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Financial clusters, industry groups, and stock return correlations
Author(s) -
Fodor Andy,
Jorgensen Randy D.,
Stowe John D.
Publication year - 2021
Publication title -
journal of financial research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.319
H-Index - 49
eISSN - 1475-6803
pISSN - 0270-2592
DOI - 10.1111/jfir.12236
Subject(s) - stock (firearms) , balance sheet , multivariate statistics , classification scheme , finance , cluster (spacecraft) , business , economics , variety (cybernetics) , financial ratio , actuarial science , financial economics , computer science , geography , archaeology , information retrieval , programming language , machine learning , artificial intelligence
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.