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Swarm Intelligence Algorithms for Portfolio Optimization

  • Hanhong Zhu
  • Yun Chen
  • Kesheng Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6145)

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.

Keywords

Swarm Intelligence (SI) Ant Colony Optimization (ACO) Particle Swarm Optimization (PSO) Portfolio Optimization (PO) Sharpe Ratio (SR) 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Hanhong Zhu
    • 1
    • 2
  • Yun Chen
    • 1
  • Kesheng Wang
    • 2
  1. 1.School of Public Economics & AdministrationShanghai University of Finance and Economics (SUFE)ShanghaiChina
  2. 2.Department of Production and Quality EngineeringNorwegain University of Science and Technology (NTNU) 

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