Répartition de l’actif d’un portefeuille d’actions internationales et exposition au risque de change
Bibliographic record
Abstract
Cet article présente un cadre d’analyse général du problème double de la décision de répartition de l’actif d’un portefeuille d’actions internationales à travers les marchés boursiers et de la décision d’exposition au risque de change selon que ces décisions sont déterminées de façon passive ou font l’objet d’une optimisation. Quatre approches possibles sont examinées en se basant sur les données historiques des indices boursiers Morgan Stanley Capital International du G-7 de juillet 1976 à juin 2001. La performance relative de chacune des approches est comparée a posteriori. Dans le cas des stratégies d’optimisation, l’accent est mis sur le gain marginal obtenu par le relâchement des contraintes pratiques relatives aux marchés boursiers (importance de l’écart par rapport à la capitalisation relative de l’indice boursier) et/ou aux devises (couverture du risque de change, couverture croisée, exposition au risque de change).
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".