Analysts' forecasting models and uncertainty about the past

• 成果类型: Article
• 署名作者: Park, Min; Zach, Tzachi
• 署名单位: University of Kansas; University System of Ohio; Ohio State University
• 刊物名称: REVIEW OF ACCOUNTING STUDIES
• ISSN/ISSBN: 1380-6653
• DOI: 10.1007/s11142-025-09898-0
• 发表日期: 2025
• 页码: 2376-2418
• 关键词: proprietary information; CORPORATE DISCLOSURE

摘要

We study the dynamics of information demand and supply in capital markets, focusing on how firms' disclosures align with analysts' information needs. Using a novel dataset from Visible Alpha, we analyze granular data from analysts' forecasting models to understand the breadth of information they seek and how firms meet these demands through mandatory and voluntary disclosures. We document significant variation in the complexity of analysts' models and the extent of firms' disclosures, leading to some items in analysts' models remaining undisclosed. This unmet information demand gives rise to a novel concept we term uncertainty about the past (UP). We investigate its implications for key capital market outcomes, including analyst forecast dispersion, market reactions to earnings announcements, and stock market liquidity. Our results demonstrate that UP plays a significant role in shaping the information environment, challenging the assumption that earnings announcements fully resolve uncertainty about past performance.