ABSTRACT This study compares four broadly available industry classification schemes in a variety of applications common to capital market research. Standard Industrial Classification (SIC) codes have been available since 1939 but are being replaced by North American Industry Classification System (NAICS) codes. The Global Industry Classifications Standard (GICS) SM system, jointly developed by Standard & Poor's and Morgan Stanley Capital International (MSCI), is popular among financial practitioners, whereas the Fama and French [1997] algorithm is used primarily by academics. Our results show that GICS classifications are significantly better at explaining stock return comovements, as well as cross‐sectional variations in valuation multiples, forecasted and realized growth rates, research and development expenditures, and various key financial ratios. The GICS advantage is consistent from year to year and is most pronounced among large firms. The other three methods differ little from each other in most applications.

We document several factors that help explain cross-sectional variations in the post-revision price drift associated with analyst forecast revisions. First, the market does not make a sufficient distinction between revisions that provide new information (“high-innovation” revisions) and revisions that merely move toward the consensus (“low-innovation” revisions). Second, the price adjustment process is faster and more complete for “celebrity” analysts (Institutional Investor All-Stars) than for more obscure yet highly accurate analysts (Wall Street Journal Earnings-Estimators). Third, controlling for other factors, the price adjustment process is faster and more complete for firms with greater analyst coverage. Finally, a substantial portion of the delayed price adjustment occurs around subsequent earnings-announcement and forecast-revision dates. Collectively, these findings show that more subtle aspects of an earnings revision signal can hinder the efficacy of market price discovery, particularly in firms with relatively low analyst coverage, and that subsequent earnings-related news events serve as catalysts in the price discovery process.