Resolving Conflicts in Crowds: An Earnings Forecast Application

Recently, investors can obtain earnings forecast information through traditional venues, such as Wall Street and the Institutional Brokers’ Estimate System (IBES), as well as modern social media platforms like Estimize, which generates consensus estimates based on forecasts from individuals with diverse backgrounds. As a result, this will inevitably lead to conflicts in the earnings forecast. This paper presents a novel and effective optimization-based approach to resolving such conflicts in earnings forecast data and generating an accurate and robust consensus estimation. Consistent with the wisdom-of-crowds effect, the new earnings forecast consensus is more accurate than the Wall Street consensus (67.5% of estimations with error less than Wall Street) and IBES consensus (67.4% of estimations with error less than IBES) of the time. Moreover, the new earnings forecast consensus can provide incrementally helpful information in forecasting earnings, and the incremental information is further priced in the market after the earnings announcement. History: Olivia Sheng served as the senior editor for this article. Supplemental Material: The online appendix, code, and data files are available at https://doi.org/10.1287/ijds.2023.0015.cd .

• Publication year

  2025

• Venue

  INFORMS Journal on Data Science

• Publication date

  2025-11-07

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1287/ijds.2023.0015
• 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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