Investment portfolio optimization with transaction costs through a multiobjective genetic algorithm: an applied case to the Colombian Stock Exchange

Authors

  • Samuel De Greiff Universidad EAFIT
  • Juan Carlos Rivera Universidad EAFIT

DOI:

https://doi.org/10.18046/j.estger.2018.146.2812

Keywords:

Genetic algorithms, Portfolio optimization, Mean-variance model, Transaction costs, Multiobjective optimization

Abstract

This paper discusses portfolio optimization by considering constraints imposed by financial markets and conditions of projects with excess liquidity, such as transaction costs, limited budget and short time planning horizons. In light of these constraints, conventional models are found to generate non-efficient portfolios. Consequently, a mathematical model is formulated and a multiobjective genetic algorithm is implemented in order to find efficient portfolios in the Colombian Stock Exchange (Bolsa de Valores de Colombia), minimizing risks and maximizing profits. In addition, results are shown which allow comparison between those portfolios obtained through the proposed model and the mean-variance model, highlighting the importance of transaction costs and budget in investment decision making.

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Author Biographies

  • Samuel De Greiff, Universidad EAFIT

    Investigador, Departamento de Organización y Gerencia, Escuela de Administración, Universidad EAFIT, Medellín, Colombia.

  • Juan Carlos Rivera, Universidad EAFIT

    Profesor Investigador, Departamento de Ciencias Matemáticas, Escuela de Ciencias, Universidad EAFIT, Medellín, Colombia.

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Published

2018-03-30

Issue

Section

Research articles

How to Cite

Investment portfolio optimization with transaction costs through a multiobjective genetic algorithm: an applied case to the Colombian Stock Exchange. (2018). Estudios Gerenciales, 34(146), 74-87. https://doi.org/10.18046/j.estger.2018.146.2812