What Drives the Tracking Error of Hedge Fund Clones?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Hedge fund clones provide a liquid, efficient, and transparent alternative to investing in hedge funds. As a group, however, their recent performance has been disappointing, despite the large variation in the replication methodologies used. The author investigates hedge fund clones’ tracking errors and finds that contrary to common belief, the reliance on historical data to “reverse engineer” hedge fund allocation is not the primary cause. Instead, the author identifies two important drivers of tracking errors of hedge fund clones. One is changes in marketwide liquidity levels as measured by the basis between derivatives and cash securities. The second is biases in measuring the returns that arise due to attrition among hedge funds that affect the performance of commonly used hedge fund indices. Together, they account for about half of the variation in hedge fund clones’ tracking errors over time. <b>TOPICS:</b>Real assets/alternative investments/private equity, quantitative methods, financial crises and financial market history, performance measurement
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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 | 0.002 | 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 | 0.002 |
| 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 it