58 Pages Posted: 6 Nov 2012 Last revised: 5 Oct 2014
Date Written: July 2, 2014
Applying a “co-search” algorithm to Internet traffic at the SEC's EDGAR web-site, we develop a novel method for identifying economically-related peer firms and for measuring their relative importance. Our results show that firms appearing in chronologically adjacent searches by the same individual (Search-Based Peers or SBPs) are fundamentally similar on multiple dimensions. In direct tests, SBPs dominate GICS6 industry peers in explaining cross-sectional variations in base firms' out-of-sample: (a) stock returns, (b) valuation multiples, (c) growth rates, (d) R&D expenditures, (e) leverage, and (f) profitability ratios. We show that SBPs are not constrained by standard industry classification, and are more dynamic, pliable, and concentrated. We also show that co-search intensity captures the degree of similarity between firms. Our results highlight the potential of the collective wisdom of investors ― extracted from co-search patterns ― in addressing long-standing benchmarking problems in finance.
Keywords: peer firm, EDGAR search traffic, revealed preference, co-search, industry classification
JEL Classification: D83, G0, M2
Suggested Citation: Suggested Citation
Lee, Charles M.C. and Ma, Paul and Wang, Charles C. Y., Search Based Peer Firms: Aggregating Investor Perceptions through Internet Co-Searches (July 2, 2014). Journal of Financial Economics (JFE), Forthcoming; Rock Center for Corporate Governance at Stanford University Working Paper No. 128; Harvard Business School Accounting & Management Unit Working Paper No. 13-048. Available at SSRN: https://ssrn.com/abstract=2171497 or http://dx.doi.org/10.2139/ssrn.2171497