BAYES AĞ MODELLERİ İLE FİNANSAL PİYASA DİNAMİKLERİ ÜZERİNE BİR İNCELEME: ELEKTRİK ÜRETİM ŞİRKETLERİ UYGULAMASI

Abstract

Yatırımcılar tasarruflarını değerlendirmek için finansal varlıklardan oluşan portföyler oluşturmaktadır. Markowitz’e (1952) göre finansal piyasalarda yatırımcılar, belirli bir risk seviyesinde en yüksek getiriyi veya belirli bir getiri düzeyinde en düşük riski sağlayacak portföyler oluşturmayı hedeflerler. Ekonomik, sosyal ve politik açıdan çok sayıda faktörden etkilenen bu piyasalarda, fiyatlarda meydana gelecek değişimler ve yatırım kararları belirsizlik içermektedir. Finans piyasalarında yüksek getiri sağlayacak portföyün seçimi, yatırım kararlarının doğru belirlenmesi ve sonuçların öngörülmesi önemlidir. Bu çalışmanın amacı Arbitraj Fiyatlama Teorisi’ni temel alarak mevcut finansal teorileri ve firmaya özgü dinamiklerin portföy getirisi üzerindeki etkilerini Bayes ağlarla incelemektir. Modelin veri seti makroekonomik faktörler, Borsa İstanbul tüm hisse senetleri endeksi, Borsa İstanbul alt sektör endeksleri ve hisse senedine özgü değişkenlerden oluşmaktadır. Araştırma periyodu Haziran 2001- Ocak 2017 dönemi olarak belirlenmiş ve 188 aylık getiri verilerden oluşturulmuştur. Çalışmada firmaya özgü risklerin portföy getirisi üzerine etkisi detaylı ve farklı açılardan araştırılmış, finans teorisiyle paralel sonuçlar elde edilmiştir.

Keywords

• Bayes Ağlar
• Portföy Seçim Teorisi
• Borsa İstanbul

A REVIEW OF FINANCIAL MARKET DYNAMICS WITH BAYES NETWORK MODELS: THE APPLICATION OF ELECTRICITY GENERATION COMPANIES

Abstract

To obtain returns, investors form portfolios of financial assets to assess funds held in their hands. According to Markowitz, investors in financial markets are aiming to create portfolios with the highest risk at a given risk level or the lowest risk at a given yield level. In these markets, which are influenced by a number of economic, social and political factors, changes in prices and investment decisions are uncertain. It is important to select the portfolio that will provide high returns in the financial markets, to determine the investment decisions correctly and to predict the results. Based on the objective Arbitrage Pricing Theory of this study, we examine Bayesian networks’ effects of existing financial theories and firm-specific dynamics on portfolio returns. The model’s dataset consists of macroeconomic factors, the Borsa Istanbul total stock index, the stock exchange Istanbul sub-sector indexes, and stock-specific variables, which are 188 months of return for June 2001-January 2017 period. In the study, the effects of firm-specific risks on portfolio turnover were investigated and results were obtained in parallel with finance theory.

Keywords

• Bayesian Networks
• Portfolio Selection Theory
• Borsa Istanbul

References

1. Aguilera, P.A., Fernandez, A., Fernandez, R., Rumi, R. ve Salmeron, A. (2011). “Bayesian networks in environmental modelling”, Environmental Modelling & Software, 26 (12), 1376-1388.
2. Aquino, R. Q. (2005). “Exchange rate risk and Philippine stock returns: before and after the Asian financial crisis”, Applied Financial Economics, Volume 15, Issue 11, 765-771.
3. Bahl, B. (2006). Testing the Fama and French Three-Factor Model and Its Variants for the Indian Stock Returns. Available at SSRN: https://ssrn.com/abstract=950899 or http://dx.doi.org/10.2139/ssrn.950899. (24.07.2017)
4. Chen, N., Roll R. ve Ross S. (1986). “Economic Forces and the Stock Market”, The Journal of Business, Vol. 59, No. 3, 383-403.
5. Cohen, K. J. ve Pogue J. E. (1967). “An Empirical Evaluation of Alternative Portfolio-Selection Models”, Journal of Business, 40, 166-193.
6. Conrady, S. ve Jouffe L. (2015). Bayesian Networks & BayesiaLab–A Practical Introduction for Researchers, Bayesia USA.
7. Demirer, R., Mau, R. ve Shenoy, C. (2006). “Bayesian Networks: A Decision Tool to Improve Portfolio Risk Analysis”, Journal of Applied Finance, (16: 2), 106-133.
8. Dhrymes, P., Friend I. ve Gültekin N. B. (1984). “A Critical Reexamination of The Empirical Evidence on The Arbitrage Pricing Theory”, Journal of Finance, Vol. 39, No: 2, 323-346.

9. Elsas, R., El-Shaer, M. ve Theissen, E. (2003). “Beta and returns revisited: Evidence from the German stock market”, Journal of International Financial Markets, Institutions and Money, 13(1), 1–18.
10. Fama, E. ve K. French. (1992). “The Cross-Section of Expected Stock Returns”, The Journal of Finance, 47, Issue 2, 427- 465.
11. Fama, E. ve K. R. French (1993). “Common risk factors in the returns on stocks and bonds”, Journal of Financial Economics, 33, 3–56.
12. Ferson, W. E. ve Harvey C. R. (1994). “Sources of Risk and Expected Returns in Global Equity Markets”, Journal of Banking and Finance, 18, 775-803.
13. Gay, R. D. (2008). “Effect of macroeconomic variables on stock market returns for four emerging economies: Brazil, Russia, India and China”, International Business & Economics Research Journal, 7(3), 1-8.
14. Greppi, A. (2014). A Bayesian Network Approach to Portfolio Management, First Dreamt Research in Progress Workshop, Pavia.
15. Greppi, A., De Giuli M. E. ve Tarantola C., (2013). Bayesian Network for Stock Picking.
16. Griffin, J. M. (2002). “Are the Fama and French factors global or country specific?”, Review of Financial Studies, 15(3), 783–803.
17. Heston, S. L., Rouwenhorst, K.G. ve Wessels, R. E. (1999). “The role of beta and size in the crosssection of European stock returns”, European Financial Management, 5(1), 9–27.
18. Hodoshima, J., Garza-Gómez, X. ve Kunimura, M. (2000). “Cross-sectional regression analysis of return and beta in Japan”, Journal of Economics and Business, 52(6), 515–533.
19. Hoe, T. K. (2014). A Machine Learning-based Decision Support Tools for Portfolio Risk Analysis. (Yayımlanmamış Yüksek Lisans Tezi). Tunku Abdul Rahman Üniversitesi.
20. Jensen, F.V. (2001). Bayesian Networks and Decision Graphs, 1.Baskı, Springer, New York.
21. Kita E., Zuo Y., Harada M. ve Mizuno T. (2012). “Application of Bayesian Network to Stock Price Prediction”, Artificial Intelligence Research, Vol. 1, No. 2.
22. Lintner, J. (1965). “The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets”, Review of Economics and Statistics, 47(1): 13-37.
23. Markowitz, H. (1952). “Portfolio Selection”, The Journal of Finance, Blackwell Publishing, Vol: 7, No: 1, 77-91.
24. Mossin, J. (1966). “Equilibrium in a Capital Asset Market”, Econometrica, 35(4): 768-783.
25. Nadkarni, S. ve Shenoy, P.P. (2004). “A causal mapping approach to constructing Bayesian networks”, Decision Support Systems, 38 (2), 259-281.
26. 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, Vol. 7, Lodz University Press, 157-171.
27. Pettengill, G. N., Sundaram, S. ve Mathur, I. (1995). “The conditional relation between beta and returns”, Journal of Financial and Quantitative Analysis, 30(1), 101–116.
28. Roll R. ve Ross S. (1980). “An Empirical Investigation of the Arbitrage Pricing Theory”, The Journal of Finance, Vol. 35, No. 5, 1073-1103.
29. Roll, R. (1977). “A Critique of the Asset Pricing Theory's Tests: Part I: On Past and Potential Testability of the Theory”, Journal of Financial Economics, Vol 4, 129-176.
30. Roll, R. ve Ross S. A. (1984). “The Arbitrage Pricing Theory Approach to Strategic Portfolio Planning” Financial Analysis Journal, 40, 14–26.
31. Ross, S. (1976). “The Arbitrage Theory Of Capital Asset Pricing”, Journal of Economic Theory, Vol. 13, 341-360.
32. Sharpe, W. F. (1963). “A Simplified Model for Portfolio Analysis”, Management Science, 9 (2): 277-293.
33. Sharpe, W. F. (1964). “Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk”, The Journal of Finance, Vol. 19, No. 3, 425-442.
34. Shenoy, C. ve Shenoy, P.P. (1998). Bayesian Networks: A Decision Tool to Improve Portfolio Risk Analysis, Working paper, School of Business, University of Kansas.
35. Tseng, C. (2003). Comparing Artificial Intelligence Systems for Stock Portfolio Selection. depts.washington.edu/sce2003/Papers/236.pdf. (24.07.2017)
36. Uyar, U. ve Kangallı, S. G. (2012). Markowitz Modeline Dayalı Optimal Portföy Seçiminde İşlem Hacmi Kısıtı, Ege Akademik Bakış, Cilt 12, Sayı 2, 183-192.
37. Villa, S. ve Stella F. (2012). “Bayesian Networks for Portfolio Analysis and Optimization”, Financial Decision Making Using Computational Intelligence, Vol. 70, 209-232.
38. Zuo, Y. ve Kita E. (2012). “Up/Down Analysis of Stock Index by Using Bayesian Network”, Engineering Management Research, Vol. 1, No. 2. 46-52.