Working Paper | HBS Working Paper Series | 2014

Search Based Peer Firms: Aggregating Investor Perceptions Through Internet Co-Searches

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

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. 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. 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; Information Acquisition; Data and Data Sets; Search Technology; Internet; Mathematical Methods; Corporate Finance;

Citation:

Lee, Charles M.C., Paul Ma, and Charles C.Y. Wang. "Search Based Peer Firms: Aggregating Investor Perceptions Through Internet Co-Searches." Harvard Business School Working Paper, No. 13-048, November 2012. (Revised September 2013, March 2014.)