Study of Security Investment Optimizing Combination Based on PSACO

Abstract

Based on Markowitzpsila theory of asset portfolio, a multi-factor and optimal model for portfolio investment in the condition of considering friction factors in China security market is established. A hybrid methodology PSACO (particle swarm ant colony optimization) combining particle swarm optimization with ant colony optimization algorithm is applied to solve the model. Both particle swarm optimization (PSO) and ant colony optimization (ACO) are co-operative, population-based global search swarm intelligence meta-heuristics. PSO is inspired by social behavior of bird flocking or fish schooling, while ACO imitates foraging behavior of real life ants. In this study, we employ a pheromone-guided mechanism to improve the performance of PSO method. Additionally, the model is implemented on the demonstrated research of the index stock of index 30, the result could provide scientific foundation for security investment.