Can quote competition reduce preferenced trading? A reexamination of the SEC’s 1997 order handling rules
Bibliographic record
Abstract
Abstract In 1997, the SEC implemented the new order handling rules (OHRs) on the NASDAQ. We observe that some uncompetitive positions gained market share without improving quote competitiveness after the implementation of the OHRs. Also observed is a significant decline in the sensitivity of trading volume to quote competitiveness, indicating lower incentive for NASDAQ dealers to engage in quote competition in the post‐OHR regime. We find that positions that gained trading volume without improving quote competitiveness were less competitive and were more closely associated with stocks showing low information asymmetry, which suggests that preferenced trading might be responsible for the decline in the trading volume sensitivity. Examining entries and exits around the periods of adopting OHRs, we observe net entry of uncompetitive positions and net exit of competitive positions, which indicates that preferenced trading crowded out quote competition subsequent to the OHRs. Our findings suggest that forcing intense quote competition alone produced an unwanted effect that preferencing emerged as a more attractive alternative to quote competition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".