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Linear Programming Methods For Solving The Portfolio’S Problems

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  • TKACENKO, Alexandra

    (“Victor Slăvescu” Centre for Financial and Monetary Research, Romanian Academy)

Abstract

It is well known that the portfolio optimization involves creating the stock portfolio minimizing the risk for a required return or maximizing the return for a given risk level. The mathematic model of these kind of problem is one of quadratic programming type. Because the solving procedure of these type of models is more complicated, in the proposed work will bring alternative models for solving a portfolio’s problem. Particularly in the paper is proposed some techniques and considerations for non-linear portfolio’s model transformation in one of linear or linear fractional type. The last ones leads to streamline the process of solving the initial model. The proposed methods have been verified practically on several examples and have been found very effective.

Suggested Citation

  • TKACENKO, Alexandra, 2014. "Linear Programming Methods For Solving The Portfolio’S Problems," Journal of Financial and Monetary Economics, Centre of Financial and Monetary Research "Victor Slavescu", vol. 1(1), pages 216-221.
    • Handle: RePEc:vls:rojfme:v:1:y:2014:i:1:p:216-221
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    References listed on IDEAS

    as
    1. Włodzimierz Ogryczak, 2000. "Multiple criteria linear programming model for portfolio selection," Annals of Operations Research, Springer, vol. 97(1), pages 143-162, December.
    2. Hiroshi Konno & Hiroaki Yamazaki, 1991. "Mean-Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo Stock Market," Management Science, INFORMS, vol. 37(5), pages 519-531, May.
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    More about this item

    Keywords

    portfolio; risk; benefit; linear programming; fractional programming;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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