Swarm intelligence algorithms for portfolio optimization

Authors:
Hanhong Zhu;Yun Chen;Kesheng Wang
Affiliations:
,School of Public Economics & Administration, Shanghai University of Finance and Economics (SUFE), Shanghai, China;School of Public Economics & Administration, Shanghai University of Finance and Economics (SUFE), Shanghai, China;Department of Production and Quality Engineering, Norwegain University of Science and Technology (NTNU)
Venue:
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Year:
2010

Citing 2
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Abstract

Swarm Intelligence (SI) is a relatively new technology that takes its inspiration from the behavior of social insects and flocking animals In this paper, we focus on two main SI algorithms: Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) An extension of ACO algorithm and a PSO algorithm has been implemented to solve the portfolio optimization problem, which is a continuous multi-objective optimization problem. The portfolio optimization model considered in this paper is based on the classical Markowitz mean-variance theory The results show ACO performs better than PSO in the case of small-scale and large-scale portfolio, but in the case of medium-scale portfolio, PSO performs a better than ACO technique.