The Value of Crowdsourced Earnings Forecasts

Russell Jame, Rick Johnston, Stanimir Markov, Michael C. Wolfe

Research output: Contribution to journalArticlepeer-review

102 Scopus citations

Abstract

Crowdsourcing—when a task normally performed by employees is outsourced to a large network of people via an open call—is making inroads into the investment research industry. We shed light on this new phenomenon by examining the value of crowdsourced earnings forecasts. Our sample includes 51,012 forecasts provided by Estimize, an open platform that solicits and reports forecasts from over 3,000 contributors. We find that Estimize forecasts are incrementally useful in forecasting earnings and measuring the market's expectations of earnings. Our results are stronger when the number of Estimize contributors is larger, consistent with the benefits of crowdsourcing increasing with the size of the crowd. Finally, Estimize consensus revisions generate significant two-day size-adjusted returns. The combined evidence suggests that crowdsourced forecasts are a useful supplementary source of information in capital markets.

Original languageEnglish
Pages (from-to)1077-1110
Number of pages34
JournalJournal of Accounting Research
Volume54
Issue number4
DOIs
StatePublished - Sep 1 2016

Bibliographical note

Publisher Copyright:
© 2016 University of Chicago on behalf of the Accounting Research Center

Keywords

  • G28
  • G29
  • M41
  • M43
  • analyst
  • crowdsourcing
  • earnings response coefficients
  • forecast

ASJC Scopus subject areas

  • Accounting
  • Finance
  • Economics and Econometrics

Fingerprint

Dive into the research topics of 'The Value of Crowdsourced Earnings Forecasts'. Together they form a unique fingerprint.

Cite this