STATIC AND DYNAMIC PORTFOLIO ALLOCATİON ANALYSIS BASED ON EXCHANGE-TRADED FUNDS

Yıl 2021, Cilt: 39 Sayı: 4, 561 - 579, 29.12.2021

Önder Büberkökü

https://doi.org/10.17065/huniibf.845019

Cited By: 1

Öz

This study compares the performances of the Markowitz (1952) mean-variance portfolio optimisation method and three alternative portfolio optimisation methods, namely, the conditional value-at-risk method, risk parity method and Kelly criterion, taking the nine Select Sector SPDR ETFs trading in the US stock market into account. All the analyses are performed primarily using the traditional static asset allocation approach that produces a single optimal portfolio for the entire period. Subsequently, we use the dynamic asset allocation approach, which produces different optimal portfolios for each month depending on the timing window of past returns. Sharpe, Calmar, Sortino, Treynor and Information ratios and Jensen’s alpha are used to compare the performances of the optimal portfolios. The findings show that the conditional value-at-risk method is the best performing of the methods examined.

Anahtar Kelimeler

Dynamic asset allocation, portfolio optimisation, conditional value-at-risk method, Risk parity method, Kelly criterion, Dynamic asset allocation, portfolio optimisation, conditional value-at-risk method, Risk parity method, Kelly criterion

BORSA YATIRIM FONLARINA DAYALI STATİK VE DİNAMİK PORTFÖY OPTİMİZASYON ANALİZLERİ

Öz

Bu çalışmada ABD S&P500 endeksinde yer alan ve 9 farklı sektörü esas alan borsa yatırım fonları (Exchange-traded funds, ETFs) dikkate alınarak Markowitz (1952) ortalama-varyans yöntemi ile bu yönteme alternatif teşkil eden koşullu riske maruz değer yöntemi, risk paritesi yöntemi ve Kelly kriterinin portföy optimizasyon performansları statik ve dinamik optimizasyon yaklaşımları kullanılarak karşılaştırılmıştır. Analizler öncelikle incelenen dönem için tek bir optimal portföy üreten statik optimizasyon yaklaşımı ile yapılmıştır. Ardından analizler aylık bazda güncellenen veri setine bağlı olarak her ay için farklı optimal portföyler üreten dinamik optimizasyon yöntemi ile yapılmıştır. Optimal portföylerin performanslarının karşılaştırılmasında Jensen (alfa) kriterinin yanı sıra Sharpe, Calmar, Sortino, Treynor ve Bilgi rasyolarından yararlanılmıştır. Çalışma bulguları açık bir şekilde koşullu riske maruz değer yönteminin en iyi performansı sergileyen yöntem olduğu sonucuna işaret etmektedir.

Anahtar Kelimeler

Dinamik portföy optimizasyonu, Koşullu riske maruz değer yöntemi,, Risk paritesi yöntemi, Kelly kriteri

Kaynakça

• Asness, C.S., A. Frazzini, L.H. Pedersen (2018), “Leverage Aversion and Risk Parity”, Financial Analysts Journal, 68 (1), 47-59.
• Aytürk, Y. (2015), “Black-Litterman Modeli ile Borsa İstanbul’da Portföy Optimizasyonu”, Bankacılar Dergisi, 95,51-66.
• Costa, G., R.H. Kwon (2019), “Risk Parity Portfolio Optimization under a Markov Regime-Switching Framework”, Quantitative Finance, 19 (3), 453-471.
• Cura, T. (2009), “Particle Swarm Optimization Approach to Portfolio Optimization”, Nonlinear Analysis: Real World Applications, 10(4), 2396–2406.
• Çelengi, A. Z., E. Eğrioğlu, B.Ş. Çorba (2015), “İMKB 30 İndeksini Oluşturan Hisse Senetleri için Parçacık Sürü Optimizasyonu Yöntemlerine Dayalı Portföy Optimizasyonu”, Doğuş Üniversitesi Dergisi, 16(1), 25-33.
• Demey, P., Maillard, S. ve Roncalli, T. (2010), Risk-Based Indexation, SSRN, March. https://ssrn.com/ abstract =1582998, E.T.: 22.05.2020.
• Fama, E.F. (1970), “Efficient Capital Markets: A Review of Theory and Emprical Work”, Journal of Finance, 25 (2), 383-417.
• Fernandez, A., S. Gomez (2007), “Portfolio Selection Using Neural Networks”, Computers & Operations Research”, 34, 1177-1191.
• Giot, P., S. Laurent (2003), “Value-at-Risk for Long and Short Positions”, Journal of Applied Econometrics, 18, 641-664.
• Gökgöz, E. (2006), Riske Maruz Değer (VaR) ve Portföy Optimizasyonu, Ankara: Sermaye Piyasası Kurulu Yayınları, Yayın No: 190.
• Gökmen, Y. (2009), Stokastik Programlama ile Optimal Portföy Oluşturma, Yayınlanmamış Doktora Tezi, Gazi Üniversitesi, Sosyal Bilimler Enstitüsü.
• İskenderoğlu, Ö., S. Akdağ (2017), “Bulanık Ortalama Mutlak Sapma Modeli ile Portföy Optimizasyonu: BİST 30 Örneği”, International Journal of Social Science Research, 6(2), 102-113.
• Johnson, C. (2018), Investing with The Kelly Criterion Model, The Finbox Blog, April. https://finbox.com/blog/investing-with-the-kelly-criterion-model, E.T.: 24.05. 2020.
• Kelly, J. L. (1956), “A New Interpretation of Information Rate”, Bell Labs Technical Journal, 35(4), 917–926.
• Kim, G., J. H. Shin (2017), “A Comparison of the Kelly Criterion and a Mean-Variance Model to Portfolio Selection with KOSPI 200”, Industrial Engineering & Management Systems, 16(3), 392-399.
• Klega, D. (2013), My Venturesare not in one Bottom Trusted: Comparative Study to Modern Portfolio Theory and Black-Litterman Portfolio Formation, Unpublished Rigorosum Thesis, Charles University, Faculty of Social Sciences.
• Kocenda, E., M. Moravcová (2019), “Exchange Rate Comovements, Hedging and Volatility Spillovers on New EU Forex Markets”, Journal of International Financial Markets, Institutions & Money, 58, 42-64.
• Krokhmal, P., S. Uryasev, J. Palmquist (2002), “Portfolio Optimization with Conditional Value-at-Risk Objective and Constraints”, Journal of Risk, 4(2), 43-68.
• Kuepper, J. (2020), Using the Kelly Criterion for Asset Allocation and Money Management, Investopedia, March, https://www.investopedia.com/articles/trading/04/091504.asp, E.T. : 12.05.2020.
• Kurnaz, E. (2019), Markowitz Ortalama-Varyans ve Black-Litterman Modelleri ile Oluşturulan Portföylerin Karşılaştırılması: BIST 100 Endeksi Şirketleri Üzerine Bir Uygulama, Yayımlanmamış Yüksek Lisans Tezi, Mersin Üniversitesi, Sosyal Bilimler Enstitüsü.
• Leiva, J. (2018), The Kelly criretion, Quandtrade, February, https://quantdare.com/kelly-criterion/, E.T.: 08.05.2020.
• Levell, C. L. (2010), Risk Parity: In The Spotlight After 50 Years, NEPC, March, http://sdcera.granicus.com/MetaViewer.php?view_id=4&clip_id=98&meta_id=12225, E.T.: 12.05.2020.
• Maillard, S., Roncalli, T. ve J. Teiletche (2010), “The Properties of Equally Weighted Risk Contribution Portfolios”, The Journal of Portfolio Management Summer, 36(4), 60-70.
• Markowitz, H. (1952), “Portfolio Selection”, The Journal of Finance, 7(1), 77-91.
• Mendelson, M., A. Berger, D. Villalon (2011), Risk Parity, Risk Management and the Real World, AQR Capital Management, Fall, https://www.aqr.com/Insights/Research/White-Papers/Risk-Parity-Risk-Management-and-the-Real-World, E.T.: 12.05.2020.
• Mercurio, P. J., Y. Wu, H. Xie (2020), “Portfolio Optimization for Binary Options Based on Relative Entropy”, Entropy, 22(7), 1-21.
• Özdemir, M. (2011), “Genetik Algoritma Kullanılarak Portföy Seçimi”, İktisat İşletme ve Finans, 26(299), 43-66.
• Pandari, A. R., A. Azar, A. R. Shavazi (2012), “Genetic Algorithms for Portfolio Selection Problems with Non-Linear Objectives”, African Journal of Business Management, 6(20), 6209-6216.
• Pflug, G. (2000), “Some Remarks on the Value-at-Risk and the Conditional Value-at-Risk”, in S. Uryasev (ed.), Probabilistic Constrained Optimization: Methodology and Applications, Netherlands: Kluwer Academic Publishers, 1-11.
• Poundstone, W. (2005), Fortune's Formula: The Untold Story of the Scientific Betting System That Beat the Casinos and Wall Street, New York: HillandWang.
• Qian, E. (2005), Risk Parity Portfolios: Efficient Portfolios Through True Diversification, Panagora, September. https://www.panagora.com/assets/Pan Agora-Risk-Parity-Portfolios-Efficient-Portfolios-Through-True-Diversificati on, pdf, E.T.: 16.05.2020.
• Raffinot, T. (2016), Hierarchical Clustering Based Asset Allocation, Working Paper, PSL Research University, Paris, France, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2840729, E.T.: 16.05.2020.
• Rockafellar, R. T., S. Uryasev (2000), “Optimization of Conditional Value at-Risk”, Journal of Risk, 2(3), 21–41.
• Roncalli, T. (2014), Introduction to Risk Parity and Budgeting, Florida: CRC Press Taylor & Francis Group.
• Sarwar, G., C. Mateus, N. Todorovic (2017), “US Sector Rotation with Five factor Fama–French Alphas”, Journal of Asset Management, 19, 116-132.
• Schneider, C. (2009), How Useful is the Information Ratio to Evaluate the Performance of Portfolio Managers?, Hamburg: GRIN Verlag GmbH.
• Shen, W., B. Wang, J. Pu, J. Wang (2019), “The Kelly Growth Optimal Portfolio with Ensemble Learning”, The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), Hawaii, USA, January 27–February 1, 2019.
• Solatikia, F., E. Kiliç, G.W. Weber (2014), “Fuzzy Optimization for Portfolio Selection based on Embedding Theorem in Fuzzy Normed Linear Spaces”, Journal of Management, Informatics and Human Resources, 47(2), 90-97.
• Uyar, U., H. Küçükşahin (2017), “Portföy Seçiminde Expected Maximum Drawdown Yaklaşımı: BIST100 ve S&P500 Uygulaması”, Business and Economics Research Journal, 8(4), 727-748.
• Uygurtürk, H., T. Korkmaz (2015), “Portföy Optimizasyonunda Markowitz Modelinin Kullanımı: Bireysel Emeklilik Yatırım Fonları Üzerine Bir Uygulama”, Muhasebe ve Finansman Dergisi, 68, 67-82.
• Yakut, E., A. Çankal (2016), “Çok Amaçlı Genetik Algoritma ve Hedef Programlama Metotlarını Kullanarak Hisse Senedi Portföy Optimizasyonu: BIST 30’da Bir Uygulama”, Business and Economics Research Journal, 7(2), 43-62.
• Zhang, L. (2010), “Research Progress on the Kelly Game”, Physics Procedia, 3,1957–1965.