ARTÍCULO
TITULO

Solving Constrained Mean-Variance Portfolio Optimization Problems Using Spiral Optimization Algorithm

Werry Febrianti    
Kuntjoro Adji Sidarto and Novriana Sumarti    

Resumen

Portfolio optimization is an activity for balancing return and risk. In this paper, we used mean-variance (M-V) portfolio models with buy-in threshold and cardinality constraints. This model can be formulated as a mixed integer nonlinear programming (MINLP) problem. To solve this constrained mean-variance portfolio optimization problem, we propose the use of a modified spiral optimization algorithm (SOA). Then, we use Bartholomew-Biggs and Kane?s data to validate our proposed algorithm. The results show that our proposed algorithm can be an efficient tool for solving this portfolio optimization problem.

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