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Portfolio selection problems in practice: a comparison between linear and quadratic optimization models

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  • Francesco Cesarone
  • Andrea Scozzari
  • Fabio Tardella
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    Abstract

    Several portfolio selection models take into account practical limitations on the number of assets to include and on their weights in the portfolio. We present here a study of the Limited Asset Markowitz (LAM), of the Limited Asset Mean Absolute Deviation (LAMAD) and of the Limited Asset Conditional Value-at-Risk (LACVaR) models, where the assets are limited with the introduction of quantity and cardinality constraints. We propose a completely new approach for solving the LAM model, based on reformulation as a Standard Quadratic Program and on some recent theoretical results. With this approach we obtain optimal solutions both for some well-known financial data sets used by several other authors, and for some unsolved large size portfolio problems. We also test our method on five new data sets involving real-world capital market indices from major stock markets. Our computational experience shows that, rather unexpectedly, it is easier to solve the quadratic LAM model with our algorithm, than to solve the linear LACVaR and LAMAD models with CPLEX, one of the best commercial codes for mixed integer linear programming (MILP) problems. Finally, on the new data sets we have also compared, using out-of-sample analysis, the performance of the portfolios obtained by the Limited Asset models with the performance provided by the unconstrained models and with that of the official capital market indices.

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    File URL: http://arxiv.org/pdf/1105.3594
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    Bibliographic Info

    Paper provided by arXiv.org in its series Papers with number 1105.3594.

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    Date of creation: May 2011
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    Handle: RePEc:arx:papers:1105.3594

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    Web page: http://arxiv.org/

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    1. Carlo Acerbi & Dirk Tasche, 2001. "On the coherence of Expected Shortfall," Papers cond-mat/0104295, arXiv.org, revised May 2002.
    2. Canakgoz, N.A. & Beasley, J.E., 2009. "Mixed-integer programming approaches for index tracking and enhanced indexation," European Journal of Operational Research, Elsevier, vol. 196(1), pages 384-399, July.
    3. Philippe Artzner & Freddy Delbaen & Jean-Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228.
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    Cited by:
    1. Taras Bodnar & Nestor Parolya & Wolfgang Schmid, 2012. "On the Equivalence of Quadratic Optimization Problems Commonly Used in Portfolio Theory," Papers 1207.1029, arXiv.org, revised Apr 2013.

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