Private Intermediary Innovation and Market Liquidity: Evidence from the Pink Sheets<sup>®</sup> Market
Bibliographic record
Abstract
Abstract In 2007, Pink Sheets LLC assigned each Pink Sheets ® company to a disclosure tier and on its website affixed a colorful graphic to its stock symbol signifying the company's public disclosure level. This unique innovation allows us to investigate the impact of increased salience of disclosure practices on liquidity. Using a difference‐in‐difference design, we find evidence that firms classified into the Current Information category experienced an increase in liquidity while firms classified into the No Information category experienced a decrease in liquidity, both relative to other unclassified over‐the‐counter firms. This suggests that increases in the salience of disclosure practices via assignment to disclosure tiers affect investors’ attention, leading to changes in trading behavior that ultimately translate into liquidity changes. We also provide evidence that some investors anticipated the resulting liquidity changes because stock returns around a key event date leading up to the release of the disclosure tiers are positively associated with subsequent liquidity changes.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".