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The Value of Crowdsourced Earnings Forecasts
Author(s) -
JAME RUSSELL,
JOHNSTON RICK,
MARKOV STANIMIR,
WOLFE MICHAEL C.
Publication year - 2016
Publication title -
journal of accounting research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.767
H-Index - 141
eISSN - 1475-679X
pISSN - 0021-8456
DOI - 10.1111/1475-679x.12121
Subject(s) - crowdsourcing , earnings , value (mathematics) , sample (material) , task (project management) , investment (military) , business , econometrics , capital market , investment decisions , economics , actuarial science , finance , computer science , machine learning , behavioral economics , management , chemistry , chromatography , politics , world wide web , political science , law
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.