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A Naïve Approach To Speed Up Portfolio Optimization Problem Using A Multiobjective Genetic Algorithm / Una Aproximación Ingenua Para Acelerar El Programa De Optimización De Carteras Usando Un Algoritmo Genético Multiobjetivo


  • Baixauli-Soler, J. Samuel

    () (Universidad de Murcia (España))

  • Alfaro-Cid, Eva

    () (Universidad Politécnica de Valencia (España))

  • Fernández-Blanco, Matilde O.

    () (Universidad de Valencia (España))


Genetic algorithms (GAs) are appropriate when investors have the objective of obtaining mean-variance (VaR) efficient frontier as minimising VaR leads to non-convex and non-differential risk-return optimisation problems. However GAs are a time-consuming optimisation technique. In this paper, we propose to use a naïve approach consisting of using samples split by quartile of risk to obtain complete efficient frontiers in a reasonable computation time. Our results show that using reduced problems which only consider a quartile of the assets allow us to explore the efficient frontier for a large range of risk values. In particular, the third quartile allows us to obtain efficient frontiers from the 1.8% to 2.5% level of VaR quickly, while that of the first quartile of assets is from 1% to 1.3% level of VaR. / Los algoritmos genéticos son apropiados cuando los inversores tienen el propósito de obtener la frontera eficiente media-VaR, ya que minimizar el VaR ocasiona que el problema de optimización rentabilidad-riesgo no sea ni convexo ni diferencial. Sin embargo, los algoritmos genéticos son una técnica de optimización que exige mucho tiempo de computación. En este artículo proponemos usar una aproximación naïve, consistente en dividir la muestra por cuartiles de riesgo para obtener la frontera eficiente en un tiempo razonable. Nuestros resultados muestran que usando problemas reducidos que sólo consideran un cuartil de los activos podemos explorar la frontera eficiente para un mayor número de niveles de riesgo. Concretamente, la muestra del tercer cuartil permite obtener rápidamente fronteras eficientes con un VaR entre el 1,8 y el 2,5%, mientras que el primer cuartil permite obtener las carteras eficientes con niveles de VaR entre el 1 y el 1,3%.

Suggested Citation

  • Baixauli-Soler, J. Samuel & Alfaro-Cid, Eva & Fernández-Blanco, Matilde O., 2012. "A Naïve Approach To Speed Up Portfolio Optimization Problem Using A Multiobjective Genetic Algorithm / Una Aproximación Ingenua Para Acelerar El Programa De Optimización De Carteras Usando Un Algoritm," Investigaciones Europeas de Dirección y Economía de la Empresa (IEDEE), Academia Europea de Dirección y Economía de la Empresa (AEDEM), vol. 18(2), pages 126-131.
  • Handle: RePEc:idi:jiedee:v:18:y:2012:i:2:p:126-131

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    More about this item


    Efficient portfolio; Genetic algorithm; Value-at-Risk; Cartera eficiente; Algoritmo genético; Valor en riesgo;

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access


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