Abstract:

The particle swarm optimization (PSO) has a strong capability of global search, but it easily falls into global extremum. Besides, it can only solve the continuity problems. In order to improve these problems, we present a discrete complex method of local search, which can enhance the search capability when solving discrete problems. Since the PSO is easy to fall into local minimum, we introduce the adaptive particle migration operation to ensure the diversity of particles and avoid falling into local convergence effectively. Simulation experiments adopt the CVaR risk measurement method to measure portfolio risks, and establish an optimization mean-CVaR model which contains the transaction costs and the limitation proportion of the assets. Experimental results verify the effectiveness of the algorithm. Compared with other algorithms, the improved PSO algorithm has higher precision and stability.

Key words: portfolio optimization, improved particle swarm optimization, discrete complex method