Informational asymmetries in US private equity: regulation in a changing regulatory environment
Bibliographic record
Abstract
Purpose This paper aims to analyze the structural features and regulatory challenges of US private equity, with a focus on informational asymmetries between general and limited partners. It examines how short-term, high-leverage strategies and limited transparency have shaped both industry practices and regulatory responses. Particular attention is given to recent efforts by the US Securities and Exchange Commission (SEC) to increase disclosure and accountability. Design/methodology/approach This paper integrates empirical findings, industry reports, case studies and legal rulings to examine informational asymmetries in private equity. It introduces a two-level framework distinguishing asymmetries at the fundraising and operational stages. It also evaluates recent SEC rulemaking, enforcement strategies and court challenges. Findings The short-term, profit-driven strategies of private equity concentrate market power and frequently disadvantage limited partners, employees and customers. Informational asymmetries allow general partners to exploit opaque governance structures, limiting oversight. While the SEC has sought to enhance transparency through disclosure rules, private equity firms have successfully challenged these regulations in court. Despite setbacks, the SEC continues to enforce accountability through whistleblower programs and existing laws. Originality/value This paper highlights the systemic risks associated with private equity and the regulatory challenges in addressing them. It advocates for balanced reforms that maintain private equity’s role in economic growth while ensuring transparency, stakeholder protection and financial stability.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".