Extended mean-variance model for reliable evolutionary portfolio optimization

Real world optimization of financial portfolios pose a challenging multiobjective problem that can be tackled using Evolutionary Algorithms. The fact that the optimization process is subject to the presence of uncertainty concerning asset returns is likely to lead to unreliable solutions. This work suggests extending the classic mean-variance optimization problem with a third explicit robustness objective. This results on sets of portfolios that can be subsequently grouped together according to their reliability. This additional information allows for a better informed decision making regarding asset allocation.