Abstract: 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.
CITATION STYLE
Quintana, D., García-Rodríguez, S., Cincotti, S., & Isasi, P. (2015). Combining RMT-based filtering with time-stamped resampling for robust portfolio optimization. International Journal of Computational Intelligence Systems, 8(5), 874–885. https://doi.org/10.1080/18756891.2015.1084707
Mendeley helps you to discover research relevant for your work.