Representative investors versus best clienteles: Performance evaluation disagreement in mutual funds
Bibliographic record
Abstract
This paper develops a diagnostic tool for candidate performance measures that accounts for investor disagreement in mutual funds. We compare the evaluation for best clienteles, specified by an upper admissible performance bound, to the one for representative investors implicit in eleven models. The results show that linear factor models misrepresent best clientele alphas, with a disagreement that relates to fund characteristics. Consumption-based alphas are generally inadmissible. The manipulation-proof performance measure generates alphas that are sensitive to its risk aversion parameter and lack statistical precision. However, a reasonable parameter gives admissible values that reflect the alphas for the most favorable clienteles. • Paper develops and implements a diagnostic tool for candidate performance models that accounts for investor disagreement. • Tool is based on a comparison between commonly used performance measures and best clientele alphas. • Empirical results shows that most models misrepresent the value of mutual funds for favorable clienteles. • Disagreement is higher for funds with higher expenses, higher turnover, more longevity, older managers and smaller size
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".