Industry classifications are used by investors, economists, and policy makers for a great variety of purposes. The traditional economic‐activity‐based systems (Global Industry Classification Standard, North American Industry Classification System, Standard Industrial Classification, and Fama–French) have been supplemented in recent years by alternative classification systems. Our purpose is to provide another alternative system that forms classification groups based on the structure of firm financial statements. Using cluster analysis, a multivariate tool that forms groups where their characteristics are similar within groups and distinct across groups, we form clusters of large U.S. firms based on their common‐size financial statements (percentage breakdowns of balance sheets and income statements). We characterize the 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. Our results demonstrate that using financial clusters and industry groups together proves advantageous relative to using either alone.

中文翻译：

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金融集群，行业集团和股票收益率的相关性

投资者，经济学家和政策制定者将行业分类用于多种目的。近年来，传统的基于经济活动的系统（全球行业分类标准，北美行业分类系统，标准行业分类和Fama-French）已得到替代分类系统的补充。我们的目的是提供另一个可替代的系统，该系统基于公司财务报表的结构形成分类组。使用聚类分析（一种多元的工具，可以形成各组之间的特征相似且各组之间的特征不同的组），我们根据共同规模的财务报表（资产负债表和损益表的百分比细分）形成大型美国公司的集群。我们根据其行业分类和其他经济信息来表征金融集群，并分别和联合评估金融集群和行业集团解释所有公司对的股票收益率相关性的能力。我们的研究结果表明，相对于单独使用金融集群和行业集团而言，事实证明是有利的。