Portfolio Model Based on Improved Particle Swarm Optimization Algorithm
Meng Wu · Jisuanji fangzhen · 2013
The classical mean-variance model is used to study the problem of optimizing the actual return rate of asset,and the problem of the actual return rate can not be well dealt with in real world for the model's extremely sensitivity to input parameters.In order to solve the problem better,a bi-criteria portfolio model based on l∞ risk function was established.As for the discontinuity of objective function in the model,an improved particle swarm optimization algorithm was chosen to solve it.And considering optimal and suboptimal locations,crossover operations in genetic algorithm were introduced in the algorithm.In simulation experiments,with real data from stocks market it was obtained that the new model has less value at risk than mean-variance model,meanwhile it performs better in the actual return rate.