Obtaining a Generalized Index of Bank Competitiveness Using a Fuzzy Approach

1. Anfilatov, V., Emelyanov, A. & Kukushkin, A. (2003). System analysis in the management [Sistemnyy analiz v upravlenii]. Finance and Statistics, Moscow, 368 p.Search in Google Scholar

2. Asanovic, Z. (2017). Predicting Systemic Banking Crises Using Early Warning Models: The Case of Montenegro. Journal of Central Banking Theory and Practice, 6(3), 157–18210.1515/jcbtp-2017-0025Search in Google Scholar

3. Barros, C. (2016). Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping. European Journal of Operational Research, 249(1), 378-389.10.1016/j.ejor.2015.10.018Search in Google Scholar

4. Barsky, A. (2004). Neural networks: recognition, decision-making [Neyronnyye seti: raspoznavaniye, prinyatiye resheniy]. Finance and Statistics, Moscow, 176 p.Search in Google Scholar

5. Borisov, V., Kruglov, V. & Fedulov, A. (2007). Fuzzy models and networks [Nechetkiye modeli i seti]. Hotline - Telecom, Moscow, 284 p.Search in Google Scholar

6. Borisov, A. &, Krumberg, O.(1990). Decision-making based on fuzzy models: examples of use [Prinyatiye resheniy na osnove nechetkikh modeley: primery]. Zinatne, Riga, 184 p.Search in Google Scholar

7. Braendle, U. & Sepasi, S. (2014). Fuzzy Evaluation Of Service Quality In The Banking Sector: A Decision Support System. Fuzzy Economic Review, 10(2), 47-79.10.25102/fer.2014.02.04Search in Google Scholar

8. Cveshnikov, C. & Bocharnikov, V. (2007). Fexcel program to work with fuzzy numbers in an environment MSExcel version 4.0 [Programma Fexcel dlya raboty s nechetkimi chislami v srede MSExcel versiya 4.0]. INEX Consulting Group, Kiev, 60 p.Search in Google Scholar

9. Degl’Innocenti, M., Kourtzidis, S., Sevic, Z. & Tzeremes, N. (2017). Bank productivity growth and convergence in the European Union during the financial crisis. Journal of Banking & Finance, 75(C), 184-199.10.1016/j.jbankfin.2016.11.016Otwórz DOISearch in Google Scholar

10. Dolgikh, V. (2016). Non-parametric estimation of efficiency of the Ukrainian banking system” [“Neparametrychni otsinky efektyvnosti ukrayinsʹkoyi bankivsʹkoyi systemy”]. Proceedings of the National Bank of Ukraine, 2(204), 29–35.Search in Google Scholar

11. Dorokhov, O., Chernov, V., Dorokhova, L. & Streimkis, J. (2018). Multicriteria choice of alternatives under fuzzy information. Transformations in Business and Economics, 17(2), 95-106.Search in Google Scholar

12. Dorokhov, O. & Dorokhova, L. (2011). Fuzzy model in fuzzy-tech environment for the evaluation of transportation’s quality for cargo enterprises in Ukraine. Transport and Telecommunication, 12(1), 25-33Search in Google Scholar

13. Fabris, N. & Vujanovic, N. (2017). The Impact of Financial Dollarization on Inflation Targeting: Empirical Evidence from Serbia. Journal of Central Banking Theory and Practice, 6(2), 23–43.10.1515/jcbtp-2017-0011Search in Google Scholar

14. Fernando, A., Ferreira, F., Jalali, M., Ferreira, J., Stankevičienė, J. & Marques, C. (2016), Understanding the dynamics behind bank branch service quality in Portugal: pursuing a holistic view using fuzzy cognitive mapping. Service Business, 10(3), 469-487.Search in Google Scholar

15. Goncharuk, A. (2016). Banking Sector Challenges in Research. Journal of Applied Management and Investments, 5(1), 34-39.Search in Google Scholar

16. Hooman, A., Marthandan, G., Yusoff, W. & Omid, M. (2016). Statistical and data mining methods in credit scoring. Journal of Developing Areas, 50(5), 371-381.10.1353/jda.2016.0057Search in Google Scholar

17. Koybichuk, V. (2013). Formation of models for features space of bank competitiveness [Formuvannya oznakovoho prostoru modeli konkurentospromozhnosti banku]. Bulletin of Khmelnytsky National University, 4(1), 173–179.Search in Google Scholar

18. Koybichuk, V. (2012). Conceptual model of the bank’s competitiveness in modern conditions [Kontseptualʹna modelʹ konkurentosti banku v suchasnykh umovakh]. Bulletin of the University of Banking of National Bank of Ukraine, 2(14), 323–329.Search in Google Scholar

19. Kukal, J. & Vanquang, T. (2014). A Monetary Policy Rule Based on Fuzzy Control in an Inflation Targeting Framework. Prague Economic Papers, 2014(3), 290-314.10.18267/j.pep.485Otwórz DOISearch in Google Scholar

20. Lapteacru, I. (2014). Do more competitive banks have less market power? The evidence from Central and Eastern Europe. Journal of International Money and Finance, 46, 41-60.10.1016/j.jimonfin.2014.03.005Otwórz DOISearch in Google Scholar

21. Leonenkov, F. (2005). Fuzzy modeling in MATLAB environment and fuzzyTECH [Nechetkoye modelirovaniye v srede MATLAB i fuzzyTECH]. BHV, S.Petersburg, 736 p.Search in Google Scholar

22. Luburic, R. & Fabris, N. (2017). Money and the Quality of Life. Journal of Central Banking Theory and Practice, 6(3), 17–3410.1515/jcbtp-2017-0019Search in Google Scholar

23. Malyaretz, L., Dorokhov, O. & Dorokhova, L. (2018).Method of constructing the fuzzy regression model of bank competitiveness. Journal of Central Banking Theory and Practice, 7(2), 139-160.10.2478/jcbtp-2018-0016Search in Google Scholar

24. Mandic, K., Delibasic, B., Knezevic, S.& Benkovic, S. (2014). Analysis of the financial parameters of Serbian banks through the application of the fuzzy AHP and TOPSIS methods. Economic Modelling, 43(C), 30-37.10.1016/j.econmod.2014.07.036Search in Google Scholar

25. Matviychuk, A. (2005), Analysis and forecasting of financial and economic systems with the use of fuzzy logic [Analiz ta prohnozuvannya rozvytku finansovo-ekonomichnykh system iz vykorystannyam teoriyi nechitkoyi lohiky]. Center of educational literature, Kiev, 209 p.Search in Google Scholar

26. Menicucci, E., Paolucci, G., Zain, M. & Rasit, Z. (2016). The determinants of bank profitability: empirical evidence from European banking sector. Journal of Financial Reporting and Accounting, 14(1), 86-115.10.1108/JFRA-05-2015-0060Search in Google Scholar

27. Omelchenko, O., Dorokhov, O., Kolodiziev, O. & Dorokhova, L. (2018). Fuzzy modeling of the creditworthiness assessments of bank’s potential borrowers in Ukraine. Ikonomicheski Izsledvania (Economic Studies), 27(4), 100-125.Search in Google Scholar

28. Ponomarenko, V., Gontareva, I. & Dorokhov, O. (2014). Statistical testing of key effectiveness indicators of the companies (Case for Ukraine in 2012). Ikonomicheski Izsledvania (Economic Studies), 23(4), 108-124.Search in Google Scholar

29. Ponomarenko, V. & Malyarets, L. (2009). Multivariate analysis of socioeconomic systems [Bahatovymirnyy analiz sotsialʹno-ekonomichnykh system]. KhNUE, Kharkiv, 384 p.Search in Google Scholar

30. Rezaei, M. & Ketabi, S. (2016). Ranking the Banks through Performance Evaluation by Integrating Fuzzy AHP and TOPSIS Methods. International Journal of Academic Research in Accounting, Finance and Management Sciences, 6(3), 19-30.10.6007/IJARAFMS/v6-i3/2148Search in Google Scholar

31. Rothstein, A. (1999). Intelligent identification technology: fuzzy sets, genetic algorithms, neural networks [Intellektual’nyye tekhnologi identifikatsii: nechetkiye mnozhestva, geneticheskiye algoritmy, neyronne seti]. Universum, Vinnitsa, 320 p.Search in Google Scholar

32. Shiryaev, V. (2007). Financial markets and neural networks [Finansovyye rynki i neyronnyye seti]. LKI Publisher, Moscow, 224 p.Search in Google Scholar

33. Shtovba, S. (2006). Construction of functions of fuzzy sets for clustering experimental data [Pobudova funktsiy nalezhnosti nechitkykh mnozhyn klasteryzatsiyeyu eksperymentalʹnykh danykh]. Information technologies and computer engineering, 2, 92–95.Search in Google Scholar

34. Yarushkina, N. & Afanasyeva, T. (2007). Fuzzy time series as a tool for assessing and measuring the dynamics of the processes [Nechetkiye vremennyye ryady kak instrument dlya otsenki i izmereniya dinamiki protsessov]. Sensors and Systems, 12, 46–51.Search in Google Scholar

35. Yarushkina, N., Afanasyeva, T. & Perfyleva, I. (2010). An intellectual analysis of time series [Intellektual’nyy analiz vremennykh ryadov]. UlSTU, Ulyanovsk, 320 p.Search in Google Scholar

36. Yarushkina, N. (2004). Fundamentals of the theory of fuzzy and hybrid systems [Osnovy teorii nechetkikh i gibridnykh sistem]. Finance and Statistics, Moscow, 320 p.Search in Google Scholar

• Sustainability of the Currency Board in Bosnia and Herzegovina in the Conditions of a Negative Interest Rate on the Reserve Currency
• Bank Regulation in the Selected Sub-Saharan African Countries: Dynamics and Trends
• Revisiting of Interest Rate Channel: Nonlinear transmission of Monetary Policy Shocks to the Turkish Economy
• Testing the Factors that Determine the Profitability of Banks with a Dynamic Approach: Evidence from Turkey
• Money Supply Determination Process for Japan
• Effects of Exchange Rate, Output Gap, and Output Gap Volatility on Inflation Volatility in Turkey
• Are Gold and Bitcoin a Safe Haven for European Indices?
• Customer Satisfaction and Switching Intentions of Banking Services End-Users in Montenegro
• Does Monetary Policy Solely Correct Disequilibrium in the Balance of Payment? Evidence From the Developing World
• Resilience and Path Dependency: Income Distribution Effects of GDP in Colombia
• Does Fiscal Transparency Matter for Bank Development? A Lookup on Emerging and Developing Countries