Disagreement exploitation and the cross‐section of hedge fund performance
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
Abstract This study examines the role of market disagreement in explaining the cross‐section of hedge fund performance. In a market where disagreement fluctuates, skilled arbitrageurs may employ trading strategies to exploit the mispricing caused by disagreement and short‐sale constraints. Skilled hedge funds with high sensitivity to disagreement can take advantage of mispricing in high‐disagreement periods to improve their performance. We show that hedge funds with a high disagreement beta tend to possess skill in exploiting disagreement and, as such, they can earn higher cross‐sectional returns compared to other hedge funds lacking this skill. Existing risk factors and a tradable disagreement factor do not fully explain the difference in hedge fund performance between those with high and low disagreement betas. Further evidence shows that experienced hedge funds and hedge funds that charge a high incentive fee are likely to have high disagreement betas. Our empirical findings are robust in using various disagreement measures and methodologies to estimate disagreement beta.
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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 | 0.001 | 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 | 0.000 | 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 it