Analysts’ forecasting models and uncertainty about the past
研究了企业信息披露如何满足分析师的信息需求，发现分析师模型中部分项目未被披露，由此提出“关于过去的不确定性”概念，并探讨其对市场的影响。
Abstract 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.