Trade transparency and trading volume: the possible impact of the financial instruments markets directive on the trading volume of EU equity markets
Bibliographic record
Abstract
The EC Directive on financial instruments markets 2004 (MiFID) has introduced a number of order and trade publication obligations imposed on organised exchanges, alternative trading systems (ATS), and the class of broker dealers that execute transactions in shares internally. This article investigates the impact of MiFID's trade transparency rules on the trading volume of EU equity markets in a forward-looking mode. We use data extracted from the closest possible precedent and examine trading volume levels before and after trading in FTSE100 stocks on the London Stock Exchange (LSE) shifted from the quote-driven Stock Exchange Automatic Quotation System (SEAQ) to the order-driven securities electronic trading service (SETS). This change resulted in significantly increased transparency standards. Trading volume is measured on the basis of three criteria: volume-based turnover, value-based turnover and turnover ratio. No evidence is found indicating that higher transparency standards lead per se to higher levels of trading volume. Therefore, the impact of MiFID's transparency rules on trading volume in EU equity markets should become a matter of further study following their implementation.
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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 | 0.004 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".