  Networks of companies can be constructed by using return correlations. A
crucial issue in this approach is to select the relevant correlations from the
correlation matrix. In order to study this problem, we start from an empty
graph with no edges where the vertices correspond to stocks. Then, one by one,
we insert edges between the vertices according to the rank of their correlation
strength, resulting in a network called asset graph. We study its properties,
such as topologically different growth types, number and size of clusters and
clustering coefficient. These properties, calculated from empirical data, are
compared against those of a random graph. The growth of the graph can be
classified according to the topological role of the newly inserted edge. We
find that the type of growth which is responsible for creating cycles in the
graph sets in much earlier for the empirical asset graph than for the random
graph, and thus reflects the high degree of networking present in the market.
We also find the number of clusters in the random graph to be one order of
magnitude higher than for the asset graph. At a critical threshold, the random
graph undergoes a radical change in topology related to percolation transition
and forms a single giant cluster, a phenomenon which is not observed for the
asset graph. Differences in mean clustering coefficient lead us to conclude
that most information is contained roughly within 10% of the edges.
