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  5. Experience Effects and Memory
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Experience Effects and Memory

Paper Session

Sunday, Jan. 5, 2025 10:15 AM - 12:15 PM (PST)

San Francisco Marriott Marquis, Yerba Buena Salon 5 & 6
Hosted By: American Finance Association
  • Alexandra Wellsjo, University of California-San Diego

Information Partitioning, Learning, and Beliefs

Pascal Kieren
,
Heidelberg University
Lukas Mertes
,
University of Mannheim
Martin Weber
,
University of Mannheim

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.

Memory and Beliefs in Financial Markets: A Machine Learning Approach

Zhongtian Chen
,
University of Pennsylvania
Jiyuan Huang
,
University of Zurich

Abstract

We develop a machine learning (ML) approach to establish new insights into how memory affects financial market participants’ belief formation processes in the field. Using analyst forecasts as proxies for market beliefs, we extract analysts' mental contexts and recalls that shape forecasts by training an ML memory model. First, we find that long-term memories are salient in analysts’ recalls. However, compared to an ML benchmark trained to fit realized earnings, analysts pay more attention to distant episodes in regular times but less during crisis times, leading to recall distortions and therefore forecast errors. Second, we decompose analysts' mental contexts and show that they are mainly shaped by past earnings and forecasting decisions instead of current firm fundamentals as indicated by the ML benchmark. This difference in contexts further explains the recall distortion. Third, our comprehensive memory model reveals the significance of specific memory features and channels in analysts' belief formation, including the temporal contiguity effect and selective forgetting.

Risk-Taking Adaptation to Macroeconomic Experiences

Remy Levin
,
University of Connecticut
Daniela Vidart
,
University of Connecticut

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.

Discussant(s)
Michael Thaler
,
University College London
Zhengyang Jiang
,
Northwestern University
Michael Weber
,
University of Chicago
JEL Classifications
  • G0 - General