MiFID <scp>II</scp> and the unbundling of analyst research from trading execution
Bibliographic record
Abstract
Abstract The revised Markets in Financial Instruments Directive (MiFID II) requires the unbundling of research payments from trading execution, fundamentally changing the way in which investors typically pay for analyst research in Europe. We examine the effectiveness of the regulation in changing the link between analyst research and trading, the research‐trading link, and the analyst response to this potential change in incentives. Using a difference‐in‐differences research design, we find that forecast frequency, optimism, and accuracy are less associated with the brokerage trading share after MiFID II, suggesting that MiFID II weakened the link between the brokerage share of trading and analyst research. Following MiFID II, analysts in Europe are less likely than analysts in the United States to continue high forecast frequency, optimism, and accuracy for stocks with high share importance for the analyst's brokerage house. We find similar results throughout for buy/sell recommendations. Overall, our evidence suggests that MiFID II is at least partially successful in unbundling research from execution, and impacts both the trading effects and the production of analyst research.
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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 | 0.025 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".