Skip to main content
SpringerLink
Log in

Symbiotic Multi-swarm PSO for Portfolio Optimization

  • Conference paper
Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence (ICIC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5755))

Included in the following conference series:

Abstract

This paper presents a novel symbiotic multi-swarm particle swarm optimization (SMPSO) based on our previous proposed multi-swarm cooperative particle swarm optimization. In SMPSO, the population is divided into several identical sub-swarms and a center communication strategy is used to transfer the information among all the sub-swarms. The information sharing among all the sub-swarms can help the proposed algorithm avoid be trapped into local minima as well as improve its convergence rate. SMPSO is then applied to portfolio optimization problem. To demonstrate the efficiency of the proposed SMPSO algorithm, an improved Markowitz portfolio optimization model including two of the most important limitations are adopted. Experimental results show that SMPSO is promising for this class of problems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Young, M.R.: A Minimax Portfolio Selection Rule with Linear Programming Solution. Management Science 44, 673–683 (1998)

    Article  MATH  Google Scholar 

  2. Arenas, M., Bilbao, A., Rodriguez Uria, M.V.: A Fuzzy Goal Programming Approach to Portfolio Selection. European Journal of Operational Research 133, 287–297 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  3. Ballestero, E., Romero, C.: Portfolio Selection: A Compromise Programming Solution. Journal of the Operational Research Society 47, 1377–1386 (1996)

    MATH  Google Scholar 

  4. Oh, K.J., Kim, T.Y., Min, S.: Using Genetic Algorithm to Support Portfolio Optimization for Index Fund Management. Expert Systems with Applications 28, 371–379 (2005)

    Article  Google Scholar 

  5. Yang, X.: Improving Portfolio Efficiency: A Genetic Algorithm Approach. Computational Economics 28, 1–14 (2006)

    Article  MATH  Google Scholar 

  6. Crama, Y., Schyns, M.: Simulated Annealing for Complex Portfolio Selection Problems. European Journal of Operational Research 150, 546–571 (2003)

    Article  MATH  Google Scholar 

  7. Fernandez, A., Gomez, S.: Portfolio Selection Using Neural Networks. Computers & Operations Research 34, 1177–1191 (2007)

    Article  MATH  Google Scholar 

  8. Derigs, U., Nickel, N.H.: On a Local-search Heuristic for a Class of Tracking Error Minimization Problems in Portfolio Management. Annals of Operations Research 131, 45–77 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  9. Derigs, U., Nickel, N.H.: 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 (2003)

    MATH  MathSciNet  Google Scholar 

  10. Schlottmann, F., Seese, D.: A Hybrid Heuristic Approach to Discrete Multi-Objective Optimization of Credit Portfolios. Computational Statistics & Data Analysis 47, 373–399 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  11. Niu, B., Zhu, Y.L., He, X.X., Wu, H.: MCPSO: A Multi-Swarm Cooperative Particle Swarm Optimizer. Applied Mathematics and Computation 185, 1050–1062 (2007)

    Article  MATH  Google Scholar 

  12. Niu, B., Zhu, Y.-l., He, X.-X.: A Multi-Population Cooperative Particle Swarm Optimizer for Neural Network Training. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3971, pp. 570–576. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  13. Niu, B., Zhu, Y.L., He, X.X., Shen, H.: A Multi-swarm Optimizer Based Fuzzy Modeling Approach for Dynamic Systems Processing. Neurocomputing 71, 1436–1448 (2008)

    Article  Google Scholar 

  14. Markowitz, H.W.: Foundations of Portfolio Theory. Journal of Finance 46, 469–477 (1991)

    Article  Google Scholar 

  15. Yang, K.Y., Wang, X.F.: Solving the Multi-solution Portfolio Selection Model Based on the GA (Chinese). Journal of ShanDong finance college 6, 60–63 (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Management, Shenzhen University, Shenzhen, Guangdong, 518060, China

    Ben Niu, Bing Xue & Li Li

  2. Faculty of Science, McMaster University, Hamilton, Ontario, L8S4L8, Canada

    Yujuan Chai

Authors
  1. Ben Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bing Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Li Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yujuan Chai
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Intelligent Machines, Chinese Academy of Sciences, China

    De-Shuang Huang

  2. Graduate School of Electrical Engineering, University of Ulsan, Korea, San 29, Mugeo-Dong, Nam-Ku, 680 - 749, Ulsan, Korea

    Kang-Hyun Jo

  3. School of Electrical Engineering, University of Ulsan, Ulsan, South Korea

    Hong-Hee Lee

  4. School of Electrical Engineering, University of Ulsan, South Korea

    Hee-Jun Kang

  5. e.B.I.S. s.r.l. (electronic Business in Security), Spin-Off of Polytechnic of Bari, Str. Prov. per Casamassima Km., 3, 70010, Valenzano, (BA), Italy

    Vitoantonio Bevilacqua

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Niu, B., Xue, B., Li, L., Chai, Y. (2009). Symbiotic Multi-swarm PSO for Portfolio Optimization. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence. ICIC 2009. Lecture Notes in Computer Science(), vol 5755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04020-7_83

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04020-7_83

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04019-1

  • Online ISBN: 978-3-642-04020-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation