Alternative Trading Platforms in the United States: Incentives for Innovation in the US Stock Market
Bibliographic record
Abstract
Is something wrong with the structure of our stock market? Recent scholarship faults the equity market for its lack of innovation. In particular, commentators stringently criticize the continuous nature of modern trading for baking in a wasteful arms race for speed among high-frequency traders. Under their current structure, stock exchanges process incoming instructions to trade in the order they arrive and as quickly as possible, which means in millionths of a second or less. The result is a race for technological speed because market participants can earn profits from being the first to trade on new information, even when that information is widely and simultaneously available. This race would be eliminated if continuous trading was replaced with discrete, periodic auctions, say once per thousandth of a second. The problem, it is argued, is that the market will not fix itself because the nation's stock exchanges lack the incentives to appropriately innovate, principally because they earn so much revenue from the sale of products dependent on speed.\nThis picture, however, is incomplete. There are other trading venues in the modern equity market than its exchanges, including the neglected cousins of the stock market, alternative trading systems ('ATSs'). While over 200 billion shares were traded on U.S. ATSs last year-more volume than the entire Canadian stock market-popular and academic discussion of equity market structure overwhelmingly emphasizes the national stock exchanges. Like stock exchanges, ATSs are electronic markets in which participants can trade the stock of public companies, but when attention turns to them, a single fact about ATSs dominates discussion-they are 'dark' (hence their popular moniker 'dark pools'). 'Dark' simply means that 'quotes' posted by traders-orders expressing their willingness to trade at a specific price-on these trading venues are not included in the public quotation feed that distributes quote data to all market participants. While this fact is important, emphasizing it has confined analysis of ATSs to an unduly narrow range of debates.\nI focus on a dimension that is not usually considered in the same breath as ATSs innovation. Innovation is both an important and timely lens for analyzing ATSs. Innovation has historically been a major ambition for equity markets, whether to ensure that market participants rapidly receive information, that they can act on it promptly, or that trading interests interact in a structure that effectively balances the competing goals of market quality. The technological transition from manual to electronic markets is widely credited with generating secular increases in the quality of trading outcomes. The present moment also highlights the importance of innovation in equity market structure. In early 2020, the Securities and Exchange Commission ('SEC') proposed a major rule change that would effectively reinvent major parts of the stock's market structure. The rule would expand what data exchanges must include in public feeds and which actors distribute it. According to the SEC, the changes would benefit 'market participants by increasing the amount of innovation in the consolidation and dissemination of consolidated market data'. Lastly, as noted above, faulting the market for a failure to innovate is a major theme of recent scholarship.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".