Congestion in Commodity Trading Advisors
Bibliographic record
Abstract
Congestion is often used in the operations area to investigate the excessive effect of inputs on outputs. In finance, and more specifically in the derivatives area, leverage is embedded in options and futures contracts. Commodity Trading Advisors (CTAs) use leverage (margin-to-equity ratio) to magnify returns through the use of these futures contracts. However, excessive leverage may hamper performance. This paper aims to show that a related data envelopment analysis (DEA) called the “congestion model” can offer a more precise picture of identifying CTAs suffering from congestion. In other words, if congestion is present then a reduction in input(s) may generate an increase in output. However, the opposite effect can arise. Although traditional DEA does an excellent job at ranking efficient CTAs, congestion on the other hand sizes up which CTAs are using too much (overuse) of each input, thereby reducing their performance/compound return (output). We measure the congestion of the largest (in terms of capital) live 50 CTAs and identify which ones exhibit congestion. The evidence shows that the probability of experiencing congestion increases with the size, minimum purchase requirements, and the incentive fees a CTA operates. In contrast, this probability decreases with the age of the CTA.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".