References
- Aliber, R.Z., Kindleberger, C.P. & McCauley, R.N. (2023) Manias, Panics and Clashes: A history of Financial Crisis. Pal-grave MacMillian, New York.
- Alkan, H. (2016). 15 Temmuz’u Anlamak: Parametreler ve Sonuçlar. Bilig, 79, 253-272.
- Aoki, M. (2001). Modeling Aggregate Behavior and Fluctuations in Economics: Stochastic Views of Interacting Agents. Cambridge University Press, Cambridge.
- Benhabib, J. (1992). Cycles and Chaos in Economic Equilibrium. Princeton University Press, New Jersey.
- Bernanke, B.S., Geithner, T.F. & Paulson, H.M. (2019). Firefighting: The Financial Crisis and Its Lesson. Penguin Books, New York.
- Büyükakın, F. & Bilal, E. (2016). Finansal Krizlere Karşı Sukukun Uygulanabilirliği Üzerine bir Değerlendirme. Finansal Araştırmalar ve Çalışmalar Dergisi, 8(14), 33-55.
- Cao, D.Z., Pang, S.L. & Bai, Y.H. (2005). Forecasting Exchange Rate Using Support Vector Machines. Proceedings of 2005 International Conference on Machine Learning and Cybernetics, 1, 3448-3452.
- Çakı, F. (2018). Türkiye’de 15 Temmuz’un Toplumsal Etkileri ve Ona Yol Açan Faktörler Üzerine Düşünceler. Akademik İncelemeler Dergisi, 13(1), 91-124.
- Cohen, B. (1997). The Edge of Chaos: Financial Booms, Bubbles, Crashes and Chaos. Wiley, New Jersey.
- Coskun, Ö.A. & Eken, M.H. (2015). 2001 ve 2008 Krizlerinin Türk Bankacılık Sektörüne Etkilerinin Karşılaştırılması. Maliye ve Finans, 104, 105-130.
- Dakhlaoui, I. & Aloui, C. (2013). The US Oil Spot Market: A Deterministic Chaotic Process or Stochastic Process? Journal of Energy Markets, 6(1), 51-93.
- Dusza, M. (2017). Financial chaos. Management Issues, 15(2), 169-189.
- Dzhagityan, E.P. (2017). Macroprudential Policy in Post-crisis Banking Regulation. World Economy and International Relations, 61(11), 13-23.
- Filip, A., Pochea, M. & Pece, A. (2015). The Herding Behaviour of Investors in the CEE Stocks Markets. Procedia Economics and Finance, 32, 307-315.
- Frank, E., Hall, M.A. & Witten, I.H. (2022). The WEKA Workbench”, Online Appendix for “Data Mining: Practical Machine Learning Tools and Techniques”. https://www.cs.waikato.ac.nz/ml/weka/Witten_et_al_2016_appendix.pdf (Accessed: 18.07.2024).
- Gaffeo, E. & Molinari, M. (2017). Taxing Financial Transactions in Fundamentally Heterogeneous Markets. Economic Modeling, 64(1), 322-333.
- Gassouma, M.S., Benhamed, A. & El Montasser, G. (2023). Investigating Similarities between Islamic and Conventional Banks in GCC countries: A Dynamic Time Warping Approach”, International Journal of Islamic and Middle Eastern Finance and Management, 16(1), 103-129.
- Garel, A. & Petit-Romec, A. (2017). Bank Capital in the Crisis: It’s not just How Much You Have but Who Provides it. Journal of Banking and Finance, 75, 152-166.
- Gennaioli, N. & Shleifer, A. (2018). A Crisis of Beliefs: Investor Psychology and Financial Fragility. Princeton University Press, New Jersey.
- Gilmore, C.G. (1993). A New Test for Chaos. Journal of Economic Behavior and Organization, 22(2), 209-237.
- Gökalp, F. (2014). Kriz Sonrası Dönemler İtibariyle Katılım Bankaları ve Ticari Bankaların Karlılığı Üzerine Karşılaştırmalı bir Araştırma. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 32, 191-201.
- Grosse, R. (2017). The global financial crisis-Market misconduct and regulation from a behavioral view. Research in International Business and Finance, 41, 387–398.
- Hernandez, J.M. & Mendoza, E.G. (2017). Optimal v. Simple Financial Policy Rules in a Production Economy with “liability dollarization”. Ensayos Sobre Politica Economica, 35(82), 25-39.
- Hlaing, S.W. & Kakinaka, M. (2018). Financial Crisis and Financial Policy Reform: Crisis Origins and Policy Dimensions. European Journal of Political Economy, 55, 224-243.
- Hoffmann, A.O.I., Post, T. & Pennings, J.M.E. (2013). Individual Investor Perceptions and Behavior during the Financial Crisis. Journal of Banking & Finance, 37, 60–74.
- IMF. (1998). Financial Crises: Characteristics and Indicators of Vulnerability”. In World Economic Outlook. International Monetary Fund, Washington.
- Kara, A. (2001). On the Efficiency of the Financial Institutions of Profit-and-Loss-Sharing”. Journal of Economic & Social Research, 3(2), 99-104.
- Kara, A. (2007). Discrete Stochastic Dynamics of Income Inequality in Education”. Discrete Dynamics in Nature and Society, 2007(1), 1-15.
- Kara, A. (2009). Implications of Multiple Preferences for a Deconstructive Critique and a Reconstructive Revision of Economic Theory. Journal of Economic & Social Research, 11(1), 69-78.
- Kara, A. (2013). Dynamics of Education and Technology in Higher Education. Hacettepe Journal of Mathematics and Statistics, 42(1), 87-99.
- Kara, A. (2023). Stabilizing Instability-Suboptimality-and-Chaos-Prone Fluctuations at Crisis Junctures: Stochastic Possibilities for Crisis Management. International Journal of Finance & Economics, 28(2), 1772-1786.
- Kara, A. & Osman, M. (2006). Dynamic Equilibria in the US Banking Sector: A Model and a Case Study. Journal of Economic and Social Research, 8(2), 43-52.
- Kariofyllas, S., Philippas, D. & Siriopoulos, C. (2017). Cognitive Biases in Investors’ Behaviour under Stress: Evidence from the London Stock Exchange. International Review of Financial Analysis, 54, 54–62.
- Kassem, B. & Saleh, M. (2005). Simulating a Banking Crisis Using a System Dynamics Model. Egyptian Informatics Journal, 6(2), 125-145.
- Katılım Bankaları Birliği. (2023). https://veripetegi.tkbb.org.tr. (Accessed: 18.07.2024).
- Klein, P.O., Turk, R. & Weill, L. (2017). Religiosity vs. Well-Being Effects on Investor Behavior. Journal of Economic Behavior & Organization, 138, 50-62.
- Korol, T. & Fodadis, A.K. (2022). Implementing Artificial Intelligence in Forecasting the Risk of Personal Bankruptcies in Poland and Taiwan. Oeconomia Copernicana, 13(2), 407-438.
- Kyrtsou, C. & Terraza, M. (2002). Stochastic Chaos or ARCH Effects in Stock Series? A Comparative Study. International Review of Financial Analysis, 11(4), 407-431.
- Liang, Y. (2012). Global Imbalances and Financial Crisis: Financial Globalization as a Common Cause. Journal of Economic Issues, 46(2), 353-362.
- Lin, W.K., Lin, S.J. & Yang, T.N. (2017). Integrated Business Prestige and Artificial Intelligence in Dynamic Environments. Cybernetics and Systems, 48(4), 303-324.
- Minsky, H.P. (2008). Stabilizing Unstable Economy, McGraw-Hill, New York.
- Purica, I. (2015). Nonlinear Dynamics of Financial Crises: How to Predict Discontinuous Decisions. Academic Press, New York.
- Racickas, E. & Vasiliauskaite, A. (2012). Classification of Financial Crises and their Occurrence Frequency in Global Financial Markets. Social Research, 4(29), 32-44.
- Reinhart, C.M. & Rogoff, K. S. (2014). Recovery from financial crises: Evidence from 100 episodes. American Economic Review, 104(5), 50-55.
- Sabah. (2016). Bank Asya Kapandı, https://www.sabah.com.tr/ekonomi/2016/07/19/bank-asya-kapandi. (Accessed: 18.07.2024).
- Saraç, M. & Zeren, F. (2015). The dependency of Islamic Bank Rates on Conventional Bank Interest Rates: Further Evidence from Turkey. Applied Economics, 47(7), 669-679.
- Sau, L. (2013). Instability and Crisis in Financial Complex Systems. Review of Political Economy, 25(3), 496-511.
- Scazzieri, R. (2018). Structural Dynamics and Evolutionary Change. Structural Change and Economic Dynamics, 46, 52-58.
- Scholten, D.G.G. (2016). Explaining the 2008 Financial Crisis with a System Dynamics Model. https://theses.ubn.ru.nl/bitstream/handle/123456789/5093/Scholten%2C_Daan_1.pdf?sequence=1. (Accessed: 18.07.2024).
- Sukmana, R. & Ibrahim, M.H. (2017). How Islamic are Islamic banks? A Non-Linear Assessment of Islamic Rate - Conventional Rate Relations. Economic Modelling, 64, 443–448.
- Tang, L. & Sheng, H. (2009). Forecasting stock returns based on spline wavelet support vector. In: 2009 International Conference on Computational Intelligence and Natural Computing (pp. 383-385). IEEE, Wuhan.
- Tang, B., Sheng, H.Y. & Tang, L.X. (2009). GARCH prediction using spline wavelet support vector machine. Neural Computing & Applicatıons, 18(8), 913-917.
- TCMB. (2023). https://www.tcmb.gov.tr. (Accessed: 18.07.2024).
- Torky, M., Gad, I. & Hassanien, A.E. (2023). Explainable AI Model for Recognizing Financial Crisis Roots Based on Pigeon Optimization Gradient Boosting Model. International Journal of Computational Intelligent Systems, 16(1), 1-30.
- Urbanikova, M. & Stubnova, M. (2020). Use of Artificial Neural Networks in the Capital Markets. AD-ALTA – Journal of Interdisciplinary Research, 10(1), 278-281.
- Ustaoğlu, D. (2014). Türkiye’de Katılım Bankacılığı: Sektördeki Yeri ve Önemi. Unpublished Ma. Thesis. Adnan Menderes University, Turkey.
- Varsak, S. (2017). Participation Banking in Turkey and its Effects on the Turkish Financial System. Balkan and Near Eastern Journal of Social Sciences, 3, 104-109.
- Yang, H., Ahn, H.J., Kim, M.H. & Ryu, D. (2017). Information Asymmetry and Investor Trading Behavior Around Bond Rating Change Announcements. Emerging Markets Review, 32, 38-51.
- Yang, H.J. & Chen, S.H. (2018). A Heterogeneous Artificial Stock Market Can Benefit People against Another Financial Crisis. Plos One, 13(6), 1-25.
- Zhang, W. & Wang, J. (2017). Nonlinear Stochastic Exclusion Financial Dynamics Modeling and Complexity Behaviors. Nonlinear Dynamics, 88(2), 921-935.