Mutual Fund Styles and Clientele-Specific Performance Evaluation
Bibliographic record
Abstract
This paper develops clientele-specific performance measures based on the style preferences of mutual fund investors. Proposing an approach that considers investor disagreement and exploits style classification data, we investigate eight measures to represent investors with favorable preferences for size and value equity styles using a large sample of actively-managed U.S. equity mutual funds from 1998 to 2012. We find that the implied style preferences differ in their rational and behavioral features: value and small-cap fund investors (growth and large-cap fund investors) are more (less) averse to difficult economic conditions, and tend to be pessimists and contrarians (optimists and trend followers). The performance of funds assigned to clientele-specific styles becomes neutral or positive when evaluated with measures that consider their most likely style clienteles. The sign of the value added by the industry is ambiguous and depends on the choice of measures. Hence, performance is more favorable when funds are evaluated with their appropriate style-clientele-specific measure, and can otherwise depend on the measure.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".