Is There a “Torpedo Effect” in Earnings Announcement Returns? The Role of Short-Sales Constraints and Investor Disagreement
Bibliographic record
Abstract
We predict and find that short-selling constraints combined with investor disagreement cause prices to respond more strongly to bad earnings news than to good earnings news, an asymmetry characterized by Skinner and Sloan as the “torpedo effect.” However, in the absence of short-sales constraints, the price reaction to good and bad news is entirely symmetric, regardless of the level of investor disagreement. Our findings contribute to the ongoing debate about the existence and causes of the torpedo effect. In particular, we extend Skinner and Sloan’s explanation of this effect by showing that short-selling constraints are essential for such an effect to occur; in their absence, there is no torpedo effect, even if investors are overoptimistic. Moreover, this asymmetric effect is not intrinsic to growth stocks; even value stocks are torpedoed in the presence of short-selling constraints and disagreement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".