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

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

Journal of Financial Economics, 2015, vol. 116, issue 2, 410-431

Abstract: 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.

Keywords: Peer firm; EDGAR search traffic; Revealed preference; Co-search; Industry classification (search for similar items in EconPapers)
JEL-codes: D83 G11 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (61)

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Working Paper: Search Based Peer Firms: Aggregating Investor Perceptions through Internet Co-searches (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jfinec:v:116:y:2015:i:2:p:410-431

DOI: 10.1016/j.jfineco.2015.02.003

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