The Ethical Mutual Fund Performance Debate: New Evidence from Canada
Bibliographic record
Abstract
Although the academic interest in ethical mutual fund performance has developed steadily, the evidence to date is mainly sample-specific. To tackle this critique, new research should extend to unexplored countries. Using this as a motivation, we examine the performance and risk sensitivities of Canadian ethical mutual funds vis-à-vis their conventional peers. In order to overcome the methodological deficiencies most prior papers suffered from, we use performance measurement approaches in the spirit of Carhart (1997, Journal of Finance 52(1): 57–82) and Ferson and Schadt (1996, Journal of Finance 51(2): 425–461). In doing so, we investigate the aggregated performance and investment style of ethical and conventional mutual funds and allow for time variation in the funds’ systematic risk. Our␣Canadian evidence supports the conjecture that any␣performance differential between ethical mutual funds and their conventional peers is statistically insignificant.
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".