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Swarm Intelligence for Cardinality-Constrained Portfolio Problems

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Computational Collective Intelligence. Technologies and Applications (ICCCI 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6423))

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Abstract

This work presents Particle Swarm Optimization (PSO), a collaborative population-based swarm intelligent algorithm for solving the cardinality constraints portfolio optimization problem (CCPO problem). To solve the CCPO problem, the proposed improved PSO increases exploration in the initial search steps and improves convergence speed in the final search steps. Numerical solutions are obtained for five analyses of weekly price data for the following indices for the period March, 1992 to September, 1997: Hang Seng 31 in Hong Kong, DAX 100 in Germany, FTSE 100 in UK, S&P 100 in USA and Nikkei 225 in Japan. The computational test results indicate that the proposed PSO outperformed basic PSO algorithm, genetic algorithm (GA), simulated annealing (SA), and tabu search (TS) in most cases.

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Author information

Authors and Affiliations

  1. Department of Management Information Systems, National Chengchi University, 64, Sec. 2, Chihnan Rd., Wenshan Dist., Taipei, 116, Taiwan ROC

    Guang-Feng Deng & Woo-Tsong Lin

Authors
  1. Guang-Feng Deng
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  2. Woo-Tsong Lin
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Editor information

Editors and Affiliations

  1. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, 415 Chien-Kung Road, 807, Kaohsiung, Taiwan

    Jeng-Shyang Pan

  2. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, #43, Sec.4, Keelung Rd., 106, Taipei, Taiwan

    Shyi-Ming Chen

  3. Institute of Informatics, Wroclaw University of Technology, Str. Wyb. Wyspianskiego 27, 50-370, Wroclaw, Poland

    Ngoc Thanh Nguyen

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© 2010 Springer-Verlag Berlin Heidelberg

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Deng, GF., Lin, WT. (2010). Swarm Intelligence for Cardinality-Constrained Portfolio Problems. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2010. Lecture Notes in Computer Science(), vol 6423. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16696-9_44

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  • DOI: https://doi.org/10.1007/978-3-642-16696-9_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16695-2

  • Online ISBN: 978-3-642-16696-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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