Financial portfolio optimization is a challenging task. One of the major difficulties is managing the uncertainty arising from different aspects of the process. This paper suggests a solution based on [Formula: see text]-neighborhoods that, combined with a time-stamped resampling mechanism, increases the robustness of the solutions. The approach is tested on four of the most popular evolutionary multiobjective algorithms over a long period of time. This results in a significant enhancement in the reliability of the estimated efficient frontier.
Financial Portfolio Optimization using Monte Carlo and Operation Research
