Alphas: A Case study in International Institutional Mutual Funds
Bibliographic record
Abstract
In this paper we show that not taking into account the fact that fund managers “deviate” from their stated categories biases upward their alphas. When evaluating fund managers most studies compare managers against the S&P 500 regardless of the sectors managers actually invest in. This procedure does not take into account that an important proportion of US stock managers invest in medium and small companies. This neglect biases performance results. In the international stock arena, not only do studies use the incorrect benchmark but they also neglect to take into account the fact that managers deviate from their stated sector. In this paper we not only employ the correct category the managers invest in but we also take into account the fact that managers systematically drift away from their stated category. This drift occurs for approximately half the funds examined and causes the estimated alpha of managers to be on average 45 basis points higher than it should be if we were to undertake the multiple regression that fund drift demands. In addition to using the right benchmarks, adjusting for “drift” in this paper we chose to use as “benchmarks” the ETF’s in each category so as to compare managers not against theoretical constructs, but against an actual investable vehicle in the corresponding category.
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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 | 0.001 |
| 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) | 0.000 | 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".