The Comparative Performance of Load and No‐Load Mutual Funds in Canada
Bibliographic record
Abstract
Abstract This paper investigates, using both single‐factor and multi‐factor models, the absolute performance of Canadian equity funds and the relative performance of load versus no‐load funds. Consistent with a wealth of other studies, I find that the typical fund manager in Canada is unable to surpass his risk‐adjusted benchmark. Moreover, the advantage possessed by load funds, in being able to undertake fewer liquidity‐motivated trades than most no‐load funds, does not translate into their being able to outperform no‐load funds, even when loads are ignored. Résumé Le présent article utilise les modèles de facteur unique et les modèles defacteur multiple pour examiner la performance absolue desfonds d'actions canadiens et la performance relative desfonds avec frais d'acquisition, par opposition aux fonds exempts des frais d'acquisition. Comme les nombreuses études antérieures, notre recherche débouche sur la conclusion qu'au Canada, le gestionnaire de fonds type est incapable de surpasser son point de référence ajusté enfonction du risque. Par ailleurs, l'avantage lié aux fonds avec frais d'acquisition, notamment sa capacité à entreprendre moins de transactions nécessitant des liquidités que les fonds exempts des frais d'acquisition, ne se traduit pas en capacité à donner de meilleurs résultats que les fonds sans frais, même si on ne tient pas compte des frais.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 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".