Dynamic Financial Asset Allocation Strategy Based on Particle Swarm Optimization Algorithm

Abstract

With the continuous improvement and development of the financial market, the allocation of financing assets has become a hot topic. In the financial market, the imbalance of capital supply and demand is very common. Therefore, in order to grasp the financial dynamics in time, it is necessary to conduct relevant analysis on its asset allocation. In order to improve the accuracy of calculation, this paper proposes the application of particle swarm optimization algorithm. This paper mainly uses digital modeling and data comparison to study the dynamic financial asset allocation strategy. The experimental results show that the TSVL-DPM model has the strongest ability to predict asset allocation, and the minimum error is 2.01.