Investor Overreaction to Earnings Surprises and Post‐Earnings‐Announcement Reversals
Bibliographic record
Abstract
ABSTRACT Prior literature suggests that the market underreacts to the positive correlation in a typical firm's seasonal earnings changes, which leads to a post‐earnings‐announcement drift (PEAD) in prices. We examine the market reaction for a distinct set of firms whose seasonal earnings changes are uncorrelated and show that the market incorrectly assumes that the earnings changes of these firms are positively correlated. We also document that positive (negative) seasonal earnings changes in the current quarter are associated with negative (positive) abnormal returns in the next quarter. Thus, we observe a reversal of abnormal returns, consistent with a systematic overreaction to earnings, rather than the previously documented PEAD. Additional analysis indicates that financial analysts similarly overestimate the autocorrelation of these firms, although to a lesser extent. We also find that the magnitude of overestimation and the subsequent price reversal are inversely related to the richness of the information environment. Our results challenge the notion that investors recognize but consistently underestimate earnings correlation and provide a new perspective on the inability of prices to fully reflect the implications of current earnings for future earnings. That is, we show that investors predictably overestimate correlation when it is lacking, but underestimate it when it is present.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".