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Investment Stock Portfolio with Multi-Stage Genetic Algorithm Optimization

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Soft Computing as Transdisciplinary Science and Technology

Part of the book series: Advances in Soft Computing ((AINSC,volume 29))

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Abstract

Portfolio optimization problem decides the percentage of the overall portfolio value allocated to each portfolio component with specified risk-return characteristics. A multi-stage stochastic optimization manages portfolio in constantly changing financial markets by periodically rebalancing the asset portfolio to achieve return maximization and/or risk minimization. This paper presents a decision-making process that incorporates Genetic Algorithms into multi-stage portfolio optimization system. The objective function is to maximize one’s economic utility or end-of-period wealth. The performance of our system is demonstrated by optimizing the allocation of cash and various stocks in Shenzhen market of China. Experiments are conducted to compare performance of the portfolios optimized by different objective functions in terms of expected return and standard derivation.

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References

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Author information

Authors and Affiliations

  1. SPEED and Department of Computing, The Hong Kong Polytechnic University, Hong Kong

    Man-Chung Chan, Chi-Cheong Wong & W. D. Luo

  2. GERAD and Ecole Polytechique de Montreal, Canada

    Bernard K -S Cheung

Authors
  1. Man-Chung Chan
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  2. Chi-Cheong Wong
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  3. W. D. Luo
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  4. Bernard K -S Cheung
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science, Oklahoma State University, North Greenwood Avenue 700, 74106, Tulsa, USA

    Ajith Abraham

  2. Dept. of Computer Science and Systems Engineering, Muroran Institute of Technology, Mizumoto-cho 27-1, 050-8585, Muroran, Hokkaido, Japan

    Yasuhiko Dote

  3. Dept. of Computational Science and Engineering, Nagoya University, Furo-cho, Chikusa-ku, 464-8601, Nagoya, Aichi-ken, Japan

    Takeshi Furuhashi

  4. Institut für Sicherheits-/Prüftechnik, Fraunhofer IPK Berlin, Pascalstr. 8-9, 10587, Berlin, Germany

    Mario Köppen

  5. Division of Complex Systems Technology Graduate School of Engineering, University of Hokkaido, N-13, W-8 060 KITA-KU, Sapporo, Japan

    Azuma Ohuchi

  6. PRESTO Japan Science & Technology Corp. GSSM, University of Tsukuba, 3-29-1 Otsuka, Bunkyo-ku, 12-0012, Tokyo, Japan

    Yukio Ohsawa

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© 2005 Springer-Verlag Berlin Heidelberg

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Chan, MC., Wong, CC., Luo, W.D., Cheung, B.K.S. (2005). Investment Stock Portfolio with Multi-Stage Genetic Algorithm Optimization. In: Abraham, A., Dote, Y., Furuhashi, T., Köppen, M., Ohuchi, A., Ohsawa, Y. (eds) Soft Computing as Transdisciplinary Science and Technology. Advances in Soft Computing, vol 29. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32391-0_117

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  • DOI: https://doi.org/10.1007/3-540-32391-0_117

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25055-5

  • Online ISBN: 978-3-540-32391-4

  • eBook Packages: EngineeringEngineering (R0)

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