Portfolio selection with an alternative measure of risk: Computational performances of Particle Swarm Optimization and Genetic Algorithms

In the classical model for portfolio selection the risk is measured by the variance of returns. Recently several alternative measures of risk have been proposed. In this contribution we focus on a class of measures that uses information contained both in lower and in upper tail of the distribution of the returns. We consider a nonlinear mixed-integer portfolio selection model which takes into account several constraints used in fund management practice. The latter problem is NPhard in general, and exact algorithms for its minimization, which are both effective and efficient, are still sought at present. Thus, to approximately solve this model we experience the heuristics Particle Swarm Optimization and we compare the performances of this methodology with respect to another well-known heuristic technique for optimization problems, that is Genetic Algorithms.