METASEZGİSEL ALGORİTMALARLA PORTFÖY OPTİMİZASYONU: BIST 30 UYGULAMASI

Yıl 2022, Cilt: 7 Sayı: 1, 164 - 176, 31.03.2022

Danyel Bekdaş Hicabi Ersoy

https://doi.org/10.29106/fesa.1084231

Cited By: 1

Öz

Hisse senetleri günlük fiyat değişimlerine bağlı olarak yatırım kategorisinde riskli varlıklar olarak sınıflandırılmaktadır. Risk ile getiri pozitif yönlü ilişki olmakla beraber, risk ile getiri arasında denge kurmak portföy yönetimi açısından oldukça önemlidir. Yapay zeka algoritmaları ile hisse senetlerinin getirisi ve risk olarak ifade edilen standart sapmaları dikkate alınarak getiri ile risk arasında optimizasyon analizi yapılmaktadır. Çalışma kapsamında son dönemde geliştirilmiş yapay zeka algoritmalarından Jaya Algoritması, Öğrentme-Öğretme Tabanlı Algoritma ve Çiçek Tozlaşma Algoritmaları tanıtılmakta, bu algoritmalar kullanılarak BIST 30 hisse senetleri için optimizasyon yapılmakta ve bu üç algoritmadan elde edilen sonuçlar kıyaslanmaktadır.

Anahtar Kelimeler

Portföy Getirisi, Portföy standart sapması, Risk, Optimizasyon, Yapay Zeka Algoritmaları, Finansal Analiz.

PORTFOLIO OPTIMIZATON WITH METAHEURISTIC ALGORITHMS: BIST 30 APPLICATION

Öz

Stocks can be classified as risky financial instruments considering volatility in stock prices. Considering positive relationship between return and risk, its very important to balance the risk and return in portfolio management.
Optimization analysis is made between return and risk with artificial intelligence algorithms by taking into account return and risk of stocks which expressed as the standard deviations. Within the scope of the study; Jaya Algorithm, Teaching-Learing Based Algorithm and Flower Pollination Algorithms, which are recently developed artificial intelligence algorithms, are introduced, optimization is made for BIST 30 stocks by using these algorithms and the results obtained from these three algorithms are compared.

Anahtar Kelimeler

Portfolio Return, Portfolio Standard Deviation, Risk, Optimization, Artificial Intelligence Algorithms, Financial Analysis.

Kaynakça

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