Working Paper | HBS Working Paper Series | 2012

Identifying Peer Firms: Evidence from EDGAR Search Traffic

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

Abstract

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.

Keywords: peer firm; EDGAR search traffic; revealed preference;

Citation:

Lee, Charles M.C., Paul Ma, and Charles C.Y. Wang. "Identifying Peer Firms: Evidence from EDGAR Search Traffic." Harvard Business School Working Paper, No. 13–048, November 2012.