How do Lead Financiers Select Their Partners in Buyout Syndicates? Empirical Results from Buyout Syndicates in <scp>E</scp>urope
Bibliographic record
Abstract
Relying on a unique dataset covering 366 buyout syndicates in Europe over the period 1999–2009, we empirically investigate the partnering decisions of lead financiers. We find that lead financiers select investors with whom they developed a prior relationship, either directly or indirectly. Also, lead financiers prefer partners with expertise in the target industry and partners with knowledge about target‐country institutions, particularly when their own knowledge in these areas is limited. Finally, they favor investors with a similar level of cognition and status. We further show that these results are mainly driven by the risky buyouts in the sample. Overall, the above partnering choices are found to have genuine economic effects for the post‐buyout performance of target firms, with expertise as regards the target industry and target‐country institutions having the largest beneficial effect.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".