应用于投资组合中的粒子群优化算法本文研究传统的投资组合方法“均值—方差”模型，并在此基础模型上，加入交易费用等约束条件，在大量的数据的情况下，发现马克威茨的不足，从而引出粒子群优化算法，并对PSO算法进行研究与改进，提出PSO-TANW和PSO-ATANW以及PSO-LIW三种方法，并且在改进的“均值—方差”的投资组合模型下，通过调整风险厌恶值的大小，我们能得出PSO-ATANW是求解投资组合优化更为有效的方法。
[关键词]：投资组合；PSO算法；改进PSO算法
Particle Swarm Optimization applied to portfolio Optimization
[Abstract]: The traditional portfolio approach " to mean - variance model, and on the basis of this model , adding transaction costs and other constraints in the case of large amounts of data and found that Markowitz of the lack of which leads to particle swarm optimization and PSO algorithm and proposed to improve the PSO the - TANW and PSO - ATANW and PSO - LIW three methods , and improved mean - variance portfolio model through the adjustment of the size of the risk aversion values , we can the draw PSO - ATANW is the solving portfolio optimization more effective way .
[Keywords]: portfolio ; the PSO algorithm ; improved PSO algorithm
*若需了解更多与协助请咨询↓→[电脑QQ][手机QQ]【数据协助】
