Flow-Performance Relationship for Hedge Funds
Bibliographic record
Abstract
Preliminary version The shape of the flow-performance relationship in the hedge fund industry is not constant over time, but varies across market conditions. We employ a switching regression approach to explain quarterly hedge fund flows, based on defining two regimes where either inflows or outflows are dominating, combined with a flexible functional form for each of the equations, allowing for a nonlinear impact of past performance at different lags. We characterize the local and global convexities of the relationship by several measures and investigate how they vary over time. Overall, the flow-performance relationship appears flatter at the one quarter horizon than at the four quarter horizon. Moreover, the curve is not uniformly convex or concave. For most periods, the flow-performance relationship is locally convex for a large subset of funds but becoming concave for the top three deciles of performers. The kink in the top part of the curve is more pronounced in periods when aggregate inflows to the industry are high. This effect seems mostly driven by funds that are restricting new inflows, for example due to capacity constraints or decreasing returns to scale. These results are helpful in understanding the incentives of hedge fund managers due to the implications for manager compensation based on performance fees and management fees.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".