Crowdsourced Forecasts and the Market Reaction to Earnings Announcement News

• 成果类型: Article
• 署名作者: Schafhautle, Sandra G.; Veenman, David
• 署名单位: University of Pennsylvania; University of Amsterdam
• 刊物名称: ACCOUNTING REVIEW
• ISSN/ISSBN: 0001-4826
• DOI: 10.2308/TAR-2021-0055
• 发表日期: 2024
• 页码: 421-456
• 关键词: cross-sectional variation; management; ANALYST; Informativeness; incentives; inferences; surprises; wisdom; access; share

摘要

This study examines whether crowdsourced forecasts of earnings and revenues help investors unravel bias in earnings announcement news, which is commonly derived from analyst forecasts. Our results suggest that investors, on average, understand and price the predictive signals reflected in crowdsourced forecasts about the bias in analyst -based earnings and revenue surprises. Using the staggered addition of firms to the Estimize platform, we find that crowdsourced coverage is associated with reductions in the mispricing of forecast bias and declines in earnings announcement premia. We further find some evidence that managers use income -increasing accruals to meet the crowdsourced forecast benchmark and that they respond to crowdsourced coverage through increased downward earnings and revenue guidance. Overall, we conclude that user -generated content on crowdsourced financial information platforms helps investors discount biases in traditional equity research and thereby better process the news in earnings announcements.