作者: David Quintana , Sandra García-Rodríguez , Silvano Cincotti , Pedro Isasi
DOI: 10.1080/18756891.2015.1084707
关键词: Portfolio optimization 、 Multi-objective optimization 、 Process (computing) 、 Robustness (computer science) 、 Set (abstract data type) 、 Computational mathematics 、 Mathematical optimization 、 Mathematics 、 Resampling 、 Random matrix
摘要: AbstractFinding the optimal weights for a set of financial assets is difficult task. The mix real world constrains and uncertainty derived from fact that process based on estimates parameters likely to be inaccurate, often result in poor results. This paper suggests combination filtering mechanism random matrix theory with time-stamped resampled evolutionary multiobjective optimization algorithms enhances robustness forecasted efficient frontiers.