Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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. <b>TOPICS:</b>Performance measurement, quantitative methods, statistical methods
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.
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 | 0.064 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.033 | 0.122 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.006 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it