Dynamic Search Space Particle Swarm Optimization Approach for Portfolio Optimization

The multi-objective programming model of portfolio investment is based on the Markowitz portfolio theory with risk and return considered in the meantime. There have been many studies for portfolio optimization problem and over recent years heuristic techniques are widely used and proved to have good performance. The main purpose of the present study is the solving of portfolio optimization problem by using Particle Swarm Optimization (PSO). Thus in this paper, we propose an approach based on a dynamic search space particle swarm optimization algorithm (DSSPSO) for the portfolio selection problem. DSSPSO is proposed to improve the performance of PSO combining the classical particle swarm optimization algorithm philosophy and population entropy. To verify the effectiveness of the algorithm, we used the closing prices of thirty sample stocks in Chinese stock market and carried out several sets of experiments. The results show that DSSPSO approach is suitable in portfolio optimization and is able to find securities portfolio with certain interests at low risk. Also we evaluate the effect of the value of risk aversion parameter on the results and found that the algorithm can effectively control risk. Furthermore, two groups of contrast experiments are carried out to substantiate the conclusion and suggest the application for future predictions.