The Effect of Reporting Opacity on Trading Opacity: New Evidence from American Depositary Receipt Trades in Dark Pools*
Bibliographic record
Abstract
ABSTRACT Trading volume is increasingly shifting to dark venues, and the causes of this move are not well understood. We examine whether a firm's reporting opacity affects its dark pool trading and provide robust evidence that (partly) explains this volume migration. Exploiting the exogenous variation in home‐country reporting opacity in a sample of American Depositary Receipts (ADRs) and using multiple metrics for reporting opacity, we find that greater opacity associates with increased dark pool trading. This relation holds after controlling for firms' information environments, country‐specific governance issues, observable differences between ADRs and other securities that trade in dark pools (matched sample analysis), volume migration to dark pools during earnings announcements, informed trading in lit versus dark venues, ADR levels, IFRS reporting requirements, and the possible endogenous determination of home‐country reporting opacity and dark pool volume. The positive relation is stronger for ADRs held by (quasi‐indexing) institutions with low turnover and diversified holdings and weaker for ADRs favored by (transient) institutions that trade frequently and (dedicated) institutions that hold concentrated portfolios. Bid‐ask spreads are greater for higher opacity ADRs that trade in dark pools, indicating that reporting opacity is associated with the negative effect of dark pool trading on market quality. Our findings help inform the current debate on off‐exchange trading by showing how reporting regimes affect the trading venue choice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".