Financial clusters, industry groups, and stock return correlations

Abstract

Investors and analysts classify firms to conduct valuations or to evaluate performance. The industry groupings usually rely on SIC, NAIC, GICS, or Fama-French classifications. Our purpose is to form groups of companies based on the structure of their financial statements. Using cluster analysis, a multivariate tool that can form groups where their characteristics are similar within groups and distinct across groups, we form clusters of large U.S. stocks based on their common size financial statements (percentage breakdowns of balance sheets and income statements). We characterize these financial clusters based on their industry classifications and other economic information and assess the ability of financial clusters and industry groups, separately and jointly, to explain stock return correlations of all pairs of firms. Using financial clusters and industry groups proves superior to using either alone.