Crowdsourcing Peer Firms: Evidence from EDGAR Search Traffic
Charles M.C. Lee
Stanford University - Graduate School of Business
University of Minnesota - Carlson School of Management
Charles C. Y. Wang
Harvard Business School
September 4, 2013
Rock Center for Corporate Governance at Stanford University Working Paper No. 128
Harvard Business School Accounting & Management Unit Working Paper No. 13-048
Using Internet traffic patterns from the Securities and Exchange Commission Electronic Data-Gathering, Analysis, and Retrieval (EDGAR) website, we show that firms appearing in chronologically adjacent searches by the same individual are fundamentally similar on multiple dimensions. In fact, traffic-based peer firms identified by our algorithm significantly outperform peer firms based on six-digit Global Industry Classification Standard (GICS) groupings in explaining cross-sectional variations in base firms’ stock returns, valuation multiples, forecasted and realized growth rates, research and development expenditures, and various other key financial ratios. Our results highlight the usefulness of EDGAR data, as well as the latent intelligence in search traffic patterns.
Number of Pages in PDF File: 49
Keywords: peer firm, EDGAR search traffic, revealed preference, crowdsourcing
JEL Classification: G0, M2working papers series
Date posted: November 6, 2012 ; Last revised: October 25, 2013
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