Comparison of the Performance of Genetic and Hunting Search Algorithms in Portfolio Optimization Using Mean-Variance Model Based on Fuzzy Logic in Tehran Stock Exchange

Abstract

Asset return is associated with uncertainty and always occurs during unexpected fluctuations in economic, social and political conditions, and so forth. In return on assets such as stocks. Fuzzy logic can be one of the best options for modelling asset returns. For this purpose, a rule based fuzzy expert system has been developed to support investment managers in their mid term investment decisions. Considering the non linearity of the portfolio selection problem and its NP Hard, the performance of the proposed fuzzy system is evaluated by the information of 157 companies that have been active in Tehran Stock Exchange between 2008 to 2018 using of Fuzzy Genetic and Fuzzy Hunting Search Algorithms. The system's performance in terms of risk taking and duration of investment was comparable to average market returns. Besides, the performance of the proposed fuzzy system for the risk averse investor in the short run yields good results.

Keywords

• markowitz model
• fuzzy logic
• genetic algorithm
• hunting search algorithm