An Empirical Analysis of Private Equity, Listed Private Equity and Public Equity
Bibliographic record
Abstract
It is not a secret that the world of Private Equity is growing year by year, with an immense upwards trend and that the willingness of understanding how Private Equity as an alternative capital raising strategy can be used by companies which do not want to go public and get financed by Public Equity.The underlying paper investigates the world of Private Equity, Listed Private Equity and Public Equity. Regarding transparency of data, the comparability of Public Equity to Listed Private Equity provides way better results than comparing Public Equity to Private Equity.Due to the listing of the firms, the disclosure requirements need to be fulfilled. The GLPE Index illustrates and underlines the effectiveness of Listed Private Equity as a financing source. The GLPE Index contains 40 to 75 listed Private Equity firms which mostly invest Private Equity in firms that are not listed.However - The top ten constituents of this index show their diversification considering the companies they have invested in and their performance, the countries in which the Headquarter are located and the performance of these funds. One can see clearly that 2021 was an extra ordinary year for all of them. They achieved returns and performance better than indices like the MSCI World and S&P 500.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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 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".