METASEZGİSEL ALGORİTMALARLA PORTFÖY OPTİMİZASYONU: BIST 30 UYGULAMASI

Year 2022, , 164 - 176, 31.03.2022

Danyel Bekdaş , Hicabi Ersoy

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

Cited By: 1

Abstract

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.

Keywords

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

PORTFOLIO OPTIMIZATON WITH METAHEURISTIC ALGORITHMS: BIST 30 APPLICATION

Abstract

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.

Keywords

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

References

• Chang, Jui-Fang, Tien Chin Wang, and Yuan-Tzu Min. "Using Genetic Algorithms to construct a low-risk fund portfolio based on the Taiwan 50 Index." 2010 International Conference on Computational Aspects of Social Networks. IEEE, 2010.
• Cura, Tunchan. "Particle swarm optimization approach to portfolio optimization." Nonlinear analysis: Real world applications 10.4 (2009): 2396-2406.
• Çankal, Ahmet. "Genetik Algoritma Kullanarak Hisse Senedi Portföy Optimizasyonu: BİST-30’DA Bir Uygulama." (2015).
• Çelenli, Azize Zehra, Erol Eğrioğlu, and Burçin Şeyda Çorba. "İMKB 30 indeksini oluşturan hisse senetleri için parçacık sürü optimizasyonu yöntemlerine dayalı portföy optimizasyonu." (2015).
• Çelenli A (2018). Yapay Arı Kolonisi Algoritması ile Sharpe Performans Oranına Dayalı Portföy Optimizasyonu: BIST 30 Uygulaması Doktora Tezi Doktora Tezi, Ondokuz Mayıs Üniversitesi.
• Chen, Angela HL, Yun-Chia Liang, and Chia-Chien Liu. "An artificial bee colony algorithm for the cardinality-constrained portfolio optimization problems." 2012 IEEE Congress on Evolutionary Computation. IEEE, 2012. Gandomi, A.H., Yang, X.S., Talatahari, S., Alavi, A.H. (Eds.), 2013, Metaheuristic Algorithms in Modeling and Optimization, Metaheuristic Applications in Structures and Infrastructures, Elsevier, ISBN: 9780123983640, 1-24.
• Golmakani, Hamid Reza, and Mehrshad Fazel. "Constrained portfolio selection using particle swarm optimization." Expert Systems with Applications 38.7 (2011): 8327-8335.
• Holland J H (1975). Adaptation in natural and artificial systems. An introductoryanalysis with application to biology, control, and artificial intelligence. AnnArbor, MI: University of Michigan Press
• Karaboğa, D., Basturk, B., 2007, A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm, Journal of Global Optimization, 39(3), 459-471.
• Kartal B (2015). Yapay Arı Kolonisi Algoritması ile Finansal Portföy Optimizasyonu. Doktora Tezi Doktora Tezi, İstanbul Üniversitesi İşletme Anabilim Dalı, 143, Sosyal Bilimler Enstitüsü. (391453).
• Kennedy, James, and Russell Eberhart. "Particle swarm optimization." Proceedings of ICNN'95-international conference on neural networks. Vol. 4. IEEE, 1995.
• Koziel, S., Yang, X.S. (Eds.), 2011, Computational Optimization, Methods and Algorithms (vol. 356), Springer-Verlag, Heidelberg, Berlin, ISBN: 978-3-642-20858-4.
• Kumar, Divya, and K. K. Mishra. "Portfolio optimization using novel co-variance guided Artificial Bee Colony algorithm." Swarm and Evolutionary Computation 33 (2017): 119-130.
• Murty, K.G., 2003, Optimization models for decision making: volume 1, Ann Arbor, University Of Michigan, http://www personal.umich.edu/~murty/books/opti_model/, [Ziyaret Tarihi: 2 Temmuz 2018].
• Oh, Kyong Joo, Tae Yoon Kim, and Sungky Min. "Using genetic algorithm to support portfolio optimization for index fund management." Expert Systems with Applications 28.2 (2005): 371-379.
• Onwubolu, G.C., Babu, B.V., 2004, New Optimization Techniques in Engineering (vol. 141), Springer-Verlag, Heidelberg, Berlin, ISBN: 978-3-540-39930-8.
• Rao, R., 2016, Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems, International Journal of Industrial Engineering Computations, 7(1), 19-34.
• Rao, R. Venkata, Vimal J. Savsani, and D. P. Vakharia. "Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems." Computer-Aided Design 43.3 (2011): 303-315.
• Toğan, V., 2012, Design of planar steel frames using teaching–learning based optimization, Engineering Structures, 34, 225-232.
• Wang, Zhen, Sanyang Liu, and Xiangyu Kong. "Artificial bee colony algorithm for portfolio optimization problems." International Journal of Advancements in Computing Technology 4.4 (2012): 8-16.
• Yang, X.S. (Eds.), Koziel, S., 2011, Computational Optimization, Methods and Algorithms (vol. 356), Springer-Verlag, Heidelberg, Berlin, ISBN: 978-3-642-20858-4.
• Yang, X.S., Koziel, S., Leifsson, L., 2013, Computational optimization, modelling and simulation: recent trends and challenges, International Conference on Computational Science ICCS 2013, 5-7 Haziran 2013 Barselona-İspanya, Procedia Computer Science, 855-860.
• Yang, X.S., Bekdaş, G., Nigdeli, S.M. (Eds.), 2016, Metaheuristics and Optimization in Civil Engineering, Springer, Switzerland, ISBN: 9783319262451.
• Zhu, Hanhong, et al. "Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem." Expert Systems with Applications 38.8 (2011): 10161-1016