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Finding the optimal weights for a set of financial assets is a difficult task. The mix of real world constrains and the uncertainty derived from the fact that process is based on estimates for parameters that likely to be inaccurate, often result in poor results. This paper suggests that a combination of a filtering mechanism based on random matrix theory with time-stamped resampled evolutionary multiobjective optimization algorithms enhances the robustness of forecasted efficient frontiers.
portfolio optimization; filtering; robustness; multi objective optimization; random matrix theory; selection; stability; risk; algorithms