Short Squeezes After Short‐Selling Attacks
Bibliographic record
Abstract
ABSTRACT We estimate the prevalence and drivers of short squeezes after short‐selling attacks. Positive returns after attacks have a disproportionate tendency to fully reverse and are accompanied by heightened short covering, consistent with the presence of short squeezes. We assess and find no support for non‐squeeze drivers of these positive return reversals and show they are more likely to be accompanied by squeeze‐related news articles, increased stock volatility, and disruptions in the stock lending market. Using positive return reversals as a proxy for short squeezes, we estimate that 15% of short attacks experience squeezes, and squeeze risk increases with short sellers’ visibility but decreases with the credibility of their evidence. Additionally, squeezes appear to be precipitated by actions of firms and investors, including insider purchases, share recalls, retail investor trading, and firm disclosures. Our findings quantify a material risk and check on activist short selling and are especially timely given recent proposed short‐selling restrictions.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".