Disclosure Policy and Market Liquidity: Impact of Depth Quotes and Order Sizes*
Bibliographic record
Abstract
Abstract This paper investigates the relation between disclosure policy and market liquidity. Our tests examine two key aspects of market liquidity, the effective bid‐ask spread and quoted depth, and how they relate to financial analysts' ratings of firms' disclosure policies. We introduce a method of combining order sizes and depth quotes to yield more precise estimates of effective spreads on trades likely constrained by quoted depth. We find that while firms with higher rated disclosures are charged lower effective spreads, they are also quoted lower depth, consistent with the notion that better disclosures reduce information asymmetry but also cause some liquidity suppliers to exit the market. Therefore, a simple examination of spreads and depths yields ambiguous inferences on the relation between disclosure policy and market liquidity. We resolve this ambiguity by estimating depth‐adjusted effective spreads, and find that firms with higher rated disclosures have lower depth‐adjusted effective spreads across all trade sizes. Consequently, our results reveal a robust inverse relation between disclosure ratings and effective trading costs. This implies that a policy of enhanced financial disclosure is related to improved market liquidity.
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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 | 0.003 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".