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Integer programming approaches in mean-risk models

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  • Hiroshi Konno
  • Rei Yamamoto
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    Abstract

    This paper is concerned with porfolio optimization problems with integer constraints. Such problems include, among others mean-risk problems with nonconvex transaction cost, minimal transaction unit constraints and cardinality constraints on the number of assets in a portfolio. These problems, though practically very important have been considered intractable because we have to solve nonlinear integer programming problems for which there exists no efficient algorithms. We will show that these problems can now be solved by the state- of-the-art integer programming methodologies if we use absolute deviation as the measure of risk. Copyright Springer-Verlag Berlin/Heidelberg 2005

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    File URL: http://hdl.handle.net/10.1007/s10287-005-0038-9
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    Bibliographic Info

    Article provided by Springer in its journal Computational Management Science.

    Volume (Year): 4 (2005)
    Issue (Month): 4 (November)
    Pages: 339-351

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    Handle: RePEc:spr:comgts:v:4:y:2005:i:4:p:339-351

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    Related research

    Keywords: Portfolio optimization; mean-absolute deviation model; integer constraints; integer programming;

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    Cited by:
    1. Walter Murray & Howard Shek, 2012. "A local relaxation method for the cardinality constrained portfolio optimization problem," Computational Optimization and Applications, Springer, vol. 53(3), pages 681-709, December.
    2. Philipp Baumann & Norbert Trautmann, 2013. "Portfolio-optimization models for small investors," Computational Statistics, Springer, vol. 77(3), pages 345-356, June.
    3. Enrico Angelelli & Renata Mansini & M. Speranza, 2012. "Kernel Search: a new heuristic framework for portfolio selection," Computational Optimization and Applications, Springer, vol. 51(1), pages 345-361, January.

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