Selection of optimal portfolio by using improved Non-Dominated Sorting Genetic Algorithm and Evolutionary Algorithm Strength Pareto By taking risk on the basis of conditional value at risk

Subject Areas : Journal of Investment Knowledge

Mojtaba Moradi ¹ , Maryam Ghavidel ²

1 - Assistant Professor, University of Guilan (Corresponding Author)
2 - MSc in Financial Mathematics

Received: 2017-05-04 Accepted : 2017-08-19 Published : 2018-12-22

Keywords: Non-Dominated Sorting improved, Evolutionary Algorithm Strengt, portfolio optimization, Conditional Value at Risk (CVa,

Abstract :

Portfolio selection problem is one of the most important economic issues. The right combination of stock or other asset portfolio is that an investor pays to buy it. Selection of an optimal portfolio is based on the principle that the investor decides to accept one or several investments among different investment depending on the tolerance of risk and expected a reasonable amount of stock returns. In this study, improved Non-Dominated Sorting multi-objective genetic algorithms and Evolutionary Algorithm Strength Pareto are used to create an optimum portfolio. These algorithms are improved version of their previous versions and have a better solution than its previous versions. The value of the portfolio and its risk, as optimization purposes and conditional value at risk as the basis risk, have been used. Two applied conditions consider to Portfolio and shown that the Evolutionary Algorithm Strength Pareto‌ has better results than the Non-Dominated Sorting Genetic Algorithm II.      

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