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Optimización de Portafolios de Inversión con Algoritmos Genéticos

Author

Listed:
  • Vanessa Fernandez Cortez

    (Universidad Autónoma del Estado de México)

  • David Valle Cruz

    (Universidad Autónoma del Estado de México)

  • Pedro Enrique Lizola Margolis

    (Universidad Autónoma del Estado de México)

Abstract

Los inversionistas buscan maximizar el rendimiento de sus acciones con el mínimo riesgo, situación que se torna compleja. El objetivo del trabajo de investigación, es generar portafolios de inversión conformados por acciones de las empresas nacionales emisoras que participan en la Bolsa Mexicana de Valores para el segundo semestre de 2018. La metodología se basa en aplicar algoritmos genéticos, a un modelo matemático lineal, para conformar portafolios eficientes que establezcan el monto óptimo de inversión en cada uno de los instrumentos financieros seleccionados, además de generar diversificación. Después de varias pruebas con el algoritmo genético, el portafolio optimizado quedó conformado por acciones de las empresas: Alsea, Arca Continental, Autlan, Banco del Bajío, Banorte, Cemex, G México, Mexichem, Peñoles, Pinfra y Vitro. Al invertir las cantidades calculadas en cada una de las acciones, de las emisoras, se obtiene un rendimiento óptimo con diversificación.

Suggested Citation

  • Vanessa Fernandez Cortez & David Valle Cruz & Pedro Enrique Lizola Margolis, 2019. "Optimización de Portafolios de Inversión con Algoritmos Genéticos," Revista de Investigación en Ciencias Contables y Administrativas, Universidad Michoacana de San Nicolás de Hidalgo, Facultad de Contaduría y Ciencias Administrativas, vol. 4(2), pages 111-125, June.
    • Handle: RePEc:snh:journl:v:4:y:2019:i:2:p:111-125
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    References listed on IDEAS

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