The Post‐earnings Announcement Drift: A Pre‐earnings Announcement Effect? A Multi‐period Analysis
Bibliographic record
Abstract
For many years, the post‐earnings announcement drift (PEAD) has been accepted as an anomaly to the efficient markets hypothesis. This drift subsequent to earnings announcements has been ascribed to the incomplete incorporation by the market of the information in these earnings announcements. Interestingly, over the past five decades of extensive research, no rational economic explanation of the PEAD has been found. In addition, there has been no specific consideration of the effect of new economic information subsequent to the earnings announcements. Our multi‐year examination of the cumulative abnormal return (CAR) incorporates the effect of economic information subsequent to the earnings announcement of traditional PEAD studies. Our analysis shows that a drift of CAR versus time can arise without recourse to invoking market inefficiency. Our results are consistent for three samples covering the period 1974–2016. We do not assert that we prove that there is no component of the traditional PEAD due to market inefficiency. Rather, our results show that studies to determine whether there is a market inefficiency component of the PEAD should use a multi‐period approach in order to account for the effect of economic information subsequent to the earnings announcements and thereby focus more precisely upon the cause of the PEAD reported in previous research studies.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".