of the considered set of stocks. Specifically, in both cases the stocks belonging to the energy sector form a cluster. Fig. \[dendroALCA\] shows that for the ALCA dendrogram the node $\alpha_2$ splits the stocks in two sets, one composed by two technology stocks and one composed by the financial stocks plus the IBM. For the SLCA dendrogram the separation of the set in the technology and financial subsectors is less sharp (see Fig. \[dendroSLCA\]). However in general hierarchical methods perform quite well in identifying groups of stocks belonging to the same economic sector (Mantegna, 1999; Bonanno et al., 2001; Coronnello et al., 2005).

In addition to the hierarchical trees and to the related filtered correlation matrices one can also obtain correlation based networks. Here we briefly recall how to select a correlation based graph out of the complete graph describing the system. A complete graph is a graph with links connecting all the elements (or nodes in the graph terminology) of the system of interest. In correlation based networks a weigth, which is monotonically related to the correlation coefficient of each pair of elements, can be associsted with each link. Therefore one can immediately associates a weighted completed graph with 