Do actively managed funds perform better than index funds? A test in the Canadian market
Bibliographic record
Abstract
Actuality of the study: Mutual funds are a favourite investment product among many investors. They provide a simple means of diversification, especially for those with smaller amounts of capital, and the popularity of mutual funds has increased with the success of the marketing efforts behind them. Purpose: This study evaluates the performance of actively managed and index mutual funds within the Canadian equities market. Findings: As index investing has increased in popularity, and other markets have become more connected and open, there is a need for research on equity mutual funds in countries outside the US. Originality / Value: The majority of previous research on index funds and actively managed mutual funds is focused on the US market and related indexes such as the S&P 500. Practical implications: This study suggests that, on average, active funds in Canada fail to beat their benchmarks net (but not gross) of the common fee or management expense ratio. Surprisingly, this research finds no positive relationship between higher fees and better gross performance. Actively managed funds also have poorer performance over the long term. This study finds that investors would be better off purchasing low cost index funds as they provide a more secure return. Future research: This study endorses research on other markets with inclusion of additional variables in order to explain gross performance and secure returns.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".