Early Movers Advantage? Evidence from Short Selling during After‐Hours on Earnings Announcement Days
Bibliographic record
Abstract
Abstract We examine short sellers’ after‐hours trading (AHT) following quarterly earnings announcements released outside of the normal trading hours. Our innovation is to use the actual short trades immediately after the announcements. We find that on these earnings announcement days, there is significant shorting activity in AHT relative to shorting activity both during AHT on nonannouncements days and during regular trading sessions around announcements. Short sellers who trade after‐hours on announcement days earn an excess return of 0.82% and 1.40% during before‐market‐open (BMO) and after‐market‐close (AMC)sessions, respectively. The magnitude of these returns increases to 1.48 (3.92%) for BMO (AMC) earnings announcements with negative surprise. We find that the reactive short selling during AHT has information in predicting future returns. Short sellers’ trades have no predictive power if they wait for the market to open to trade during regular hours. In addition, we find that the weighted price contribution during AHT increases with an increase in after‐hours short selling. Overall, our results suggest that short sellers in AHT are informed. Our findings remain robust using alternative holding periods and after controlling for macroeconomic news announcements during BMO sessions.
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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 | 0.001 | 0.011 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".