The Value of Crowdsourced Earnings Forecasts

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.

Keywords: Analyst, Forecast, Earnings Response Coefficients, Crowdsourcing

JEL Classification: G28, G29, M41, M43