Using Genetic Algorithm in Solving Stochastic Programming for Multi-Objective Portfolio Selection in Tehran Stock Exchange

Abstract

Investor decision making has always been affected by two factors: risk and returns. Considering risk, the investor expects an acceptable return on the investment decision horizon. Accordingly, defining goals and constraints for each investor can have unique prioritization. This paper develops several approaches to multi criteria portfolio optimization. The maximization of stock returns, the power of liquidity of selected stocks and the acceptance of risk to market risk are set as objectives of the problem. In order to solve the problem of information in the Tehran Stock Exchange in 2017, 45 sample stocks have been identified and, with the assumption of normalization of goals, a genetic algorithm has been used. The results show that the selected model provides a good performance for selecting the optimal portfolio for investors with specific goals and constraints.