Close Communications: Hedge Funds, Brokers and the Emergence of Herding
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
We examine how communication, evaluation and decision‐making practices among competing market actors contribute to the establishment of herding and whether this has impact on market‐wide phenomena such as prices and risk. Data are collected from interviews and observations with hedge fund industry participants in Europe, the USA and Asia. We examine both contemporaneous and biographical data, finding that decision‐making relies on an elaborate two‐tiered structure of connections among hedge fund managers and between them and brokers. This structure is underpinned by idea sharing and development between competing hedge funds leading to ‘expertise‐based’ herding and an increased probability of over‐embeddedness. We subsequently present a case study demonstrating the role that communication between competing hedge funds plays in the creation of herding and show that such trades affect prices by introducing an additional risk: the disregarding of information from sources outside the trusted connections.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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