Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks

Fatma Busem HATİPOĞLU, (Muğla Sıtkı Koçman Üniversitesi)
Umut UYAR (Pamukkale Üniversitesi)
Business and Economics Research Journal
8 0
Yıl: 2019 Cilt: 10 Sayı: 4 Sayfa Aralığı: 807 - 822 Metin Dili: İngilizce DOI: 10.20409/berj.2019.202 İndeks Tarihi: 21-01-2020

Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks

Öz:
According to the modern portfolio theory, the direction of the relationshipbetween the securities in the portfolio is stated to be effective in reducing the risk.Moreover, securities in high correlation are avoided by taking place in the sameportfolio. The models structured by the Bayesian networks are capable of visuallyillustrate the probabilistic relationship. Also, portfolio returns could be refreshedsimultaneously when new information has arrived. The study aims to provide dynamicinformation through Bayesian networks and to investigate the relationship betweenmacroeconomic indicators and stock returns of Turkish major bank stocks based on theArbitrage Pricing Model. The dataset includes stock returns of four banks listed in theBorsa Istanbul from June 2001 to January 2017. Besides, macroeconomic variables suchas BIST-100 Index, oil prices, inflation, exchange, and interest rate & money supply aregathered for the same period. The results suggest that the Bayesian network modelsallow dynamics among stock returns could be investigated in more detail. Additionally,it determines that macroeconomic variables would have various impacts on stockreturns on bank stocks by comparison of the conventional methods.
Anahtar Kelime:

Konular: İş İşletme İktisat İşletme Finans
Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • Alizadeh, N. (2013). Analysing the impact of macro economical data on the stock returns of oil and natural gas in Turkey by using arbitrage pricing model. Hacettepe University, Master’s Thesis, Turkey.
  • Altay, E. (2005). The effect of macroeconomic factors on asset returns: A comparative analysis of the German and the Turkish stock markets in an APT framework. Marmara Universitesi Sosyal Bilimler Enstitusu Dergisi, 6(23), 217- 237.
  • BAT, (2017, August). The banking system in Turkey yearly statistics: 1959 – 2016. The Banking Association of Turkey Report, Turkey.
  • Beenstock, M., & Chan, K. (1988). Economic forces in the London Stock Market. Oxford Bulletin of Economics and Statistics, 50(1), 27-39.
  • Bensi, M. T., Kiureghian, A. D., & Straub, D. (2011). A Bayesian network methodology for infrastructure seismic risk assessment and decision support. Pacific Earthquake Engineering Research Center.
  • Burmeister, E., & McElroy, M. (1988). Joint estimation of factor sensitivities and risk premia for the Arbitrage Pricing Theory. Journal of Finance, 43(3), 721-733.
  • Burmeister, E., & Wall, K. D. (1986). The Arbitrage Pricing Theory and macroeconomic factor measures. Financial Review, 21(1), 1-20.
  • Chan, K., Chen, N., & Hsieh, D. (1985). An exploratory investigation of the firm size effect. Journal of Financial Economics, 14, 451–71.
  • Chen, N., Roll, R., & Ross, S. (1986). Economic forces and the stock market. The Journal of Business, 59(3), 383-403.
  • Chen, S., & Jordan, B. (1993). Some empirical tests of the Arbitrage Pricing Theory: Macrovariables vs. derived factors. Journal of Banking and Finance, 17, 65-89.
  • Clare, A. D., & Thomas, S. H. (1994). Macroeconomic factors, the APT and the UK stock market. Journal of Business Finance and Accounting, 21, 309-330.
  • Conrady, S., & Jouffe, L. (2015). Bayesian Networks & BayesiaLab–A practical introduction for researchers. USA: Bayesia.
  • Demirer, R., Mau, R., & Shenoy, C. (2006). Bayesian networks: A decision tool to improve portfolio risk analysis. Journal of Applied Finance, 16(2), 106-133.
  • Elton, E. J., & Gruber, M. J. (1981). Modern portfolio theory and investment analysis. New York: John Wiley and Sons.
  • Greppi, A. (2014, June-July). A Bayesian network approach to portfolio management. First DREAMT Research in Progress Workshop, 83-92, Pavia.
  • Greppi, A., De Giuli, M. E., & Tarantola, C. (2013, October). Bayesian network for stock picking. Conference presentation at 10th Scientific Meeting of Classification and Data Analysis Group, Cagliari.
  • Hoe, T. K. (2014). A machine learning-based decision support tools for portfolio risk analysis. Universiti Tunku Abdul Rahman, Master’s Thesis.
  • Kearney, C., & Daly, K. (1998). The causes of stock market volatility in Australia. Applied Financial Economics, 8, 597-605.
  • Kwon, C. S., & Shin, T.S. (1999). Cointegration and causality between macroeconomic variables and stock market returns. Global Finance Journal, 10(1), 71-81.
  • Lauria, E. J. M., & Duchessi, P. (2007). A methodology for developing Bayesian networks: An application to information technology (IT) implementation. European Journal of Operational Research, 179, 234-252.
  • Market, M. (2018). The Turkish banking sector: A promising market? Retrieved February 20, 2018, from themarketmogul.com/turkish-banking-sector Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
  • Mittal, A., Kassim, A., & Tan, T. (2007). Bayesian network technologies: Applications and graphical models. USA: IGI Global.
  • Mrad, A. B., Delcroix, V., Maalej, M. A. & Abid, M. (2012). Understanding soft evidence. The Sixth European Workshop on Probabilistic Graphical Models (PGM 2012), 19-21 September, Granada, Spain.
  • Nagarajan, R., Scutari, M., & Lebre, S. (2013). Bayesian networks in R with applications in systems biology. New York: Springer Science+Business Media.
  • Olbryś, J. (2009). Forecasting portfolio return based on Bayesian network model. In W. Milo, G. Szafrański, & P. Wdowiński (eds.) Financial Markets. Principles of Modelling, Forecasting and Decision-Making, FindEcon Monograph Series: Advances in Financial Market Analysis, Volume 7, Lodz University Press, 157-171.
  • Ozer, O., Kaya, A., & Ozer, N. (2011). Hisse senedi fiyatlari ile makroekonomik degiskenlerin etkilesimi. Dokuz Eylul Universitesi Iktisadi ve Idari Bilimler Fakultesi Dergisi, 26(1), 163-182.
  • Ozkan, N., Cakan, S., & Kayacan, M. (2017). Intellectual capital and financial performance: A study of the Turkish banking sector. Borsa Istanbul Review, 17(3), 190-198.
  • Sammut, C., & Webb, G. I. (2011). Encyclopedia of machine learning. Springer.
  • Shenoy, C., & Shenoy, P. P. (1998). Bayesian networks: A decision tool to improve portfolio risk analysis. Working Paper, School of Business, University of Kansas.
  • Sucar, L. E. (2015). Probabilistic graphical models principles and applications. London: Springer-Verlag.
  • Trucco, P., Cagno, E., Ruggeri, F., & Grande, O. (2008). Bayesian belief network modelling of organisational factors in risk analysis: A case study in maritime transportation. Reliability Engineering and System Safety, 93, 823-834.
  • Tseng, C. (2003). Comparing artificial intelligence systems for stock portfolio selection. Retrieved July 24, 2017, from http://depts.washington.edu/sce2003/Papers/236.pdf
  • Valtorta M., Kim Y., & Vomlel J. (2002). Soft evidential update for probabilistic multiagent systems. International Journal of Approximate Reasoning, 29(1), 71–106.
  • Villa, S., & Stella, F. (2012). Bayesian networks for portfolio analysis and optimization. Financial Decision Making Using Computational Intelligence, 70, 209-232.
  • Zuo, Y., & Kita, E. (2012). Up/down analysis of stock index by using Bayesian network. Engineering Management Research. 1(2), 46-52.
APA HATİPOĞLU F, UYAR U (2019). Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks. , 807 - 822. 10.20409/berj.2019.202
Chicago HATİPOĞLU Fatma Busem,UYAR UMUT Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks. (2019): 807 - 822. 10.20409/berj.2019.202
MLA HATİPOĞLU Fatma Busem,UYAR UMUT Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks. , 2019, ss.807 - 822. 10.20409/berj.2019.202
AMA HATİPOĞLU F,UYAR U Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks. . 2019; 807 - 822. 10.20409/berj.2019.202
Vancouver HATİPOĞLU F,UYAR U Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks. . 2019; 807 - 822. 10.20409/berj.2019.202
IEEE HATİPOĞLU F,UYAR U "Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks." , ss.807 - 822, 2019. 10.20409/berj.2019.202
ISNAD HATİPOĞLU, Fatma Busem - UYAR, UMUT. "Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks". (2019), 807-822. https://doi.org/10.20409/berj.2019.202
APA HATİPOĞLU F, UYAR U (2019). Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks. Business and Economics Research Journal, 10(4), 807 - 822. 10.20409/berj.2019.202
Chicago HATİPOĞLU Fatma Busem,UYAR UMUT Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks. Business and Economics Research Journal 10, no.4 (2019): 807 - 822. 10.20409/berj.2019.202
MLA HATİPOĞLU Fatma Busem,UYAR UMUT Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks. Business and Economics Research Journal, vol.10, no.4, 2019, ss.807 - 822. 10.20409/berj.2019.202
AMA HATİPOĞLU F,UYAR U Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks. Business and Economics Research Journal. 2019; 10(4): 807 - 822. 10.20409/berj.2019.202
Vancouver HATİPOĞLU F,UYAR U Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks. Business and Economics Research Journal. 2019; 10(4): 807 - 822. 10.20409/berj.2019.202
IEEE HATİPOĞLU F,UYAR U "Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks." Business and Economics Research Journal, 10, ss.807 - 822, 2019. 10.20409/berj.2019.202
ISNAD HATİPOĞLU, Fatma Busem - UYAR, UMUT. "Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks". Business and Economics Research Journal 10/4 (2019), 807-822. https://doi.org/10.20409/berj.2019.202
Sayfa Oluşturulma Tarihi
30-04-2024 22:15

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