Stochastic Portfolio Selection Using Breeding Swarm Optimization

In this study, the portfolio selection problem is concerned, in case that expected return rates are stochastic variables and the breeding swarm algorithm is applied to solve this problem. The first, the stochastic portfolio model and reliable decision are presented. The second, the global evolutionary computation algorithm-breeding swarm is proposed in order to overcome the computational complexity and local and global searching limitation of traditional optimization methods. Finally, a numerical example of portfolio selection problem is given. Findings endorse the effectiveness of the newly proposed algorithm in comparison to particle swarm optimization method. The results show that the breeding swarm approach to portfolio selection has high potential to achieve better solution and higher convergence.