I study the formation of analysts' subjective beliefs about firms' earnings using analysts' own written text from over 1.1 million equity research reports. Text in analyst reports strongly predicts analysts' forecast revisions and forecast errors. Using a Large Language Model, I distinguish between factual and subjective content and distill it into interpretable topics on firm fundamentals. I document three sets of novel findings regarding analysts' subjective beliefs. (1) I show that analysts' attention allocation varies significantly over business cycles, firms, and forecast horizons. Analysts pay more attention to profitability information during booms and pay more attention to financial conditions and macroeconomics during recessions. These patterns align with a model of rational inattention. (2) I introduce a novel text-instrumented Coibion-Gorodnichenko regression to study analysts' misreaction to specific information. I find a pervasive underreaction across topics in analysts' short-term earnings forecasts, while their overreaction in long-term forecasts is mainly significant for business operations, corporate management, and macroeconomic information. This pattern is consistent with a "story-statistics gap'' in associative memory being an important driver of overreaction to qualitative, story-like information. (3) I find that both asymmetric information and differences of opinion contribute to disagreement in earnings forecasts. Together, these results offer new insights into the formation of subjective beliefs about firms' earnings.

We construct a new measure—the Return-Earnings Gap (REG)—that captures the market’s relative (mis)reaction to earnings surprises. About 50% of the earnings-day return associated with REG reverses subsequently, with the reversal being strikingly slow, taking about three years. REG feeds back into and distorts market participants’ belief formation, predicting subsequent analyst forecast errors, corporate actions associated with mispricing, and the divergence of anomaly returns. A simple structural model of market participants’ expectation formation corroborates these findings. Our results show that earnings-day returns contain a substantial non-fundamental component with long-term effects, contrasting with the predominant fundamental view of earnings days.

This paper explores the role of memory in shaping belief formation of financial market participants. We estimate a structural machine learning model of memory-based belief formation applied to consensus earnings forecasts of sell-side stock analysts. The estimated model reveals significant recall distortions compared to a benchmark model trained to fit realized earnings revisions. Specifically, analysts over-recall distant historical episodes most of the time, when recent events are more useful for forming forecasts than those in the distant past, but under-recall them during crisis times, when history helps to interpret unusual events. We document two potential driving forces behind these distortions. First, analyst memory overweights the importance of past earnings and forecasts. Second, analysts are more likely to selectively forget past positive events. Our model of analyst recalls strongly predicts their earnings forecast revisions and errors, as well as stock returns, which suggests that distorted recalls might contribute to mispricing of assets in financial markets.