PSO Heuristics Algorithm for Portfolio Optimization

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


One of the most studied problems in the financial is the intractability of the portfolios. Some practical formulations of the problem include various kinds of nonlinear constraints and objectives and can be efficiently solved by approximate algorithms. In this paper, we present a meta-heuristic algorithm named Particle Swarm Optimization (PSO) to the construction of optimal risky portfolios for financial investments. The PSO algorithm is tested on two portfolio optimization models and a comparative study with Genetic Algorithm has been implemented. The PSO model demonstrates high computational efficiency in constructing optimal risky portfolios. Preliminary results show that the approach is very promising and achieves results comparable or superior with the state of the art solvers.


Swarm Intelligence (SI) Particle Swarm Optimization (PSO) Portfolio Management (PM) Sharp Ratio (SR) Efficient Frontier (EF) 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Markowitz, H.: Portfolio Selection. The Journal of Finance 7, 77–91 (1952)CrossRefGoogle Scholar
  2. 2.
    Zhou, X.Y., Li, D.: Continuous-Time Mean-Variance Portfolio Selection: A Stochastic LQ Framework. Applied Mathematics and Optimization 42, 19–33 (2000)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Chang, T.J., Meade, N., Beasley, J.E., Sharaiha, Y.M.: Heuristics for Cardinality Constrained Portfolio Optimisation. Computers & Operations Research 27, 1271 (2000)zbMATHCrossRefGoogle Scholar
  4. 4.
    Gaspero, L.D., Tollo, G.d., Roli, A., Schaerf, A.: Hybrid Metaheuristics for Portfolio Selection Problems. In: MIC 2007 - Metaheuristics International Conference, Montreal (2007)Google Scholar
  5. 5.
    Doerner, K., Gutjahr, W., Hartl, R., Strauss, C., Stummer, C.: Pareto Ant Colony Optimization: A Metaheuristic Approach to Multiobjective Portfolio Selection. Annals of Operations Research 131, 79–99 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Giovanis, E.: The Arbitrage Pricing Theory and the Capital Asset Pricing Models and Artificial Neural Networks Modeling with Particle Swarm Optimization (PSO). SSRN eLibrary (2009)Google Scholar
  7. 7.
    Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: International Conference on Neural Networks, ICNN 1995, p. 1942 (1995)Google Scholar
  8. 8.
    Sharpe, W.F.: Mutual Fund Performance. The Journal of Business 39, 119–138 (1966)CrossRefGoogle Scholar
  9. 9.
    Bratton, D., Kennedy, J.: Defining a Standard for Particle Swarm Optimization. In: Swarm Intelligence Symposium, SIS 2007, pp. 120–127. IEEE, Los Alamitos (2007)CrossRefGoogle Scholar
  10. 10.
    Black, F.: Capital Market Equilibrium with Restricted Borrowing. The Journal of Business 45, 444–455 (1972)CrossRefGoogle Scholar
  11. 11.
    Wang, K.: Applied Computational Intelligence in Intelligent Manufacturing Systems. Advanced Knowledge International (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yun Chen
    • 1
  • Hanhong Zhu
    • 1
  1. 1.Department of Public Economics & AdministrationShanghai University of Finance and Economics (SUFE)ShanghaiChina

Personalised recommendations