Abstract

The article analyzes the changes in the financial condition of the banking system during the last years, during the period of intensive reduction of the banking services market. The author has established a stable distribution of the banking services market for homogeneous groups throughout the research period, the features of business models and the risk profiles of banks of each group have been identified. An assessment of the financial stability of each bank is proposed to be carried out based on an analysis of the bank’s trajectory on the Kohonen self-organizing map constructed during the research period.

Structural-functional analysis of the banking system is intended for the analysis of gradual structural changes in comparison with the previous reporting periods, the identification of specific characteristics of each group of banks, the relationship between groups and the place of each bank. This analysis allowed to identify the main problematic aspects of banks’ activities, which affected the deterioration of financial stability of the system and individual banks.

Keywords: structural-functional analysis, finanсial system indicators services, stability of banking system, self-organizing map.

JEL Classification: G21.

Cite as: Zarutska El. (2018). Structural-functional analysis of the Ukraine banking system. Financial Markets, Institutions and Risks, 2(1), 79-96.

References

1. European Central Bank, ECB (2014). Guide to banking supervision. Available at: https://www. bankingsupervision.europa.eu/ecb/pub/pdf/ssmguidebankingsupervision201411.en.pdf?807838fa2a8bb958749f411c432d1f3e
2. European Central Bank, ECB (2017). SSM SREP Methodology Booklet. Available at: https://www. bankingsupervision.europa.eu/ecb/pub/pdf/ssm.srep_methodology_booklet_2017.en.pdf?508ca0e386f9b91369820bc927863456
3. Basel Committee on Banking Supervision, BCBS (2000). Principles for the Management of Credit Risk. Available at: http://www.bis.org/publ/bcbs75.pdf
4. Basel Committee on Banking Supervision, BCBS (2008). Principles for sound liquidity risk management and supervision. Available at: http://www.bis.org/publ/bcbs144.pdf
5. National Bank of Ukraine, NBU (2018). Shock Contagion, Asset Quality and Lending Behavior. Available at: https://bank.gov.ua/doccatalog/document?id=62899125
6. National Bank of Ukraine, NBU (2017). Secular Stagnation: Policy Options and the Cyclical Sensitivity in Estimates of Potential Output. Available at: https://bank.gov.ua/doccatalog/document?id=48800101
7. Altunbas, Y., Manganelli, S., and Marques-Ibanez, D., (2011). Bank Risk during the Financial Crisis – Do Business Models Matter?, Working Paper Series, No. 1394, Frankfurt am Main, Germany: European Central Bank.
8. Kohonen, T. (2001). Self-Organizing Maps. Berlin Heidelberg: Springer-Verlag.
9. Kohonen, T (2013). Essentials of the self-organizing map. Neural Networks, 37, 52-65.
10. Severin. E. (2010). Self organizing maps in corporate finance: Quantitative and qualitative analysis of debt and leasing, Neurocomputing, 73 (10-12), 2061-2067.
11. AghaeiRad, A., Chen, N. and Ribeiro, B. (2017). Improve credit scoring using transfer of learned knowledge from self-organizing map, Neural Computing & Applications, 28 (6), 1329-1342.
12. Tkac, M. and Verner, R. (2015). Artificial neural networks in business: Two decades of research, Applied Soft Computing, 38, 788-804.
13. Sarlin, P and Peltonen, T. (2013). Mapping the state of financial stability» Journal Of International Financial Markets Institutions & Money, 26, 46-76.
14. Severin, E (2012). Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time» European Journal Of Operational Research, 221 (2), 378-396.
15. Rutherford, M.W. (2016). Proposing a Financial Legitimacy Threshold in Emerging Ventures: A Multi-Method Investigation. Group & Organization Management, 41, (6), 751-785.
16. Koc, Elcin K et al. (2014). Efficient adaptive regression spline algorithms based on mapping approach with a case study on finance. Journal Of Global Optimization, 60(1), 103-120.
17. Jarvinen, J., Linnakangas, J (2012) Firm Capabilities in the Finnish Forest Cluster: Comparisons Based on Self-Organizing Map. Silva Fennica, 46(1), 131-150.
18. Lin, W.Y., Hu, Y.H. and Tsai, C.F. (2012). «Machine Learning in Financial Crisis Prediction: A Survey». Ieee Transactions On Systems Man And Cybernetics Part C-Applications And Reviews, 42(4), 421-436.
19. Hsu, C.M. (2012). A hybrid procedure for stock price prediction by integrating self-organizing map and genetic programming, Expert Systems With Applications, 38(11), 14026-14036
20. Ayadi R.W et al. (2015). Banking business model monitor 2015: Europe. Centre for European Policy Studies and International Observatory on Financial Services Cooperatives. Available at: https://www.ceps.eu/system/files/Banking-Business-Models-Monitor-Europe-2015.pdf
21. Mergaerts, F and Vennet R. (2015). Business models and bank performance. A long-term perspective. 4th EBA Policy Research Workshop. Nov. 18 . Available at: https://www.eba.europa. eu/documents/ 10180/1018121/Mergaerts%2C%20Vander+Vennet++Business+models+and+bank+performance.+A+long+term+perspective+-+Paper.pdf.
22. Beltratti, A. and Stulz, R.M., (2012). The credit crisis around the globe: Why did some banks perform better? Journal of Financial Economics, 105(1), 1-17.
23. Huang, X., Zhou H. and Zhu H. (2011). Systemic Risk Contributions, Federal Reserve Board Finance and Economics Discussion Series 2011-08.
24. Drehmann, M. and Tarashev, N. (2011). Systemic Importance: Some Simple Indicators, Bank for International Settlements Quarterly Review, Bank for International Settlements, March.
25. Brownlees, C.T. and Engle R.F. (2010). Volatility, Correlation and Tails for Systemic Risk Measurement. Available at SSRN: http://ssrn.com/abstract=1611229.
26. Boot, A. and Thakor, A.V. (2010). The Accelerating Integration of Banks and Markets and its Implications for Regulation, in A. Berger, P. Molyneux and J. Wilson (eds.), The Oxford Handbook of Banking, 58-90.
27. Bekaert, G., Ehrmann, M., Fratzscher, M. and Mehl, A. (2011). Global Crises and Equity Market Contagion. Available at: http://ssrn.com/abstract=1856881.
28. Acharya, V., Cooley T., Richardson M. and Walter I. (2010). Manufacturing Tail Risk: A Perspective on the Financial Crisis of 2007-09, Foundations and Trends in Finance, 4, 247-325.
29. Berger, A. and Bouwman C. (2010). How Does Capital Affect Bank Performance During Financial Crises? Wharton Financial Institutions Center Working Paper, 11-22.
30. De Jonghe, O. (2010). Back to the Basics in Banking? A Micro-analysis of Banking System Stability, Journal of Financial Intermediation, 19(3), 387-417.
31. Maddaloni, A. and Peydró J.L. (2011). Bank Risk Taking, Securitization, Supervision, and Low Interest Rates: Evidence from Lending Standards, Review of Financial Studies, 24(6), 2121-2165.
32. Baele, L., De Jonghe, O., and Vander Vennet, R. (2007). Does the stock market value bank diversification? Journal of Banking and Finance, 31(7), 1999-2023.
33. Hasan, I., Lozano-Vivas, A. and Pastor, J.T. (2001). European bank performance beyond country borders: what really matters? European Finance Review, 5(2), 141-165.
34. Billio, M., Getmansky, M., Lo, A.W. and Pelizzon, L. (2012). Econometric measures of connectedness and systemic risk in the finance and insurance sectors, Journal of Financial Economics, 104(3), 535-559.
35. Brandt, M.W., Brav, A., Graham, J. and Kumar, A. (2010). The idiosyncratic volatility puzzle: time trend or speculative episodes? Review of Financial Studies, 23(2), 863-899.
36. Coughlan, J., Shale, E. and Dyson, R. (2010). Including the customer in efficiency analysis: evidence of a hybrid relational-transactional approach”, International Journal of Bank Marketing, 28(2), 136-149.
37. Greenwood, R., Landier, A. and Thesmar, D. (2015). Vulnerable banks, Journal of Financial Economics, 115(3), 471-485.
38. Hays, F., de Lurgio, S. and Gilbert, A. (2009). Efficiency ratios and community bank performance, Journal of Finance and Accountancy, 1(1), 1-15.
39. Paleologo, G., Elisseeff, A. and Antonini, G. (2010). Subagging for credit scoring models”, European Journal of Operational Research, 201(2), 490-499.
40. Petersen, M. (2009). Estimating standard errors in finance panel data sets: comparing approaches, Review of Financial Studies, 22(1), 435-480.
41. Pistol, G. (2010). The role and importance of the strategic planning in bank marketing”, Аnnals of Spiru Haret University. Economic Series, 1(2), 153-161.
42. Zheng, D., Zhang, Y. and Wen, L. (2012). The credibility models with time changeable effects, Journal of Jiangxi Normal University: Natural Science Edition, 36(1), 249-252.
43. Tomkus, M. (2014). Identifying Business Models of Banks: Analysis of Biggest Banks from Europe and United States of America. Aarhus University: Business and Social Science. Available at: http://pure.au.dk/ portal/files/69715984/be_apendixu.pdf
44. Köhler M. (2015). Which banks are more risky? Th e impact of business models on bank stability, Journal of Financial Stability, 16, 195-212.
45. Laeven L. (2013)/ Corporate governance: What’s special about banks? Annual Review of Financial Economics, 5, 63-92
46. Roengpitya, R. (2014). Bank business models, Bank for International Settlements. Available at: http://www.imf.org/external/pubs/ft/sdn/2014/sdn1404.pdf