Harnessing the Wisdom of Crowds

Abstract

When will a large group provide an accurate answer to a question involving quantity estimation? We empirically examine this question on a crowd-based corporate earnings forecast platform (Estimize.com). By tracking user activities, we monitor the amount of public information a user views before making an earnings forecast. We find that the more public information users view, the less weight they will put on their own private information. While this improves the accuracy of individual forecasts, it reduces the accuracy of the group consensus forecast, because useful private information is prevented from entering the consensus. To address endogeneity concerns related to a user’s information acquisition choice, we collaborate with Estimize.com to run experiments that restrict the information available to randomly selected stocks and users. The experiments confirm that “independent” forecasts result in a more accurate consensus. Estimize.com was convinced to switch to a “blind” platform from November 2015 on. The findings suggest that the wisdom of crowds can be better harnessed by encouraging independent voices from among group members, and that more public information disclosure may not always improve group decision making.

Keywords: Wisdom of Crowds, Herding, Naive Learning, Social Learning, Group Decision Making, Earnings Forecast

JEL Classification: G00, G20