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Asymmetric Learning from Prices and Post‐Earnings‐Announcement Drift
Author(s) -
Choi Jaewon,
Thompson Linh,
Williams Jared
Publication year - 2019
Publication title -
contemporary accounting research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.769
H-Index - 99
eISSN - 1911-3846
pISSN - 0823-9150
DOI - 10.1111/1911-3846.12477
Subject(s) - earnings , economics , post earnings announcement drift , private information retrieval , arbitrage , accrual , asset (computer security) , financial economics , value (mathematics) , monetary economics , econometrics , earnings response coefficient , finance , statistics , mathematics , computer security , machine learning , computer science
Motivated by research in psychology and experimental economics, we assume that investors update their beliefs about an asset's value upon observing the price, but only when the price clearly reveals that others obtained private information that differs from their own private information. Specifically, we assume that investors learn from the price of an asset in an asymmetric manner—they learn from the price if they observe good (bad) private information and the price is worse (better) than what is justified based on public information alone. We show that asymmetric learning from an asset's price leads to post‐earnings‐announcement drift (PEAD), and that it generates arbitrage opportunities that are less attractive than alternative explanations of PEAD. In addition, our model predicts that PEAD will be concentrated in earnings surprises that are not dominated by accruals, and it also predicts that earnings response coefficients will decline in the magnitude of the earnings surprises.