Investors’ Quantitative Disclosure: Target Prices by Short Sellers
Bibliographic record
Abstract
While few market participants besides sell-side analysts publicly disclose target prices, we examine a growing trend where activist short sellers provide target prices to support their short theses. We find that short sellers’ target prices are informative in predicting future returns. We argue that their decision to disclose target prices reflects a trade-off between three factors: the speed of price adjustment, the exacerbation of certain risks, and reputation considerations. We find supporting evidence: target price disclosures are positively associated with price adjustment speed, the challenges and retaliation from shareholders and sell-side analysts, and proxies of short sellers’ information precision (which contributes to their reputation). We further argue and find evidence that the salience and quantitative nature of target prices contribute to the accelerated price adjustment by reducing investors’ processing costs. Overall, our study sheds light on the economic tradeoffs arising when investors decide to disclose quantitative information.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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".