ABSTRACT 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.

中文翻译：

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 分类预测


摘要我们采用一种新颖的机器学习技术，根据修正对公开可用信号的权衡方式，将分析师的预测修正分为五种类型。我们将这些预测类型标记为定量预测、杂项预测、反向预测、牧民预测和独立预测。我们的测试表明，共识中预测类型的多样性与共识分散度的增加和共识准确性的提高有关。此外，共识多样性与企业信息环境的改善相关，这体现在盈利公告信息不对称和波动性的减少、盈利反应系数的提高以及价格形成速度的加快。我们的研究揭示了分析师如何修改他们的预测，并记录与不同分析师预测方法相关的资本市场收益。