Would Hedge Fund Regulation Mitigate Systemic Risk? Direct vs. Indirect Regulation Approach
Bibliographic record
Abstract
This paper presents the direct vs. indirect debate of hedge fund regulation and attempts to find which approach is better able to mitigate systemic risk that the industry poses to the economy. The waves of regulatory reforms and enhanced concern regarding investors protection have recently brought attention of the regulators to hedge fund regulation issue. But, many academics fear that direct intervention may limit industry growth and benefit. Addressing these concerns, this paper observes the systemic importance of hedge fund industry based on four criteria’s [size, leverage, interconnectedness to large complex financial institutions (LCFIs) and herding] and concludes that although this industry is still small in terms of size and leverage, their interconnectivity with LCFIs and potential herding make them systemically significant. Hence, regulation of hedge fund is necessary to restrict the transmission of systemic events. Analysing direct and indirect approaches, this paper suggests that the counterparties are best positioned to implement this regulatory change.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".