References
- 1. Anfilatov, V., Emelyanov, A. & Kukushkin, A. (2003). System analysis in the management [Sistemnyy analiz v upravlenii]. Finance and Statistics, Moscow, 368 p.
- 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), 157–182
- 3. Barros, C. (2016). Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping. European Journal of Operational Research, 249(1), 378-389.
- 4. Barsky, A. (2004). Neural networks: recognition, decision-making [Neyronnyye seti: raspoznavaniye, prinyatiye resheniy]. Finance and Statistics, Moscow, 176 p.
- 5. Borisov, V., Kruglov, V. & Fedulov, A. (2007). Fuzzy models and networks [Nechetkiye modeli i seti]. Hotline - Telecom, Moscow, 284 p.
- 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.
- 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.
- 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.
- 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.2016.11.016
- 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.
- 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.
- 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), 25-33
- 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.
- 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.
- 15. Goncharuk, A. (2016). Banking Sector Challenges in Research. Journal of Applied Management and Investments, 5(1), 34-39.
- 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.
- 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.
- 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.
- 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.485
- 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.2014.03.005
- 21. Leonenkov, F. (2005). Fuzzy modeling in MATLAB environment and fuzzyTECH [Nechetkoye modelirovaniye v srede MATLAB i fuzzyTECH]. BHV, S.Petersburg, 736 p.
- 22. Luburic, R. & Fabris, N. (2017). Money and the Quality of Life. Journal of Central Banking Theory and Practice, 6(3), 17–34
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 29. Ponomarenko, V. & Malyarets, L. (2009). Multivariate analysis of socioeconomic systems [Bahatovymirnyy analiz sotsialʹno-ekonomichnykh system]. KhNUE, Kharkiv, 384 p.
- 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.
- 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.
- 32. Shiryaev, V. (2007). Financial markets and neural networks [Finansovyye rynki i neyronnyye seti]. LKI Publisher, Moscow, 224 p.
- 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.
- 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.
- 35. Yarushkina, N., Afanasyeva, T. & Perfyleva, I. (2010). An intellectual analysis of time series [Intellektual’nyy analiz vremennykh ryadov]. UlSTU, Ulyanovsk, 320 p.
- 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.