Interplay Between Competition Networks, Strategy Uniqueness, and 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 investigates the effect of competition networks among hedge fund managers on strategy distinctiveness and fund performance. Using a sample of 2711 US‐based hedge funds from the Lipper TASS database between 1994 and 2018, we construct a hedge fund competition network (HFCN) based on alumni and employment ties derived from LinkedIn profiles. We find that greater centrality in the HFCN, indicating closer proximity to peer competitors, is associated with lower abnormal performance. This effect is partially mediated by a decline in strategy distinctiveness, measured by the Strategy Distinctiveness Index (SDI). Funds with stronger network ties tend to exhibit greater return similarity with peers, suggesting that social proximity encourages strategic conformity. The results are robust across performance metrics, style classifications, and subsamples and are particularly pronounced among managers with strong cognitive profiles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 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 it