Portfolio Optimization Using Particle Swarm Optimization and Genetic Algorithm
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Authors
Samira Kamali
- Abadan branch, Islamic Azad University, Abadan, Iran.
Abstract
This study basically employs the Markowitz mean–variance model for portfolio selection problem. Since this model is classified as a quadratic programming model there is not any efficient algorithm to solve it. The goal of this study is to find a feasible portfolio with a minimum risk through the application of heuristic algorithm. The two PSO and GA algorithm has been used. The results show that PSO approach is suitable in portfolio optimization.
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ISRP Style
Samira Kamali, Portfolio Optimization Using Particle Swarm Optimization and Genetic Algorithm, Journal of Mathematics and Computer Science, 10 (2014), no. 2, 85-90
AMA Style
Kamali Samira, Portfolio Optimization Using Particle Swarm Optimization and Genetic Algorithm. J Math Comput SCI-JM. (2014); 10(2):85-90
Chicago/Turabian Style
Kamali, Samira. "Portfolio Optimization Using Particle Swarm Optimization and Genetic Algorithm." Journal of Mathematics and Computer Science, 10, no. 2 (2014): 85-90
Keywords
- Portfolio optimization
- Particle Swarm Optimization
- Generic Algorithm.
MSC
References
-
[1]
T. J. Chang, S. Yang, K. Chang, Portfolio optimization problems in different risk measures using genetic algorithm, Expert Systems with Applications, 36 (2009), 10529–10537
-
[2]
T. Cura, Particle swarm optimization approach to portfolio optimization, Nonlinear Analysis Real World Applications, 10 (2009), 2396–2406
-
[3]
D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA (1989)
-
[4]
H. R. Golmakani, M. Fazel , Constrained Portfolio Selection using Particle Swarm Optimization, Expert Systems with Applications, 38 (2011), 8327–8335
-
[5]
R. Khalesi, H. Maleki, A new method for solving fuzzy MCDM problems, Journal of mathematics and computer Science, 1 (2010), 238-438
-
[6]
A. Loraschi, A. Tettamanzi, M. Tomassini, C. Svizzero, C. Scientifico, P. Verda, Distributed genetic algorithms with an application to portfolio selection, Proceedings of the international conference on artificial neural networks and genetic algorithms, ICANNGA95, (1995), 384–387
-
[7]
H. Markowitz , Portfolio selection, efficient diversification of investments, New York , Wiley (1959)
-
[8]
H. Markowitz, The optimization of a quadratic function subject to linear constraints, Naval Research Logistics Quarterly, 3 (1956), 111–133
-
[9]
H. Markowitz, Portfolio selection, Journal of Finance, 7 (1952), 77–91
-
[10]
F. Matroud, H. Sadeghi, Solving bi-level programming with multiple linear objectives at lower level using particle swarm optimization, Journal of mathematics and computer science, 7 (2013), 221-229
-
[11]
M. Rostami, M. Kianpour, E. bashardoust, A numerical algorithm for solving nonlinear fuzzy differential equations, Journal of mathematics and computer Science, 177 (2007), 3397-3410
-
[12]
H. Soleimani, Portfolio selection using genetic algorithm, MS Degree Thesis, Amirkabir University of Technology, Industrial Engineering Department, Tehran (2007)
-
[13]
H. Soleimani, H. R. Golmakani, M. H. Salimi , Markowitz-based portfolio selection with minimum transaction lots, cardinality constraints and regarding sector capitalization using genetic algorithm, Expert Systems with Applications, 36 (2009), 5058–5063
-
[14]
P. Wolfe, The simplex method for quadratic programming, Econometrica, 27 (1959), 382 -398
-
[15]
F. Xu, W. Chen, L. Yang, Improved Particle Swarm Optimization for realistic portfolio selection , In Eighth ACIS international conference on software engineering, artificial intelligence, networking, and parallel/distributed computing, IEEE Computer Society, (2007), 185–190