  Given a set of assets and an investment capital, the classical portfolio
selection problem consists in determining the amount of capital to be invested
in each asset in order to build the most profitable portfolio. The portfolio
optimization problem is naturally modeled as a mean-risk bi-criteria
optimization problem where the mean rate of return of the portfolio must be
maximized whereas a given risk measure must be minimized. Several mathematical
programming models and techniques have been presented in the literature in
order to efficiently solve the portfolio problem. A relatively recent promising
line of research is to exploit clustering information of an assets network in
order to develop new portfolio optimization paradigms. In this paper we endow
the assets network with a metric based on correlation coefficients between
assets' returns, and show how classical location problems on networks can be
used for clustering assets. In particular, by adding a new criterion to the
portfolio selection problem based on an objective function of a classical
location problem, we are able to measure the effect of clustering on the
selected assets with respect to the non-selected ones. Most papers dealing with
clustering and portfolio selection models solve these problems in two distinct
steps: cluster first and then selection. The innovative contribution of this
paper is that we propose a Mixed-Integer Linear Programming formulation for
dealing with this problem in a unified phase. The effectiveness of our approach
is validated reporting some preliminary computational experiments on some real
financial dataset.
