Abstract
In this paper we describe a method to analyze the structure and dynamics of the 30 largest North American companies. The method combines the tools of symbolic time series analysis (Daw et al. in Rev Sci Instrum 74:916–930, 2003) with the nearest neighbor single linkage clustering algorithm (Mantegna and Stanley in An introduction to econophysics: Correlations and complexity in finance, Cambridge University Press, UK, 2000). Data symbolization allows to obtain a metric distance between two different time series that is used to construct a minimal spanning tree allowing to compute an ultrametric distance. From the analysis of time series data of companies included in Dow Jones Industrial Average, we derive a hierarchical organization of these companies. In particular, we detect different clusters of companies which correspond with their common production activities or their strong interrelationship. The obtained classification of companies can be used to study deep relationships among different branch of economic activities and to construct financial portfolios.