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We review some methods recently used in the literature to detect theexistence of a certain degree of common behavior of stock returns belonging tothe same economic sector. Specifically, we discuss methods based on randommatrix theory and hierarchical clustering techniques. We apply these methods toa set of stocks traded at the New York Stock Exchange. The investigated timeseries are recorded at a daily time horizon.  All the considered methods are able to detect economic information and thepresence 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 methodcould not be able to extract all the economic information present in thecorrelation coefficient matrix of a set of stocks.Comment: 13 pages, 8 figures, 2 Table

Economic sector identification in a set of stocks traded at the New York Stock Exchange: a comparative analysis