Varieties of funds and performance: the case of private equity
Bibliographic record
Abstract
Within the growing body of literature on private equity, there is intense controversy as to whether, and by how much, the industry really adds value. However, much of the diversity in results can be ascribed to a tendency to focus on a subset of private equity fund types of venture capital and buyout funds or combine very different fund types. This study identifies and explores variations in performance according to eleven different types of fund, providing a much more fine-grained picture than preceding studies. We evidence considerable heterogeneity in performance results between fund types, with funds typically associated with riskier areas of activity having divergent outcomes and generally underperforming compared to buyout funds. We also find that all eleven fund types outperform the stock market when evaluating PMEs. We explore why underperforming fund types continue to attract significant investment. We apply agency theory to help understand general partner behaviour in private equity partnerships and building on the literature on the economics of expectation and of systemic evolution to explain limited partner behaviour, draw out the implications for theory and practice.Highlights An analysis of the relationship between a much wider range of PE fund types than preceding studies, and performance.Explanatory application of agency, expectations, and evolutionary theories.We evidence considerable heterogeneity in the performance of different types of fund. Funds typically associated with riskier areas of activity generally underperform buyout funds.We explore possible explanations behind mediocre or superior returns for specific fund types and why levels of return for some exhibit much more diversity than others.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".