Bibliographic record
Abstract
A persistent (but overlooked) feature of the cross-sectional distribution of quarterly earnings announcement returns is that the measured earnings surprise and share price response to that surprise are often in the opposite direction. Extending the study by Kinney, Burgstahler, and Martin, this study provides evidence on the prevalence, determinants, and consequences of contrarian stock returns at the earnings announcement date. Using the most recent Institutional Brokers’ Estimate System (I/B/E/S) consensus earnings per share forecast as the earnings benchmark, the authors find that contrarian returns occur for roughly 40% of the more than 230,000 quarterly earnings announcements that comprise their sample. Contrarian returns are only slightly less prevalent in extreme earnings surprise deciles and are evident each quarter during 1985-2005. The incidence of contrarian returns is statistically related to “noise” in the measured earnings surprise (stale I/B/E/S consensus forecasts, preannouncement stock returns, and the presence of Generally Accepted Accounting Principles [GAAP] exclusions) and “noise” in the share price response to announced earnings (discordant revenue changes, discordant earnings forecast revisions, return volatility, bid-ask spread, and discordant prior quarter earnings surprises). Finally, contrarian stocks exhibit little post-earnings-announcement drift.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".