Analysis of stock market structure by identifying connected components in the market graph

We consider a recently introduced network-based representation of the U.S. stock market referred to as the market graph, which has been shown to follow the power-law model. We propose a computationally efficient technique for identifying clusters of similar stocks in the market by partitioning the market graph into a set of connected components. It turns out that these groups have specific structure, where each cluster corresponds to certain industrial segments. Moreover, the size of these connected components is consistent with the theoretical properties of the power-law model.