Proper fund size: a perspective from both investors and fund managers
Bibliographic record
Abstract
This paper proposes the notion of the proper size interval for funds and the market redemption return in fund markets. We establish a model to determine the proper size interval that accounts for the interests of both investors and fund managers as well as market constraints. We then propose a method to analyze fund managers’ abilities and fund sizes compared to the market averages. Using data on Chinese equity and hybrid funds for the sample period from 2009 to 2019, we find that there is a negative log-linear relationship between fund net excess return and fund size. Our model shows that both equity and hybrid funds experience the transition from being over-sized to properly- and under-sized. Under-sized and over-sized funds account for about 60% and 30% of the total sample, respectively, while less than 10% of funds have a proper size. Compared with over- or under-sized funds, funds with a proper size can generate higher returns and higher capital inflow. Our empirical results confirm that funds with higher managerial ability indeed outperform those funds with lower managerial ability, regardless of the category of fund size. Overall, our model provides a new method for fund investors, managers, and regulators to classify and evaluate funds.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".