Harnessing the Wisdom of Crowds

We examine the negative information externality associated with herding on a crowd-based earnings forecast platform (Estimize.com). By tracking user viewing activities, we monitor the amount of information a user viewed before she makes an earnings forecast. We find the more public information viewed, 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 persist in the consensus forecast and result in return predictability at the earnings announcements. To address the endogeneity concerning the information acquisition choice, we collaborate with Estimize.com to run experiments where we restrict the information set on randomly selected stocks and users. The experiments confirm that "independent" forecasts lead to more accurate consensus and convince Estimize.com to switch to a "blind" platform from November 2015. Overall, our findings suggest that wisdom of crowds can be better harnessed by encouraging independent voice from the participants.

• Publication year

  2020

• Venue

  Management Sciences

• Publication date

  2020-05-01

• Fields of study

  Computer Science, Economics

• Identifiers
  DOI 10.2139/SSRN.2731884
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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