We review some methods recently used in the literature to detect the existence of a certain degree of common
behavior of stock returns belonging to the same economic sector. Specifically, we discuss methods based on
random matrix theory and hierarchical clustering techniques. We apply these methods to a set of stocks traded
at the New York Stock Exchange. The investigated time series are recorded at a daily time horizon. All the
considered methods are able to detect economic information and the presence of clusters characterized by the
economic sector of stocks. However, different methodologies provide different information about the considered
set. Our comparative analysis suggests that the application of just a single method could not be able to extract
all the economic information present in the correlation coefficient matrix of a set of stocks.
We apply a method to filter relevant information from the correlation coefficient matrix by extracting a network of relevant interactions. This method succeeds to generate networks with the same hierarchical structure of the Minimum Spanning Tree but containing a larger amount of links resulting in a richer network topology allowing loops and cliques. In Tumminello et al.,1 we have shown that this method, applied to a financial portfolio of 100 stocks in the USA equity markets, is pretty efficient in filtering relevant information about the clustering of the system and its hierarchical structure both on the whole system and within each cluster. In particular, we have found that triangular loops and 4 element cliques have important and significant relations with the market structure and properties. Here we apply this filtering procedure to the analysis of correlation in two different kind of interest rate time series (16 Eurodollars and 34 US interest rates).