Dergiler / Business and Economics Research Journal / 2019 / Cilt: 10 - Sayı: 4

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

Sayfa
807–822
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Additionally, it determines that macroeconomic variables would have various impacts on stock returns on bank stocks by comparison of the conventional methods.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:According to the modern portfolio theory, the direction of the relationship between the securities in the portfolio is stated to be effective in reducing the risk.
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Temel Bulgular & Veri:The models structured by the Bayesian networks are capable of visually illustrate the probabilistic relationship.
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Sonuç & Katkı:Additionally, it determines that macroeconomic variables would have various impacts on stock returns on bank stocks by comparison of the conventional methods.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2001 Verileri
⚙️Değişkenler:Alan Parametreleri

Özet

According to the modern portfolio theory, the direction of the relationship between 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 same portfolio. The models structured by the Bayesian networks are capable of visually illustrate the probabilistic relationship. Also, portfolio returns could be refreshed simultaneously when new information has arrived. The study aims to provide dynamic information through Bayesian networks and to investigate the relationship between macroeconomic indicators and stock returns of Turkish major bank stocks based on the Arbitrage Pricing Model. The dataset includes stock returns of four banks listed in the Borsa Istanbul from June 2001 to January 2017. Besides, macroeconomic variables such as BIST-100 Index, oil prices, inflation, exchange, and interest rate & money supply are gathered for the same period. The results suggest that the Bayesian network models allow dynamics among stock returns could be investigated in more detail. Additionally, it determines that macroeconomic variables would have various impacts on stock returns on bank stocks by comparison of the conventional methods.

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Bu Makaleye Atıf Yap

HATİPOĞLU, F. B., UYAR, U. (2019). Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks. Business and Economics Research Journal, Cilt: 10 - Sayı: 4, 807–822. https://ajindex.com/articles/143793

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Kaynakça (10)

  1. 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.
  2. 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.
  3. Burmeister, E., & Wall, K. D. (1986). The Arbitrage Pricing Theory and macroeconomic factor measures. Financial Review, 21(1), 1-20.
  4. Chan, K., Chen, N., & Hsieh, D. (1985). An exploratory investigation of the firm size effect. Journal of Financial Economics, 14, 451–71.
  5. Chen, N., Roll, R., & Ross, S. (1986). Economic forces and the stock market. The Journal of Business, 59(3), 383-403.
  6. 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.
  7. 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.
  8. 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.
  9. 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,
  10. 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,