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The Value of Crowdsourced Earnings Forecasts
Authors:RUSSELL JAME  RICK JOHNSTON  STANIMIR MARKOV  MICHAEL C WOLFE
Institution:1. University of Kentucky;2. University of Alabama at Birmingham;3. Southern Methodist University;4. Virginia Tech
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:G28  G29  M41  M43  analyst  forecast  earnings response coefficients  crowdsourcing
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