Evidence on the Performance of Infrastructure Mutual Funds
Bibliographic record
Abstract
This article investigates, empirically, whether infrastructure-focused mutual funds provide superior performance (higher alphas) than comparable equity mutual funds not investing in infrastructure. Using monthly returns on US equity mutual funds, the “best clientele performance measure” developed by Chrétien and Kammoun (2017, 1583), and the generalized method of moments estimation, we find that infrastructure-focused mutual funds have higher alphas (higher best clientele alphas) than comparable funds not investing in infrastructure. Our results support the growing belief that infrastructure-focused equity mutual funds are able to provide superior performance resulting from the financial characteristics of infrastructure. Furthermore, our results show that the investor disagreement about the performance of infrastructure-focused equity mutual funds is not significantly different from that of comparable funds not investing in infrastructure. TOPICS:Wealth management, mutual fund performance Key Findings • The infrastructure-focused equity mutual funds have higher alphas (higher best clientele alphas) than comparable funds not investing in infrastructure. • The investor disagreement about the performance of infrastructure-focused equity mutual funds is not significantly different from that of comparable funds not investing in infrastructure. • Our results support the growing belief about the superior performance of infrastructure-focused equity mutual funds resulting from the financial characteristics of infrastructure.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".