Does the Public Availability of Market Participants' Trading Data Affect Firm Disclosure? Evidence from Short Sellers
Bibliographic record
Abstract
Market transparency affects how much information investors can glean by observing market data, while firm transparency determines the extent to which outsiders can gain access to firms’ inside information. Each type of transparency has been independently studied in the prior literature. The relation between the two, however, is not well understood. By making use of a natural experiment in which the transparency of short selling in the market place improved exogenously, this paper provides initial evidence on the consequences of improving market transparency on a firm’s transparency, and empirically tests recently developed dynamic disclosure theory. Using a unique difference-in-differences design, I illustrate that making short-interest data publicly available increases firms’ voluntary disclosure. This outcome suggests that revealing sophisticated investors’ trading positions has a disciplining effect on firm disclosure. Additional tests reveal that this disciplining effect exists for disclosures both before and after the short-interest release. Finally, to further understand firms’ rationale to disclose more, in cross-sectional analyses I show that the positive effect of making short interest public on firm disclosure increases with short-interest level and litigation risk, but decreases with the real-option value of withholding news. This paper highlights the importance of market transparency in improving firms’ information environment.
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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 | 0.008 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".