Solving Multi-objective Portfolio Optimization Problem for Saudi Arabia Stock Market Using Hybrid Clonal Selection and Particle Swarm Optimization

Abstract

The portfolio is a group of assets held by an institution or a private individual. Each asset has an investment share of the total investment. The investor tries to distribute the investment to these different assets. The main issue in portfolio optimization is the allocation of different assets for maximum return and minimum risk within a suitable time. These two objectives lead to the multi-objective portfolio optimization problem, which must be solved. Several previous studies have addressed this issue. In this article, we propose a new intelligence hybrid evolutionary algorithm that combines clonal selection with particle swarm optimization to optimize the portfolio’s return and risk. We then show the results of the proposed solution through experiments that are conducted using stocks in Kingdom of Saudi Arabia stock exchange market (Tadawul). Moreover, we compare our hybrid algorithm, clonal selection and particle swarm optimization-based solution.