Les hedge funds ont‐ils leur place dans un portefeuille institutionnel canadien?
Bibliographic record
Abstract
Abstract This article examines the return and risk of hedge funds (HF), and their correlations with traditional asset classes for the 1990–2002 period. Efficient frontiers resulting from optimizations with and without constraints demonstrate that it is worthwhile to include HF in a Canadian institutional investor's portfolio. HF offer a high potential return relative to risk, while weaker correlations with traditional asset classes create a beneficial diversification effect. Non‐directional HF provide protection in bear markets and are more suitable for lower risk portfolios, whereas directional HF are better suited to higher risk portfolios. Caveats are necessary due to the skew‐ness and kurtosis of the return distributions, potential biases in the return series, the lower liquidity, and the complexity of the HF industry. Résumé Cet article examine le rendement, le risque et les correélations des hedge funds (HF) avec les catégories d'actif traditionnelles sur la période 1990–2002. Des optimisations avec et sans contraintes montrent qu'il est avantageux d'inclure les HF dans un portefeuille institutionnel canadien du fait d'un potentiel de rendement élevé par rapport au risque encouru et de faibles corrélations. Les HF non‐directionnels offrent une meilleure protection en marché baissier et sont plus appropriés pour des portefeuilles moins risqués. Les HF directionnels conviennent davantage aux portefeuilles prksentant un risque plus élevé. Des réserves doivent toutefois étre émises en raison des coefficients d'asymétrie et d'aplatissement de la distribution des rendements, des biais potentiels des données, de la faible liquidité, et de la complexité de l'industrie des HF.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".