained adequately reflected the state of stability of the banking system for the analyzed period, which coincided in time with the crisis phenomena occurring in the Ukrainian banking system. The obtained value of the integrated index characterizes the stability of Ukraine’s banking system at the average level, since it depends not only on the internal state of the system, but also on the influence of external factors, both national and international.

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    • Figure 1. The results of tests for the normality of the analyzed variables (fragment)
    • Figure 2. The Mamdani model constructed
    • Figure 3. Dynamics of the integrated index of Ukraine’s banking system stability and the polynomial trend calculated by the Mamdani fuzzy logic method
    • Table 1. Results of factor analysis by the principal component method
    • Table A1. Descriptive statistics
    • Table B1. Correlation matrix of analyzed variables
    • Table C1. Factor scores in each of the 49 quarters
    • Conceptualization
      Ivan S. Blahun
    • Funding acquisition
      Ivan S. Blahun, Ivan I. Blahun
    • Methodology
      Ivan S. Blahun
    • Project administration
      Ivan S. Blahun
    • Supervision
      Ivan S. Blahun
    • Writing – original draft
      Ivan S. Blahun
    • Data curation
      Ivan I. Blahun, Semen I. Blahun
    • Formal Analysis
      Ivan I. Blahun, Semen I. Blahun
    • Investigation
      Ivan I. Blahun
    • Validation
      Ivan I. Blahun
    • Visualization
      Ivan I. Blahun
    • Writing – review & editing
      Ivan I. Blahun
    • Software
      Semen I. Blahun