Should investors prefer Canadian hedge funds or stocks?
Bibliographic record
Abstract
Brulhart and Klein (2006) found the true magnitude of extreme returns from hedge fund indices is less than what has been popularly believed and investors should not be afraid of investing in hedge funds.This paper updates Brulhart and Klein (2006) by comparing the magnitude of extreme returns from Tremont, HFRI hedge fund indices with stock indices.It also compares the magnitude of extreme returns from Canadian hedge fund indices with stock indices.We found that the results from Brulhart and Klein (2006) still hold even for the updated US data.However, the results do not hold for the Canadian hedge fund indices.The magnitude of extreme returns from Canadian hedge fund indices is lower than the magnitude of extreme returns from TSX composite, Nasdaq, Tremont and HFRI hedge fund indices, but it is higher than the S&P 500.We believe that is because the composition of the Canadian hedge fund industry is different from the US hedge fund industry.Equity longlshort is the most popular hedge fund strategy in Canada, so the Canadian hedge fund industry overall is more similar to the US equity longlshort strategy.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".