Classifying Forecasts

We employ a novel machine learning technique to classify analysts’ forecast revisions into five types based on how the revision weighs publicly available signals. We label these forecast types as quant, sundry, contrarian, herder, and independent forecasts. Our tests reveal that a greater diversity of forecast types within the consensus is associated with increased consensus dispersion and improved consensus accuracy. Additionally, consensus diversity is associated with an improved information environment for firms, as reflected in reduced earnings announcement information asymmetry and volatility, higher earnings response coefficients, and faster price formation. Our study sheds light on how analysts revise their forecasts and documents capital market benefits associated with different analyst forecasting approaches.

• Publication year

  2024

• Venue

  Accounting Review

• Publication date

  2024-06-01

• Fields of study

  Not labeled

• Identifiers
  DOI 10.2308/tar-2023-0117
• 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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