CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Portfolio Optimization Problems in Different Input Data using Particle Swarm Optimization

by Zeinodin Alizadeh, Hossein Panahian
journal cover thumbnail

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.

References
  1. Markowitz, H. M. 1952. Portfolio selection. J Finance 7:77–91.
  2. Fernandez, A. and Gomez, S. 2007. Portfolio selection using neural networks, Computers & Operations Research 34, 1177–1191.
  3. Oh, K. J. , Kim, T. Y. and Min, S. 2005. Using genetic algorithm to support portfolio optimization for index fund management, Expert Systems with Applications 28, 371–379.
  4. Chang, T. J. , Meade, N. , Beasley, J. E. , and Sharaiha, Y. M. 2000. Heuristics for cardinality constrained portfolio optimization, Computers & Operations Research 27, 1271–1302.
  5. Yang, X. 2006 . Improving portfolio efficiency: A genetic algorithm approach, Computational Economics 28, 1–14.
  6. Loraschi, A. , Tettamanzi, A. , Tomassini, M. , Svizzero, C. , Scienti?co, C. , & Verda, P. 1995. Distributed genetic algorithms with an application to portfolio selection. In D. W. Pearson, N. C. Steele, & R. F. Albrecht (Eds. ), Proceedings of the international conference on arti?cial neural networks and genetic algorithms (ICANNGA95) (pp. 384–387). Berlin: Springer-Verlag.
  7. Rolland, E. 1996. A tabu search method for constrained real-number search: Applications to portfolio selection. Columbus: Ohio State University, Department of Accounting & Management Information Systems.
  8. Crama, Y. and Schyns, M. 2003. Simulated annealing for complex portfolio selection problems, European Journal of Operational Research 150, 546–571.
  9. Derigs, U. and Nickel, N. H. 2004. On a local-search heuristic for a class of tracking error minimization problems in portfolio management, Annals of Operations Research 131, 45–77.
  10. Mansini, R. and Speranza, M. G. 1999. Heuristic algorithms for the portfolio selection problem with minimum transaction lots, European Journal of Operational Research 114, 219–233.
  11. Derigs, U. and Nickel, N. H. 2003. Meta-heuristic based decision support for portfolio optimization with a case study on tracking error minimization in passive portfolio management, OR Spectrum 25, 345–378.
  12. Schlottmann, F. and Seese, D. A. 2004. hybrid heuristic approach to discrete multi-objective optimization of credit portfolios, Computational Statistics & Data Analysis 47, 373–399.
  13. Chang, T. , Meade, T. , Beasley, J. E. and Sharaiha , Y. M. 2000. Heuristics for Cardinality constrained Portfolio Optimization . Computer & Operations Research, 27. 12711302.
  14. Sharpe, W. F. 1966. Mutual fund performance. The Journal of Business, 39, 119–138.
  15. Eberhart, R. C. , and Kennedy, J. 1995. "A New Optimizer Using Particle Swarm Theory. " Proceedings of the 6th International Symposium on Micro Machine and Human Science. Nagoya, Japan 39-432.
  16. Shi Y. H. , Eberhart R. C. 1998. "A modified particle swarm optimizer. " in: Proc. of IEEE World Conf. on Computation
Index Terms

Computer Science
Information Sciences

Keywords

Portfolio Optimization Modern Portfolio Theory Particle Swarm Optimization Sharpe Ratio

Powered by PhDFocusTM