Portfolio Selection Under Buy-In Threshold Constraints Using DC Programming and DCA

In matter of portfolio selection, we consider a generalization of the Markowitz mean-variance model which includes buy-in threshold constraints. These constraints limit the amount of capital to be invested in each asset and prevent very small investments in any asset. The new model can be converted into a NP-hard mixed integer quadratic programming problem. The purpose of this paper is to investigate a continuous approach based on DC programming and DCA (DC algorithms) for solving this new model. DCA is a local continuous approach to solve a wide variety of nonconvex programs for which it provided quite often a global solution and proved to be more robust and efficient than standard methods. Preliminary comparative results of DCA and a classical branch-and-bound algorithm is presented. These results show that DCA is an efficient and promising approach for the considered portfolio selection problem