Models for Bundle Trading in Financial Markets
Bibliographic record
Abstract
Bundle trading is a new trend in financial markets that allows traders to submit consolidated orders to sell and buy packages of assets. We propose a new formulation for portfolio bundle trading that extends the previous models of the literature through a more detailed representation of portfolios and the formulation of new bidding requirements. We also present post-optimality tie-breaking procedures intended to discriminate equivalent orders on the basis of their submission times. Numerical results evaluate the "bundle"" effect as well as the bidding flexibility and the computational complexity of our formulation." Une nouvelle tendance dans les marchés financiers consiste à transiger des valeurs financières sous forme d'ordres composites d'achat et de vente. Nous proposons une nouvelle formulation basée sur les ordres composites du problème d'allocation de valeurs financières. Notre modèle, comparativement à ceux de la littérature, permet une représentation plus détaillée des portefeuilles financiers et la formulation de nouvelles contraintes transactionnelles. Nous présentons en outre une procédure de discrimination d'ordres équivalents sur la base de leur temps de soumission. Les résultats numériques de notre étude permettent d'évaluer empiriquement l'effet « ordres composites », ainsi que la flexibilité et la complexité numérique de notre formulation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".