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Multi-stage stochastic mean–semivariance–CVaR portfolio optimization under transaction costs

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  • Najafi, Amir Abbas
  • Mushakhian, Siamak

Abstract

In this paper, we present a model for portfolio selection, characterized on the basis of three parameters: the expected value, semivariance, and Conditional Value-at-Risk (CVaR) at a specified confidence level. In order to solve the proposed model, we design a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. Because the effectiveness of meta-heuristic algorithms significantly depends on the proper choice of parameters, a Taguchi experimental design method is applied to set the suitable values of parameters to improve the hybrid algorithm performance. Finally, some numerical examples are given to illustrate the effectiveness of the proposed algorithm.

Suggested Citation

  • Najafi, Amir Abbas & Mushakhian, Siamak, 2015. "Multi-stage stochastic mean–semivariance–CVaR portfolio optimization under transaction costs," Applied Mathematics and Computation, Elsevier, vol. 256(C), pages 445-458.
    • Handle: RePEc:eee:apmaco:v:256:y:2015:i:c:p:445-458
    DOI: 10.1016/j.amc.2015.01.050
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    2. Najafi, Amir Abbas & Pourahmadi, Zahra, 2016. "An efficient heuristic method for dynamic portfolio selection problem under transaction costs and uncertain conditions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 154-162.
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    10. Kashanian, Motahareh & Pishvaee, Mir Saman & Sahebi, Hadi, 2020. "Sustainable biomass portfolio sourcing plan using multi-stage stochastic programming," Energy, Elsevier, vol. 204(C).

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