Handling uncertainty through confidence intervals in portfolio optimization.

The approach proposed here uses evolutionary algorithms combined with interval analysis to optimize the allocation of resources in portfolio optimization. The proposal uses probabilistic confidence intervals to characterize the solutions. Such characterization allows the investor to consider not only the expected impact of the portfolios but also the risk of not obtaining that expected impact. This approach identifies the behavior of the investor in the face of risk and gives her/him support depending on her/his own preferences. Portfolio optimization is performed through one of the most outstanding evolutionary multi-objective approaches, the so-called Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D). To the best of our knowledge, this algorithm has not been used in the context of interval analysis. In this work, MOEA/D has been enhanced so that it can deal with chromosomes and fitness values described as interval numbers.