The Search for Benchmarks: When Do Crowds Provide Wisdom?

Abstract

We compare the performance of a comprehensive set of alternative peer identification schemes used in economic benchmarking. Our results show the peer firms identified from aggregation of informed agents' revealed choices in Lee, Ma, and Wang (2014) perform best, followed by peers with the highest overlap in analyst coverage, 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. Conversely, peers firms identified by Google and Yahoo Finance, as well as product market competitors gleaned from 10-K disclosures, turned in consistently worse performances. We contextualize these results in a simple model that predicts when information aggregation across heterogeneously informed individuals is likely to lead to improvements in dealing with the problem of economic benchmarking. JEL: D83, G11Keywords: peer firm, benchmarking, EDGAR search traffic, co-search, analyst coverage, industry classification, wisdom of crowds * The authors can be contacted at clee8@stanford.edu, paulma@umn.edu, and charles.cy.wang@hbs.edu. We thank Boris Groysberg, Paul Healy, Ryan Buell, Kai Du, Akash Chattopadhyay, Andrew Jing Liu, Daniel Malter, Tatiana Sandino, Pian Shu, Martin Szydlowski, Akhmed Umyarov, and Aaron Yoon for helpful comments and suggestions. We are very grateful to Scott Bauguess at the Securities and Exchange Commission for assistance with the EDGAR search traffic data. We also thank Kyle Thomas for excellent research assistance. All errors remain our own.

Shared analyst coverage: Unifying momentum spillover effects

“…Consistent with this idea, Lee, Ma, and Wang (2016) document that analysts cover economically similar or related stocks-the fundamentals of analyst co-covered stocks are highly correlated. Furthermore, they show 2 that analyst linkages explain the cross-sectional variation in firm returns and fundamentals better than traditional industry linkages.…”

“…Using a variety of proxies for interfirm linkage or relatedness, various papers have verified such spillovers. 1 This literature documents lead-lag relationships among stocks belonging to the same industry (Moskowitz and Grinblatt 1999), firms that share the same geographic location (Parsons, Sabbatucci, and Titman 2016), firms that are linked along the supply chain Frazzini 2008 andMenzly andOzbas 2010), firms with similar technologies (Lee, Ma, and Wang 2016), and single-and multi-segment firms operating in similar segments (Cohen and Lou 2012).…”

“…An emerging literature documents that shared analyst coverage of firms is associated with greater contemporaneous return correlations (Anton and Polk (2014), Muslu, Rebello, and Xu (2014), Lee, Ma, and Wang (2016)). Lee, Ma, and Wang (2016) also examine various approaches to peer firm identification such as GICS industry classification, Yahoo Finance peers, and common search based peers. They find that analyst co-coverage peers explain the cross-sectional variation in contemporaneous firm returns and fundamentals much better than 9 GICS based peers.…”

Tweet Sentiments and Crowd-Sourced EarningsEstimates as Valuable Sources of Information aroundEarnings Releases

“…Schijven and Hitt [2012] analyzed the source of the wisdom of crowds and concluded that the buy-side crowd's attempt to assess managerial perceptions based on publicly available information leads to a vicarious form of wisdom. In economic benchmarking, Lee, Ma, and Wang [2014] displayed evidence that the wisdom of crowds results in peer identification schemes that systematically outperform standard classification schemes.…”

Common Analysts: Method for Defining Peer Firms

“…The method developed and tested in this paper is used in Kaustia and Rantala (2015) to generate peer groups in a study of corporate peer effects involving stock splits. It has also been independently evaluated in a contemporaneous paper by Lee, Ma, and Wang (2016). They find that combining two recent approaches, one based on chronologically adjacent searches by the same individual performed on SEC's Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system (Lee, Ma, and Wang (2015)) and the analyst-based method, is especially effective.…”