GAs and PSO: two metaheuristic methods for portfolio optimization

Abstract

The thesis begins with the description of the Markowitz model for optimal portfolio selection. Limitations and improvements of such model are described. In the second chapter the concept of metaheuristics is introduced, focusing on two particular metaheuristics: Genetic Algorithms and Particle Swarm Optimization. These concepts are introduced as alternative optimization methods. In the following chapter a portfolio to optimize is chosen as well as the risk measure to use for the portfolio selection model. In the fourth chapter the two metaheuristics, genetic algorithms and particle swarm optimization, are applied in order to find the optimal portfolio. At the end comparisons between the two methods are provided and conclusions are made.