Bibliographic record
Abstract
As Transaction Cost Analysis (TCA) has matured, the issue of picking the correct benchmark to measure trading performance has been raised by many Head Traders. A desire to simplify the process by determining a single benchmark has been expressed. While appealing, the cost of simplicity of TCA analysis using a single benchmark is that not all of the details of the trading performance may be captured. While it may be possible to define a single benchmark based on a specific implementation strategy, there are many different types of trading strategies and so a benchmark that may be suitable for measuring the performance of one trading strategy may not be suitable for a different trading strategy that has different objectives. The author suggests that the optimal TCA benchmark should be tied to the implementation instructions and any order constraints, and further that for a given trading strategy, the use of multiple benchmarks may provide for a richer analysis of trading performance. There is a trade-off between the simplicity of using a single TCA benchmark and the richness of the TCA analysis that comes from using multiple TCA benchmarks. TOPICS:Performance measurement, quantitative methods, statistical methods
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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 | 0.036 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".