Hybrid metaheuristics for constrained portfolio selection problems

Portfolio selection is a problem arising in finance and economics. While its basic formulations can be efficiently solved using linear or quadratic programming, its more practical and realistic variants, which include various kinds of constraints and objectives, have in many cases to be tackled by heuristics. In this work, we present a hybrid technique that combines a local search metaheuristic, as master solver, with a quadratic programming procedure, as slave solver. Experimental results show that the approach is very promising, as it regularly provides the optimal solution and thus achieves results comparable, or superior, to state-of-the-art solvers, including widespread commercial software tools (CPLEX 11.0.1 and MOSEK 5). The paper reports a detailed analysis of the behavior of the technique in various constraint settings, thus demonstrating how the performance is dependent on the features of the instance.