Rutgers Business School Newark and New Brunswick
Bibliographic record
Abstract
I am writing this letter in response to the Commission's request for comments on the above concept release. In particular I would like to address the issue of order routing to nondisplayed liquidity (dark pools and internalization.) These trades are then reported through a FINRA Trade Reporting Facility (TRF.) I will refer to these trades as off-exchange reporting. I have just completed a study of the impact of off-exchange reporting on market quality. I am attaching it for your reference. In the paper I provide a survey of existing theoretical and empirical work on the topic. I also conduct statistical tests to empirically examine the impact of off-exchange reporting on U.S. markets during the month of October 2009. The conclusion of both portions of the attached study is that off-exchange reporting hurts market quality. First let me address the existing literature on off-exchange reporting. That literature has focused on internalization and the more general issue of preferencing. In reviewing the literature, I found no paper that concludes that preferencing/internalization (hereafter P/I) improves market quality. I found three empirical papers that empirically find that it is benign. One of the papers only examined the most liquid securities in the market so its findings cannot be applied to the market as a whole. The other two conclude that P/I did not have an impact on
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.662 | 0.485 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".