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. <b>TOPICS:</b>Wealth management, mutual fund performance <b>Key Findings</b> • 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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".