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A Perception-Based Portfolio Under Uncertainty: Minimization of Average Rates of Falling

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Modeling Decisions for Artificial Intelligence (MDAI 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5861))

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Abstract

A perception-based portfolio model under uncertainty is discussed. In the proposed model, randomness and fuzziness are evaluated respectively by the probabilistic expectation and the mean values with evaluation weights and λ-mean functions. The means, the variances and the covariances fuzzy numbers/fuzzy random variables are evaluated in the possibility case and the necessity case, and the rate of return with portfolios is estimated by the both random factors and imprecise factors. In the portfolio model, the average rate of falling is minimized using average value-at-risks as a coherent risk measure. By analytical approach, we derive a solution of the portfolio problem to minimize the average rate of falling. A numerical example is given to illustrate our idea.

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Author information

Authors and Affiliations

  1. Faculty of Economics and Business Administration, University of Kitakyushu, 4-2-1 Kitagata, Kokuraminami, Kitakyushu, 802-8577, Japan

    Yuji Yoshida

Authors
  1. Yuji Yoshida
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Editors and Affiliations

  1. IIIA-CSIC, Campus UAB s/n, 08193, Bellaterra, Catalonia, Spain

    Vicenç Torra

  2. Toho Gakuen, 3-1-10 Naka, Kunitachi, 186-0004, Tokyo, Japan

    Yasuo Narukawa

  3. Graduate School of Engineering Science, Osaka University, 1-3, Machikaneyama, Toyonaka, 560-8531, Osaka, Japan

    Masahiro Inuiguchi

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Yoshida, Y. (2009). A Perception-Based Portfolio Under Uncertainty: Minimization of Average Rates of Falling. In: Torra, V., Narukawa, Y., Inuiguchi, M. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2009. Lecture Notes in Computer Science(), vol 5861. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04820-3_14

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  • DOI: https://doi.org/10.1007/978-3-642-04820-3_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04819-7

  • Online ISBN: 978-3-642-04820-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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