Keeping up with the Joneses: A Model Systemic Risk Reporting Regime for the Canadian Hedge Fund Industry
Bibliographic record
Abstract
The purpose of this paper is to suggest a regulatory model by which Canadian securities regulators may monitor the systemic risk contributed to by the Canadian hedge fund industry. The bases for this model are recent regulatory reform initiatives adopted in the U.S. and Europe. There, securities regulators have adopted Form PF and AIFMD, respectively, to monitor the systemic risk contributed to by hedge funds. However, the features of those regimes are not necessarily appropriate for the Canadian industry. The appropriateness of the features of Form PF and AIFMD for the Canadian hedge fund industry is evaluated on two criteria: the average industry fund size, and the cost of regulatory compliance. This paper identifies three features of Form PF and AIFMD that are appropriate for the Canadian hedge fund industry: a minimum size exemption, uniform reporting depth, and extensive data sharing. L’objectif de l’auteur est de proposer un modele de reglementation par lequel les instances reglementaires canadiennes en matiere de valeurs mobilieres pourraient surveiller le risque systemique auquel contribue l’industrie canadienne des fonds de couverture. Le modele propose s’inspire des recentes initiatives de reforme de la reglementation aux Etats-Unis et en Europe. Dans ces pays, les instances reglementaires en matiere de valeurs mobilieres ont adopte, respectivement, le Formulaire PF et l’AIFMD, pour surveiller le risque systemique auquel contribuent les fonds de couverture. Cependant, les caracteristiques de ces regimes ne sont pas necessairement appropriees pour l’industrie canadienne. La pertinence des elements du Formulaire PF et de l’AIFMD pour l’industrie canadienne des fonds de couverture est evaluee en fonction de deux criteres : la taille moyenne des fonds et le cout du respect de la reglementation. L’auteur releve trois caracteristiques du Formulaire PF et de l’AIFMD appropriees pour l’industrie canadienne des fonds de couverture : une mesure d’exemption relative a la taille minimum, l’uniformite pour ce qui est des rapports et le partage de renseignements.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".