Classifying Forecasts

• 成果类型: Article
• 署名作者: Drake, Michael S.; Moon Jr, James R.; Warren, James D.
• 署名单位: Brigham Young University; University System of Georgia; Georgia Institute of Technology; Texas A&M University System; Texas A&M University College Station; Mays Business School
• 刊物名称: ACCOUNTING REVIEW
• ISSN/ISSBN: 0001-4826
• DOI: 10.2308/TAR-2023-0117
• 发表日期: 2023
• 页码: 129-156
• 关键词: ANALYSTS EARNINGS FORECASTS; INFORMATION; dispersion; performance; investors; accuracy; IMPACT; volume; press; BIAS

摘要

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.