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Portfolio Optimization Problems in Different Input Data using Particle Swarm Optimization

by Zeinodin Alizadeh, Hossein Panahian
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 50 - Number 2
Year of Publication: 2012
Authors: Zeinodin Alizadeh, Hossein Panahian
10.5120/7743-0795

Zeinodin Alizadeh, Hossein Panahian . Portfolio Optimization Problems in Different Input Data using Particle Swarm Optimization. International Journal of Computer Applications. 50, 2 ( July 2012), 23-26. DOI=10.5120/7743-0795

@article{ 10.5120/7743-0795,
author = { Zeinodin Alizadeh, Hossein Panahian },
title = { Portfolio Optimization Problems in Different Input Data using Particle Swarm Optimization },
journal = { International Journal of Computer Applications },
issue_date = { July 2012 },
volume = { 50 },
number = { 2 },
month = { July },
year = { 2012 },
issn = { 0975-8887 },
pages = { 23-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume50/number2/7743-0795/ },
doi = { 10.5120/7743-0795 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:47:17.017163+05:30
%A Zeinodin Alizadeh
%A Hossein Panahian
%T Portfolio Optimization Problems in Different Input Data using Particle Swarm Optimization
%J International Journal of Computer Applications
%@ 0975-8887
%V 50
%N 2
%P 23-26
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Portfolio optimization and diversification as a tool for development and understanding of financial markets have been entered in financial management topics for guide investors to make appropriate decision. Markowitz's modern portfolio theory (MPT) has been the most successful achievement in this field. Since, stock has a non-linear behavior in stock market; the need to non-linear models in order to identify the behavior of stock in relation to portfolio's optimum selection should be felt. Because of successful performance of evolutionary algorithms, these algorithms can provide suitable methods for investors. In this paper, particle swarm optimization (PSO) algorithm employed to select optimum portfolio. Proposed approach was tested on monthly and yearly data set of fifty top companies accepted in Tehran Stocks Exchange which were collected from March 2007 to September 2011. PSO performance compared with genetic algorithm (GA) and artificial bee colony algorithm (ABC) in term of Sharp ratio. The computational results show that the PSO algorithm impressively outperforms GA and ABC, and monthly data is better criterion than yearly data in portfolio selection.

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Index Terms

Computer Science
Information Sciences

Keywords

Portfolio Optimization Modern Portfolio Theory Particle Swarm Optimization Sharpe Ratio

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