Is Industry Classification Useful to Predict U.S. Stock Price Co-Movements?

The first factor explaining stock price co-movements is the equity risk premium: Stocks co-move because they are exposed to equity risk. But what is the second factor? The most popular method investors use to classify stocks is by type of business. This is useful for comparison purposes, e.g., to assess the fundamental data of a company with respect to its peers, and it is also supposed to predict stock price co-movements. The underlying idea is that each industrial sector responds to macroeconomic factors, economic policies, and news in a different and unique way. Many studies point out the importance of industries (Held [2009], Blitzer and Maitland [2009], Cavaglia et al. [2004] and Boillat et al. [2002]), suggesting that starting in the 1990s they replaced countries in developed international markets as a second factor explaining stock price co-movements. For the importance of countries before the 1990s, the interested reader can refer to Heston and Rouwenhorst [1995].

The economic explanation of this empirically observed phenomenon relies on the globalization and increased internationalization of large companies (Cavaglia et al. [2004] and Boillat et al. [2002]). It comes as no surprise that portfolio managers, risk managers, and asset allocators use industry classifications to drive investment decisions, to control portfolio risk, and to perform strategic asset allocation. The recent rise in assets allocated to sector exchange-traded funds (ETFs) (Lydon [2013]) clearly shows that market participants endorse industry groupings.

In this article, we focus on the U.S. domestic equity market and try to answer the following questions: Are industries still a predominant factor in explaining U.S. stock market co-movements? And how do they compare to data-driven categorizations? To answer these questions, we derive a datadriven categorization of stocks, which relies exclusively on stock price time-series, and compare it with the standard categorization. The latter relies on the use of revenues as a key measure of company business activity, although often earnings and market perception are also used. Previous studies analyzed the efficacy of industry classifications and compared different providers (Horrell and Meraz [2009] and Nadig and Crigger [2011]). Cluster analysis was used in Arnott [1980] to derive a multiple-factor risk model. More recently, it was used in Chan et al. [2007] to check the quality of industry classifications, reaching the...