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Swarm Intelligence Algorithms for Portfolio Optimization

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Advances in Swarm Intelligence (ICSI 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6145))

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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.

This research is supported by the Shanghai key scientific and technological project (Grant No. 08DZ1120500).

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Author information

Authors and Affiliations

  1. School of Public Economics & Administration, Shanghai University of Finance and Economics (SUFE), 200433, Shanghai, China

    Hanhong Zhu & Yun Chen

  2. Department of Production and Quality Engineering, Norwegain University of Science and Technology (NTNU),  

    Hanhong Zhu & Kesheng Wang

Authors
  1. Hanhong Zhu
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  2. Yun Chen
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  3. Kesheng Wang
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Editor information

Editors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), Department of Machine Intelligence, Peking University, 100871, Beijing, China

    Ying Tan

  2. Research and Postgraduate Office, Xi’an Jiaotong-Liverpool University, 215123, Suzhoum, China

    Yuhui Shi

  3. Department of Electrical and Computer Engineering, National University of Singapore, Engineering Drive 3, 4 Engineering Drive 3, 117576, Singapore

    Kay Chen Tan

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© 2010 Springer-Verlag Berlin Heidelberg

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Zhu, H., Chen, Y., Wang, K. (2010). Swarm Intelligence Algorithms for Portfolio Optimization. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13495-1_38

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  • DOI: https://doi.org/10.1007/978-3-642-13495-1_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13494-4

  • Online ISBN: 978-3-642-13495-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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