Competition and Market Structure of National Association of Securities Dealers Automated Quotations
Bibliographic record
Abstract
ABSTRACT In this paper, we study the relation among market structure, trading costs, and competition in National Association of Securities Dealers Automated Quotations (NASDAQ). In particular, we address the following questions: Do NASDAQ dealers exercise market power and extract economic rents in setting bid‐ask spread? How persistent is the market power of dominant dealers? Our estimate of the rent is approximately ¢8.76, or 0.54% of stock price. The half‐life of the persistence of this rent is approximately 20 months for the entire sample, while the half‐life of younger stocks tend to be shorter than those of more mature stocks. Our result supports Schultz: NASDAQ dealers make markets only for stocks where they have competitive advantages in accessing order flow and in information. It might take a while before a market maker poses effective competition to existing dominant market makers. In the meantime, incumbent market makers are able to exercise market power and appear to earn abnormally large profits.
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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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".