What are the macroeconomic drivers of the asset returns of Turkish banks?
Abstract
The aim of this paper is to investigate the effects of the macroeconomic factors to the movements of the asset returns of the banks in Turkey in terms of systemic risk from 2005 to 2018. In the study, Independent Component Analysis is applied for extracting driving factors of the asset returns of Turkish banks by decomposing the returns into its components. After examining the relationship between the independent components and the macroeconomic variables, the results conclude that one component shows strong similarities with the well-known stock market index of Turkey, namely the BIST100. Besides, the BIST100 is observed as the most important macroeconomic indicator affecting the movements of the asset returns. From systemic risk perspective, the BIST100 and the exchange rate from US dollar to Turkish lira are interpreted as two macro factors that contribute to the systemic risk of Turkish banks. When it is reviewed the regression results of the estimated independent components with the macroeconomic variables, it is found that while the BIST100 affects the asset returns of Turkish banks on its own, three macroeconomic factors (the credit default swap spreads of Turkey, the exchange rate and volatility) jointly affect the banks by creating a chain effect.
First published online 08 November 2022
Keyword : independent component analysis, asset returns, macroeconomic factors, systemic risk, exchange rate, banks, BIST100, credit default swap
How to Cite
Civan, Z., Simsek, G. G., & Çinar, U. K. (2023). What are the macroeconomic drivers of the asset returns of Turkish banks?. Technological and Economic Development of Economy, 29(1), 91–113. https://doi.org/10.3846/tede.2022.17750
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Ayaydin, H., & Karaaslan, İ. (2014). Stock market development, bank concentration, ownership structure, and bank performance: Evidence from Turkey. Journal of Economics & Political Economy, 1(1), 49–67. https://doi.org/10.1453/jepe.v1i1.25
Aysan, A. F., & Ceyhan, S. P. (2008). What determines the banking sector performance in globalized financial markets? The case of Turkey. Physica A: Statistical Mechanics & its Applications, 387(7), 1593–1602. https://doi.org/10.1016/j.physa.2007.11.003
Back, A. D., & Weigend, A. S. (1997). A first application of independent component analysis to extracting structure from stock returns. International Journal of Neural Systems, 8(4), 473–484. https://doi.org/10.1142/S0129065797000458
Bajo-Rubio, O., Berke, B., & McMillan, D. (2017). The behaviour of asset return and volatility spillovers in Turkey: A tale of two crises. Research in International Business & Finance, 41, 577–589. https://doi.org/10.1016/j.ribaf.2017.04.003
Baltaci, N. (2014). Turkish banking sector: The analysis of macroeconomics indicators and bank profitability with panel data approach. Journal of Academic Research in Economics, 6(1), 82–92.
Bank for International Settlements. (2007). Understanding asset prices: An overview (BIS Papers No. 34). https://doi.org/10.2139/ssrn.1190962
Bank for International Settlements. (2017). Asset prices and macroeconomic outcomes: A survey (BIS Working Papers No. 676).
Binici, M., Koksal, B., & Orman, C. (2013). Stock return comovement and systemic risk in the Turkish banking system. Central Bank Review, 13, 41–63. https://doi.org/10.2139/ssrn.2054436
Ceffer, A., Levendovszky, J., & Fogarasi, N. (2019). Applying independent component analysis and predictive systems for algorithmic trading. Computational Economics, 54(1), 281–303. https://doi.org/10.1007/s10614-017-9719-z
Cha, S. M., & Chan, L. W. (2002). Applying independent component analysis to factor model in finance. In K. S. Leung, L. W. Chan, & H. Meng (Eds.), Lecture notes in computer science: Vol. 1983. Intelligent data engineering and automated learning – IDEAL 2000. Data mining, financial engineering, and intelligent agents (pp. 538–544). Springer. https://doi.org/10.1007/3-540-44491-2_78
Chau, F., Han, C., & Shi, S. (2018). Dynamics and determinants of credit risk discovery: Evidence from CDS and stock markets. International Review of Financial Analysis, 55, 156–169. https://doi.org/10.1016/j.irfa.2017.11.004
Chaudhury, M. (2014). How did the financial crisis affect the daily stock returns? Journal of Investing, 23(3), 65–84. https://doi.org/10.3905/joi.2014.23.3.065
Chen, K. H., & Khashanah, K. (2015, December). The reconstruction of financial signals using Fast ICA for systemic risk. In 2015 IEEE Symposium Series on Computational Intelligence (pp. 885–889). Cape Town, South Africa. IEEE. https://doi.org/10.1109/SSCI.2015.130
Chen, N., Roll, R., & Ross, S. A. (1986). Economic forces and the stock market. Journal of Business, 59(3), 383–403. https://doi.org/10.1086/296344
Chen, Y., Hardle, W., & Spokoiny, V. (2007). Portfolio value at risk based on independent component analysis. Journal of Computational & Applied Mathematics, 205(1), 594–607. https://doi.org/10.1016/j.cam.2006.05.016
Chowdhury, U. N., Chakravarty, S. K., & Hossain, H. (2018). Short-term financial time series forecasting integrating principal component analysis and independent component analysis with support vector regression. Journal of Computer & Communications, 6(3), 51–67. https://doi.org/10.4236/jcc.2018.63004
Christensen, B. J., Nielsen, M., & Zhu, J. (2015). The impact of financial crises on the risk-return tradeoff and the leverage effect. Economic Modelling, 49, 407–418. https://doi.org/10.1016/j.econmod.2015.03.006
Civan, Z, Simsek, G. G., & Akay, E. C. (2020). Identifying the systemically important banks of Turkey with the CoVaR method. Heliyon, 6(9), 1–15. https://doi.org/10.1016/j.heliyon.2020.e04790
Comon, P. (1994). Independent component analysis, a new concept? Signal Processing, 36(3), 287–314. https://doi.org/10.1016/0165-1684(94)90029-9
Cortes, R. L. G., Porras, S. T., & Moreno, E. M. (2019). Extraction of the underlying structure of systematic risk from non-gaussian multivariate financial time series using independent component analysis: Evidence from the Mexican stock. Computacion y Sistemas, 22(4), 1049–1064. https://doi.org/10.13053/CyS-22-4-3083
Dagidir, C. (2010). Profitability in Turkish banking sector and its relations with macroeconomic variables. Journal of Economics, 2(1), 25–33.
Diebold, F., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534), 158–171. https://doi.org/10.1111/j.1468-0297.2008.02208.x
Dogan, S., & Akinci, H. (2013). Extraction of the intrinsic images from video frames by using independent component analysis (ICA). Electronic Journal of Map Technologies, 5(1), 13–25.
Fabozzi, F. J., Giacometti, R., & Tsuchida, N. (2015). The ICA-based factor decomposition of the Eurozone sovereign CDS spreads (IMES Discussion Paper Series 2015-E-04). Institute for Monetary and Economic Studies, Bank of Japan.
Fama, E. F. (1981). Stock returns, real activity, inflation and money. American Economic Review, 71(4), 545–565.
Fama, E. F. (1990). Stock returns, expected returns, and real activity. The Journal of Finance, 45(4), 1089–1108. https://doi.org/10.2307/2328716
Fifield, S. G. M., Power, D. M., & Sinclair, C. D. (2002). Macroeconomic factors and share returns: An analysis using emerging market data. International Journal of Finance & Economics, 7(1), 51–62. https://doi.org/10.1002/ijfe.173
Garcia-Ferrer, A., Gonzalez-Prieto, E., & Pena, D. (2008). A multivariate generalized independent factor GARCH model with an application to financial stock returns (Statistics and Econometrics Working Papers). Universidad Carlos III, Departamento de Estadistica y Econometria.
Garcia-Ferrer, A., Gonzalez-Prieto, E., & Pena, D. (2012). A conditionally heteroskedastic independent factor model with an application to financial stock returns. International Journal of Forecasting, 28(1), 70–93. https://doi.org/10.1016/j.ijforecast.2011.02.010
Hartmann, P., Straetmans, S., & De Vries, C. G. (2004). Asset market linkages in crisis periods. The Review of Economics & Statistics, 86(1), 313–326. https://doi.org/10.1162/003465304323023831
Herault, J., & Jutten, C. (1986). Space or time adaptive signal processing by neural network models. In Neural Networks for Computing Proceeding of AIP Conference (pp. 211–206). American Institute of Physics. https://doi.org/10.1063/1.36258
Hyvarinen, A. (1996). Simple one unit algorithms for blind source separation and blind deconvolution. In Progress in Neural Information Processing ICONIP’96 2 (pp. 1201–1206). Springer.
Hyvarinen, A., & Oja, E. (2000). Independent component analysis: Algorithms and applications. Neural Networks, 13(4–5), 411–430. https://doi.org/10.1016/S0893-6080(00)00026-5
Hyvarinen, A., Karhunen, J., & Oja, E. (2001). Independent component analysis. John Wiley & Sons Inc. https://doi.org/10.1002/0471221317
Humpe, A., & Macmillan, P. (2009). Can macroeconomic variables explain long-term stock market movements? A comparison of the US and Japan. Applied Financial Economics, 19(2), 111–119. https://doi.org/10.1080/09603100701748956
Jutten, C., & Herault, J. (1991). Blind separation of sources, part I: An adaptive algorithm based on neuromimetric architecture. Signal Processing, 24(1), 1–10. https://doi.org/10.1016/0165-1684(91)90079-X
Kucukkocaoglu, G., Unalmis, D., & Unalmis, I. (2013). How do banks’ stock returns respond to monetary policy committee announcements in Turkey? Evidence from traditional versus new monetary policy episodes. Economic Modelling, 35, 536–545. https://doi.org/10.1016/j.econmod.2013.07.019
Kumiega, A., Neururer, T., & Van-Vliet, B. (2011). Independent component analysis for realized volatility: Analysis of the stock market crash of 2008. The Quarterly Review of Economics & Finance, 51(3), 292–302. https://doi.org/10.1016/j.qref.2011.03.002
Liu, H., & Wang, J. (2011). Integrating independent component analysis and principal component analysis with neural network to predict Chinese stock market. Mathematical Problems in Engineering, 2011, 1–15. https://doi.org/10.1155/2011/382659
Liu, Y., Wang, F., Chang, Y., Gao, F., & He, D. (2019). Performance-relevant kernel independent component analysis based operating performance assessment for nonlinear and non-Gaussian industrial processes. Chemical Engineering Science, 209, 115167. https://doi.org/10.1016/j.ces.2019.115167
Longstaff, F. A., Pan, J., Pedersen, L. H., & Singleton, K. J. (2011). How sovereign is sovereign credit risk? American Economic Journal: Macroeconomics, 3(2), 75–103. https://doi.org/10.1257/mac.3.2.75
Lu, C. J., Lee, T. S., & Chiu, C. C. (2009). Financial time series forecasting using independent component analysis and support vector regression. Decision Support Systems, 47(2), 115–125. https://doi.org/10.1016/j.dss.2009.02.001
Markose, S., Giansante, S., & Shaghaghi, A. R. (2012). “Too interconnected to fail” financial network of US CDS market: Topological fragility and systemic risk. Journal of Economic Behaviour & Organization, 83(3), 627–646. https://doi.org/10.1016/j.jebo.2012.05.016
Miettinen, J., Matilainen, M., Nordhausen, K., & Taskinen, S. (2020). Extracting conditionally heteroskedastic components using independent component analysis. Journal of Time Series Analysis, 41(2), 293–311. https://doi.org/10.1111/jtsa.12505
Moneta, A., & Pallante, G. (2020). Identification of structural VAR models via independent component analysis: A performance evaluation study (LEM Working Paper Series No. 2020/24). Scuola Superiore Sant’Anna.
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