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Fuzzy Portfolio Selection Problems Based on Credibility Theory

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Advances in Machine Learning and Cybernetics

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3930))

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Abstract

We first deduce the variance formulas of normal, triangular and trapezoidal fuzzy variables in credibility theory. Then two classes of fuzzy portfolio selection models are built based on credibility measure, the expected value and variance of a fuzzy variable. To solve the proposed models, a genetic algorithm is employed. Finally, two numerical examples are provided for the proposed portfolio selection models to test the designed algorithm.

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Author information

Authors and Affiliations

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

    Yanju Chen, Yan-Kui Liu & Junfen Chen

Authors
  1. Yanju Chen
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  2. Yan-Kui Liu
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  3. Junfen Chen
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Editor information

Editors and Affiliations

  1. Department of Computing, Hong Kong Polytechnic University, P.O. Box, Hong Kong, China

    Daniel S. Yeung

  2. School of Creative Media, City University of Hong Kong,, China

    Zhi-Qiang Liu

  3. Department of Mathematics and Computer Science, Hebei University, 071002, Baoding, Hebei, P.R. China

    Xi-Zhao Wang

  4. School of Electrical and Information Engineering, University of Sydney, 2006, NSW, Australia

    Hong Yan

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© 2006 Springer-Verlag Berlin Heidelberg

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Chen, Y., Liu, YK., Chen, J. (2006). Fuzzy Portfolio Selection Problems Based on Credibility Theory. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_40

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  • DOI: https://doi.org/10.1007/11739685_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33584-9

  • Online ISBN: 978-3-540-33585-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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