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

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.

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

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

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