Particle swarm optimised analysis of investment decision

Abstract A portfolio forecasting model based on particle swarm optimization (PSO) algorithm with automatic factor scaling is proposed in this Article to effectively improve the accuracy of related analysis model in portfolio application. Firstly, the portfolio problem is analyzed and a hybrid constraint portfolio model is obtained by improving portfolio model with consideration of general portfolio model and combination of market value constraint and upper bound constraint according to Markowitz's theory. Secondly, PSO algorithm is introduced during analysis on portfolio model and solution is found with the hybrid constraint portfolio model of PSO on portfolio. In addition, in order to further improve the performance of PSO in model solution, automatic factor scaling is used for adaptive learning on parameters associated with PSO, to improve convergence of the algorithm. At last, simulation experiments show that the algorithm proposed can obtain a more ideal investment portfolio scheme, thereby reducing investment risks and obtaining greater investment returns.