Preliminary draft Not for attribution Comments welcome
Bibliographic record
Abstract
We thank the Island ECN for providing us with the data for this study, and are especially grateful for the help of Cameron Smith, Josh Levine, and Rob Newhouse. We also thank Tim McCormick and the NASD’s Economic Research for providing us with odd-lot data, and Lei Yu for her research assistance. All errors are our own responsibility. Page 1 Limit Orders and Volatility in a Hybrid Market: The Island ECN This paper is an empirical analysis of trading activity on the Island ECN, an electronic communications network for US equities, which is organized as an electronic limit order book. The approach is cross-sectional across firms. The goal is to characterize the firmspecific determinants of Island activity, with particular emphasis on the volatility of the firm’s stock. We find that Island’s market share for a given firm is positively related to the overall level of Nasdaq trading in the firm. Across a number of volatility proxies, we find that higher volatility is associated with • a lower proportion of limit orders in the incoming order flow • a higher probability of limit order execution • shorter expected time to execution • lower depth in the book. In addition, we find substantial use of hidden limit orders (for which the submitter has opted to forgo display of the order). Finally, over one quarter of the limit orders submitted to Island are canceled (unexecuted) within two seconds or less. The extensive use of these “fleeting ” orders is at odds with the view that limit order traders (like dealers) are patient providers of liquidity. Page 2 1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.680 | 0.434 |
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".