How do Lead Financiers Select Their Partners in Buyout Syndicates? Empirical Results from Buyout Syndicates in <scp>E</scp>urope
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Bibliographic record
Abstract
Relying on a unique dataset covering 366 buyout syndicates in E urope 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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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 it