The Reliability of Crowdsourced Earnings Forecasts

54 Pages Posted: 14 Sep 2017 Last revised: 1 Oct 2018

See all articles by Lawrence D. Brown

Lawrence D. Brown

Temple University - Department of Accounting

Joshua Khavis

Temple University - Department of Accounting

Date Written: September 23, 2018

Abstract

A growing number of studies use crowdsourced data to draw inferences regarding information relevance. To bolster research using crowdsourced data and to allow researchers to draw stronger inferences regarding information relevance, we examine the reliability of online biographies using earnings forecasts provided by Estimize contributors. We examine if: (1) biographical information provided by Estimize contributors are reliable; (2) forecast quality is conditional on whether contributors provide their biographical information and names; and (3) contributors who provide their biographical information but withhold their identities make forecasts with different characteristics than those who provide their biographical information and identities. We find Estimize buy siders behave similarly to buy siders documented in prior studies, and Estimize sell siders (especially brokers) are similar to sell siders documented in prior studies. We show that, relative to other Estimize contributors, brokers’ forecasts are more akin to IBES in that they are: made closer in time to IBES forecasts, more likely to be within one penny of IBES forecasts, and as biased as IBES forecasts. We find that contributors who reveal their biographical information are more active on the Estimize platform and issue higher quality forecasts. Finally, we document that known brokers are more pessimistic than anonymous brokers.

Keywords: reliability, analyst forecasts, bias, accuracy, crowdsourcing, anonymity

JEL Classification: M41, G02

Suggested Citation

Brown, Lawrence D. and Khavis, Joshua, The Reliability of Crowdsourced Earnings Forecasts (September 23, 2018). Fox School of Business Research Paper No. 18-001. Available at SSRN: https://ssrn.com/abstract=3034323 or http://dx.doi.org/10.2139/ssrn.3034323

Lawrence D. Brown

Temple University - Department of Accounting ( email )

Philadelphia, PA 19122
United States

Joshua Khavis (Contact Author)

Temple University - Department of Accounting ( email )

Philadelphia, PA 19122
United States

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