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Mean-Entropy Model for Portfolio Selection with Type-2 Fuzzy Returns

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Bio-Inspired Computing and Applications (ICIC 2011)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 6840))

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Abstract

Entropy is a measurement of the degree of uncertainty. Mean-entropy method can be used for modeling the choice among uncertain outcomes. In this paper, we consider the portfolio selection problem under the assumption that security returns are characterized by type-2 fuzzy variables. Since the expectation and entropy of type-2 fuzzy variables haven’t been well defined, type-2 fuzzy variables need to be reduced firstly. Then we propose a mean-entropy model with reduced variables. To solve the proposed model, we use the entropy formula of reduced fuzzy variable and transform the mean-entropy model to its equivalent parametric form, which can be solved by standard optimization solver.

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Author information

Authors and Affiliations

  1. College of Mathematics & Computer Science, Hebei University, Baoding, 071002, Hebei, China

    Ying Liu & Yanju Chen

Authors
  1. Ying Liu
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  2. Yanju Chen
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Editor information

Editors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. School of Computer and Communication Engineering, Zhengzhou University of Light Industry, No. 5, Dongfeng Road, Jinshui District, 450002, Zhengzhou, Henan, China

    Yong Gan

  3. School of Electrical, Computer & Telecommunications Engineering, University of Wollongong, 2522, P.O. Box, North Wollongong, NSW, Australia

    Prashan Premaratne

  4. School of Computer Science and Engineering, Inha University, 402-751, Inchon, Korea

    Kyungsook Han

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© 2012 Springer-Verlag Berlin Heidelberg

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Liu, Y., Chen, Y. (2012). Mean-Entropy Model for Portfolio Selection with Type-2 Fuzzy Returns. In: Huang, DS., Gan, Y., Premaratne, P., Han, K. (eds) Bio-Inspired Computing and Applications. ICIC 2011. Lecture Notes in Computer Science(), vol 6840. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24553-4_46

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  • DOI: https://doi.org/10.1007/978-3-642-24553-4_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24552-7

  • Online ISBN: 978-3-642-24553-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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