Macroeconomic effects on Private Equity funds’ exit determinations
Bibliographic record
Abstract
This thesis studies the external and internal exit determinants of\nEuropean, Canadian and American private equity funds, using a\ndata set of 32.881 investments completed between 1990 and 2021.\nThe most common exits are through trade sales and sales to GP.\nWe show that the likelihood of the different exit channels alters\nwith changing market- and fund characteristics. The exit channels\ndepend on the general economic environment, which significantly\naffects the window of opportunity for PE firms. Funds with more\nexperience can exploit other exit opportunities while minimizing\ntheir risk of writing off investments. These results indicate that\nthe average private equity fund is flexible and adapts depending on\ncurrent and future market conditions.\nKey words: Private Equity funds, exit channels, write-off, cyclicality,\nleveraged buyout, VIX, interest rate, great financial crisis,\nexpertise.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".