Comparative evaluation of fuzzy logic and genetic algorithms models for portfolio optimization

Abstract

Selection of optimum methods which have appropriate speed and precision for planning and decision-making has always been a challenge for investors and managers. One the most important concerns for them is investment planning and optimization for acquisition of desirable wealth under controlled risk with the best return. This paper proposes a model based on Markowitz theorem by considering the aforementioned limitations in order to help effective decisionsmaking for portfolio selection. Then, the model is investigated by fuzzy logic and genetic algorithms, for the optimization of the portfolio in selected active companies listed in Tehran Stock Exchange over the period 2012-2016 and the results of the above models are discussed. The results show that the two studied models had functional differences in portfolio optimization, its tools and the possibility of supplementing each other and their selection.