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Harnessing the Wisdom of Crowds

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We examine the impact of herding on the accuracy of consensus earnings forecasts from a crowd-based forecast platform (Estimize.com). By tracking user viewing activities, we monitor the amount of information a user views before she makes an earnings forecast. We find that the more public information a user views, the less weight she will put on her private information. While this improves the accuracy of each individual forecast, it reduces the accuracy of the consensus forecast, since useful private information is prevented from entering the consensus. Predictable errors made by “influential users” early on persist in the consensus forecast and result in return predictability at earnings announcements. To address endogeneity concerns related to information acquisition choices, we collaborate with Estimize.com to run experiments where we restrict the information set for randomly selected stocks and users. The experiments confirm that “independent” forecasts lead to a more accurate consensus and convince Estimize.com to switch to a “blind” platform from November 2015. Overall, our findings suggest that the wisdom of crowds can be better harnessed by encouraging independent voices from the participants.

This talk is part of the Cambridge Finance Workshop Series series.

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