İMKB 30 İNDEKSİNİ OLUŞTURAN HİSSE SENETLERİ İÇİN PARÇACIK SÜRÜ OPTİMİZASYONU YÖNTEMLERİNE DAYALI PORTFÖY OPTİMİZASYONU

Yıl 2015, Cilt: 16 Sayı: 1, 25 - 33, 01.01.2015

Azize Zehra Çelenli , Erol Eğrioğlu Burçin Şeyda Çorba

Öz

1950 yılına kadar menkul kıymet çeşidi arttıkça portföy riskinin azalacağı savunulmaktadır. Getirisi yüksek olan menkul kıymetlere yatırım yapılmasını öneren geleneksel portföy teorisi, ortalama varyans modelinin geliştirilmesi ve böylece modern portföy teorisinin temellerinin ortaya atılması ile terk edilmiştir. Ortalama varyans modeli matematiksel programlama yöntemleri ile çözümlenmektedir. Son yıllarda portföy optimizasyonunda, yapay zeka yöntemleri kullanılmaktadır. Bu çalışmada klasik ve garanti yakınsamalı parçacık sürü optimizasyonu yöntemleri İMKB 30 indeksini oluşturan hisse senetlerinden oluşacak portföy optimizasyonu için uygulanmış ve elde edilen sonuçlar matematiksel programlamadan elde edilen sonuçlar ile karşılaştırılmıştır.

Anahtar Kelimeler

Portföy Optimizasyonu, Parçacık Sürü Optimizasyonu, Ortalama Varyans Modeli

PARTICLE SWARM OPTIMIZATION METHODS BASED ON PORTFOLIO OPTIMIZATION FOR IMKB 30 STOCK SHARES

Öz

It had been asserted that the more kind of instruments meant the less risk of portfolio until 1950. Conventional portfolio theory that proposed to invest for instruments was given up after mean- variance model was proposed and modern portfolio theory was established. The mathematical programming techniques are used to solve mean-variance model. In recent years, artificial intelligence methods have been employed for portfolio optimization. In this study, standard and guaranteed convergence particle swarm optimization methods have been applied to optimize the portfolio that contains IMKB 30 stock shares. The results are compared to the other results that are obtained through mathematical programming

Anahtar Kelimeler

Portfolio Optimization, Particle Swarm Optimization, Mean-Variance Model

Kaynakça

• CHANG, T.J., MEADE, N., BEASLEY, J.E., & SHARAIHA, Y. M. (2000). Heuristics forcardinality constrained portfolio optimisation. Computers & Operations Research, 27, ss. 1271–1302.
• CRAMA, Y., SCHYNS, M. (2003). Simulated annealing for complex portfolio selection problems. European Journal of Operational Research, 150, ss. 546– 571.
• CHEN, W., ZHANG, R.T., CAI, Y.M., XU F.S. (2006). Particle swarm optimization for constrained portfolio selection problems. In: Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian, 2006, pp. 2425–2429.
• CURA T., (2009). Particle swarm optimization approach to portfolio optimization. Nonlinear Analysis: Real World Applications, 10 (4), ss. 2396-2406.
• DERIGS, U., NICKEL, N.H. (2004). On a local-search heuristic for a class of tracking error minimization problems in portfolio management. Annals of Operations Research, 131, ss. 45–77.
• DOERNER, K., GUTJAHR, W.J., HARTL, R.F., STRAUSS C., STUMMER, C. (2004). Pareto Ant Colony Optimization: A Metaheuristic Approach to Multiobjective Portfolio Selection, Annals of Operations Research, 131, ss. 79-99.
• FERNANDEZ, A., GOMEZ, S. (2007). Portfolio selection using neural networks. Computers & Operations Research, 34, ss. 1177–1191.
• KENNEDY, J., EBERHART, R. (1995). Particle Swarm Optimization, IEEE International Conference on Neural Networks, 1995. Proceedings. , Volume 4, ss. 1942-1948.
• KONNO, H., YAMAZAKI, H. (1991). Mean-absolute deviation portfolio in optimization model and its application to Tokyo stock market. Management Science, 37 , ss. 519–531.
• MARKOWITZ, H. (1952). Portfolio Selection. The Journal of Finance.7 (1), ss. 77-91.
• OH, K.J., KIM, T.Y., MIN, S. (2005). Using genetic algorithm to support portfolio optimization for index fund management. Expert Systems with Applications, 28, ss. 371– 379.
• SHARPE, W.F. (1963). A Simplified Model for Portfolio Analysis. Management Science, 9 (2), ss. 277–93.
• VAN DEN BERG ve ENGELBRECH, T. A. (2002). A new locally convergent particle swarm optimizer. Proceedings of IEEE Conference on Systems, Man and Cybernetics, (Hammamet, Tunusia).
• YANG, X. (2006). Improving portfolio efficiency: A genetic algorithm approach. Computational Economics, 28, ss. 1–14.
• ZHU H., WANG Y., WANG K., CHEN Yun. (2011). Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem. Expert System with Applications, 38 (8), ss. 10161-10169.