Limit Orders, Trading Activity, and Transactions Costs in Equity Futures in an Electronic Trading Environment
Bibliographic record
Abstract
The behaviour of limit order quotes and trading activity are studied using a unique and rich database that includes the identity of market participants from a fully automated derivatives market. The analysis is performed using transactions records for three aggregated trader types and three trade identifiers, with trades stamped in milliseconds for the SXF, the equity futures contract of the Montreal Exchange. The identifiers distinguish trades between principals; agency based trades, as well as transactions that are conducted for risk management as opposed to speculative purposes. Agency related trades are shown to represent the largest amount of trading activity relative to other account types. Over 90% of trades in this electronic market are limit orders. The limit order book, especially the depth 1 order, has a dominant role in providing liquidity and in explaining market participants’ trading behaviour. Participants in the SXF reference their trades to the best limit order depth. Hence, investors with large positions or investors who want to build a large position have to strategically split large orders to close/build their position, according to the depth of the best limit order, to ameliorate price impact and information leakage effects. In addition, the results show that traditionally measured spreads have no relationship with trading costs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".