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International Conference on Natural Computation

ICNC 2005: Advances in Natural Computation pp 735–743Cite as

Dynamical Proportion Portfolio Insurance with Genetic Programming

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3611))

Abstract

This paper proposes a dynamic proportion portfolio insurance (DPPI) strategy based on the popular constant proportion portfolio insurance (CPPI) strategy. The constant multiplier in CPPI is generally regarded as the risk multiplier. Since the market changes constantly, we think that the risk multiplier should change accordingly. This research identifies factors relating to market volatility. These factors are built into equation trees by genetic programming. Experimental results show that our DPPI strategy is more profitable than traditional CPPI strategy.

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Author information

Authors and Affiliations

  1. Department of Information Management, National Central University, Jungli, Taniwan, 320

    Jiah-Shing Chen & Chia-Lan Chang

Authors
  1. Jiah-Shing Chen
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  2. Chia-Lan Chang
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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Chen, JS., Chang, CL. (2005). Dynamical Proportion Portfolio Insurance with Genetic Programming. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_104

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  • DOI: https://doi.org/10.1007/11539117_104

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28325-6

  • Online ISBN: 978-3-540-31858-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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