Search Based Peer Firms: Aggregating Investor Perceptions through Internet Co-Searches
Charles M.C. Lee
Stanford University - Graduate School of Business
University of Minnesota - Carlson School of Management
Charles C. Y. Wang
Harvard Business School
March 26, 2014
Rock Center for Corporate Governance at Stanford University Working Paper No. 128
Harvard Business School Accounting & Management Unit Working Paper No. 13-048
Applying a “co-search" algorithm to Internet traffic at the SEC's EDGAR website, we develop a novel method for identifying economically-related peer firms. 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 is more dynamic, pliable, and concentrated. Our results highlight the potential of the collective wisdom of investors ― extracted from co-search patterns ― in addressing long-standing benchmarking problems in finance.
Number of Pages in PDF File: 56
Keywords: peer firm, EDGAR search traffic, revealed preference, co-search, information acquisition
JEL Classification: D83, G0, M2working papers series
Date posted: November 6, 2012 ; Last revised: March 27, 2014
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