Portfolio Diversification and Optimization at Industry Level, Evidence from Turkey

Yıl 2022, Cilt: 17 Sayı: 65, 277 - 294, 31.01.2022

Tarana Azimova

https://doi.org/10.19168/jyasar.835037

Öz

This paper applies the mean-variance, mean-VaR, and mean-CVaR portfolio optimization approach to
investigate opportunities for domestic diversification from Turkey investors’ viewpoints. We explore
diversification potential and investment opportunities at the industry level for the time period between 2007 and
2020. The study uses factor analysis to determine domestic diversification opportunities and measure the
optimal weight of sectors in the market index. Results from factor analysis show that for investors who desire to
create a domestic portfolio considerable diversification opportunities are available. Portfolio optimization
analysis indicates that the wholesale, retail trade and transportation industries should be prioritized by the
policymakers, as these industries earn the highest returns at a given risk level.

Anahtar Kelimeler

Portfolio Optimization, Factor Analysis, Domestic Diversification, Markowitz's Risk-Return Framework, Efficient Portfolia

Destekleyen Kurum

none

Proje Numarası

none

Teşekkür

none

Sektör Düzeyinde Portföy Çeşitlendirmesi ve Portföy Optimizasyonu, Türkiye Uygulaması

Öz

This paper explores diversification potential and investment opportunities at the industry level for Turkey for the time period between 2007 and 2020. This study uses the factor analysis to determine domestic diversification opportunities and the Markowitz's risk-return model to evaluate portfolio optimization and measure the optimal weight of sectors in the market index. Results show that the wholesale, retail trade and transportation industries should be prioritized by the policy makers, as these industries earn highest returns at a given risk level.

Bu çalışma 2007 ve 2020 yılları arasında Türkiye’de sektör düzeyinde çeşitlendirme potansiyelini ve yatırım fırsatlarını incelemektedir. Bu çalışma, yurtiçi çeşitlendirme fırsatlarını belirlemek için faktör analizini ve portföy optimizasyonunu değerlendirmek ve piyasa endeksindeki sektörlerin optimum ağırlığını ölçmek için Markowitz’in risk-getiri modelini kullanmaktadır. Yazarlar toptan ve perakende ticaret, taşıma ve ulaşım sektörlerin siyasete yön verenler tarafından öncelik verilmesi gerektiğini ortaya koymaktadır. Bu sektörlerin belirli bir risk seviyesinde en yüksek getiriye sahip oldukları gözlemlenmektedir.

Anahtar Kelimeler

Portföy optimizasyonu, faktör analizi, yurtiçi çeşitlendirme, Markowitz'in risk-getiri modeli, etkin portfolio

Proje Numarası

none

Kaynakça

• Akyuwen, R., Boffey, R. R., Powell, R. J., & Wijaya, K. (2017).Optimizing the industry mix of Indonesian portfolios. KINERJA, 18(2), 101-114.
• Allen, D., Kramadibrata, A., Powell, R., & Singh, A. (2012). Conditional value at risk applications to the global mining industry. Journal of Business and Policy Research, 7(3), 11-23.
• Allen, D., & Powell, R. (2011). Measuring and optimising extreme sectoral risk in Australia. Asia Pacific Journal of Economics and Business, 15(1), 1-14.
• Al Janabi, M. A. (2014). Optimal and investable portfolios: An empirical analysis with scenario optimization algorithms under crisis market prospects. Economic Modelling, 40(3), 369-381.
• Alexander, G. J., & Baptista, A. M. (2002). Economic implications of using a mean-VaR model for portfolio selection: A comparison with meanvariance analysis. Journal of Economic Dynamics and Control, 26(7), 1159-1193.
• Anderson, T. W. and H. Rubin (1956). Statistical inference in factor analysis, 2nd edition, University of California Press. Akyuwen, R., Boffey, R. R., Powell, R. J., &Wijaya, K. (2017). Optimizing the industry mix of Indonesian portfolios. KINERJA, 18(2), 101-114.
• Artzner, P., Delbaen, F., Eber, J. M., & Heath, D. (1999). Coherent measures of risk. Mathematical finance, 9(3), 203-228. Bali, T. G. & Engle, R. F. (2010).The intertemporal capital asset pricing model with dynamic conditional correlations. Journal of Monetary Economics, 57(4), 377-390.
• Brandt, M. W. & Kang, Q. (2004). On the relationship between the conditional mean and volatility of stock returns: A latent VAR approach. Journal of Financial Economics, 72(2), 217-257.
• Chen, R., & Yu, L. (2013). A novel nonlinear value-at-risk method for modeling risk of option portfolio with factor mixture of normal distributions. Economic Modelling, 35(4), 796-804.
• Campbell, R., Huisman, R., &Koedijk, K. (2001). Optimal portfolio selection in a Value-at-Risk framework. Journal of Banking & Finance, 25(9), 1789-1804.
• Donald R. Lessard (2008). International portfolio diversification: A factor analysis for a group of Latin American Countries. The Journal of Finance, 28 (3), 619-633.
• Duc Hong Vo, Thach Ngoc Pham, TrungThanh Vu Pham, Loc Minh Truong , Thang Cong Nguyen (2018) . Risk, return and portfolio optimization for various industries in the ASEAN region, Borsa Istanbul Review, 19(2), 132-138.
• Dziuban, C. D. and E. C. Shirkey (1974). When is a correlation matrix appropriate for factor analysis, Psychological Bulletin, 81(6), 358–361.
• Eduardo Sánchez Ruenes,José Antonio Núñez Mora and Martha Beatriz Mota Aragón, (2020). VaR and CVaR estimates in BRIC’s oil sector: A normal inverse gaussian distribution approach .EconomíaTeoría y Práctica, 52(28), 207-236.
• Embrechts, P., Resnick, S. I., &Samorodnitsky, G. (1999). Extreme value theory as a risk management tool. North American Actuarial Journal, 3(2), 30-41.
• Girard, E., Hassan, M.K., (2008). Is there a cost to faith-based investing: Evidence from FTSE Islamic indices? Journal of Investment 17(11), 12–121.
• Hakim Levy and Marshall Sarnat (1970). International diversification of investment portfolios, American Economic Review, 17(3), 668-675.
• Hannah Nadiah Abdul Razak ,Mohd. AzdiMaasar, Nur Hafidzah Hafidzuddin, Ernie Syufina Chun Lee (2019). Portfolio optimization of risky assets using mean-variance and mean-CVaR. Journal of Academia, 7(1) , 25-32.
• Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised, Chicago: University of Chicago Press.
• He, Z., Huh, S. W. & Lee, B. S. (2010). Dynamic factors and asset pricing. Journal of Financial and Quantitative Analysis, 45(3), 693- 707.
• Pogue, G.A., (1970). An extension of the Markowitz portfolio selection model to include variable transactions’ costs, short sales, leverage policies and taxes. Journal of Finance. 25 (5), 1005–1027.
• Merton, R. C. (1973). An intertemporal capital asset pricing model. Econometrica: Journal of the Econometric Society, 41(5), 867-887.
• Marshall E. Blume. (1971). On the Assessment of Risk. Journal of Finance,32(3), 1-10.
• Rockafellar, R. T., &Uryasev, S. (2002). Conditional value-at-risk for general loss distributions. Journal of Banking & Finance, 26(7), 1443-1471.
• Kane, A., (1982). Skewness preference and portfolio choice. Journal of Financal Quantitative Analysis 17(1), 15–25.
• Konno, H., Wijayanayake, A., (2001). Portfolio optimization problem under concave transaction costs and minimal transaction unit constraints. Mathematical Programming 99(5), 287–304.
• Simonson, D., (1972). The speculative behavior of mutual funds. Journal of Finance 27(2), 381–391.
• Tobin, J., (1965). The theory of portfolio selection in the theory of interest rates, Macmillan. F.H. Hahn and F.P.R. Brechling, London.
• Trichilli Y., Boujelbène M. A., Masmoudi A., (2020). Islamic and conventional portfolios optimization under investor sentiment states: Bayesian vs Markowitz portfolio analysis, Research in International Business and Finance, 51(2), 2-21.
• Xiao Z, Zhao P., (2013). Intertemporal Relation Between The expected return and risk: an evaluation of emerging market, The Journal of Applied Business Research, 29(3), 809-818.