Search-based peer firms: Aggregating investor perceptions through internet co-searches

Charles M.C. Lee, Paul Ma, Charles C.Y. Wang

Research output: Contribution to journalArticlepeer-review

116 Scopus citations


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 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.

Original languageEnglish (US)
Pages (from-to)410-431
Number of pages22
JournalJournal of Financial Economics
Issue number2
StatePublished - May 1 2015

Bibliographical note

Publisher Copyright:
© 2015 Elsevier B.V.


  • Co-search
  • EDGAR search traffic
  • Industry classification
  • Peer firm
  • Revealed preference


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