Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

Portfolio Optimization Problems in Different Input Data using Particle Swarm Optimization

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 50 - Number 2
Year of Publication: 2012
Zeinodin Alizadeh
Hossein Panahian

Zeinodin Alizadeh and Hossein Panahian. Article: Portfolio Optimization Problems in Different Input Data using Particle Swarm Optimization. International Journal of Computer Applications 50(2):23-26, July 2012. Full text available. BibTeX

	author = {Zeinodin Alizadeh and Hossein Panahian},
	title = {Article: Portfolio Optimization Problems in Different Input Data using Particle Swarm Optimization},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {50},
	number = {2},
	pages = {23-26},
	month = {July},
	note = {Full text available}


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.


  • Markowitz, H. M. 1952. Portfolio selection. J Finance 7:77–91.
  • Fernandez, A. and Gomez, S. 2007. Portfolio selection using neural networks, Computers & Operations Research 34, 1177–1191.
  • 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.
  • 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.
  • Yang, X. 2006 . Improving portfolio efficiency: A genetic algorithm approach, Computational Economics 28, 1–14.
  • 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.
  • 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.
  • Crama, Y. and Schyns, M. 2003. Simulated annealing for complex portfolio selection problems, European Journal of Operational Research 150, 546–571.
  • 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.
  • 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.
  • 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.
  • 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.
  • Chang, T. , Meade, T. , Beasley, J. E. and Sharaiha , Y. M. 2000. Heuristics for Cardinality constrained Portfolio Optimization . Computer & Operations Research, 27. 12711302.
  • Sharpe, W. F. 1966. Mutual fund performance. The Journal of Business, 39, 119–138.
  • 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.
  • Shi Y. H. , Eberhart R. C. 1998. "A modified particle swarm optimizer. " in: Proc. of IEEE World Conf. on Computation