Abstract: We experimentally study how information partitioning affects learning and beliefs. Holding the informational content constant, we show that observing small pieces of information at higher frequency (narrow brackets) causes beliefs to become overly sensitive to recent signals compared to observing larger pieces of information at lower frequency (broad brackets). As a result, partitioning information in narrow or broad brackets causally affects judgements. Observing information in narrow brackets leads to less accurate beliefs and to worse recall than observing information in broad brackets. As mechanism, we provide direct evidence that partitioning information into narrower brackets shifts attention from the macro-level to the micro-level, which leads people to overweight recent signals when forming beliefs.
Discussant: Michael Thaler, University College London
Abstract: 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 past 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.
Discussant: Zhengyang Jiang, Northwestern University
Abstract: We study how lifetime experiences of macroeconomic volatility shape individual
risk attitudes. We build a Bayesian model where risk aversion endogenously adapts
to agents’ beliefs about an exogenous income process. We combine panel data from
Indonesia and Mexico containing elicited measures of risk aversion with state-level real
GDP growth time series capturing individuals’ lifetime macroeconomic experiences. In
line with the model’s predictions, we find that measured risk aversion increases with
macroeconomic volatility, and that this is a first-order driver of risk attitudes. These
results are robust to many alternate specifications and controls and extend to risk-taking
behavior in other domains.
