Bibliographic record
Abstract
Syndicated investments are common in the private equity industry. This pa- per examines how management team composition might influence LBO syndica- tion decisions, and links both to performance. By using a unique hand-collected dataset of 947 LBO investments, we show that investment size, geographic dis- tance, and investor experience increase syndication likelihood. Besides, manage- ment teams with engineers and MBA graduates are prone to syndication. More specifically, Harvard MBAs tend to work with each other while Columbia MBAs are more likely to syndicate with each other as well as with engineers. We find a non-linear relationship between syndication and performance, probably due to different inherent nature of deals. MBA graduates seem to affect perfor- mance in non-syndicated deals, but not in syndicated ones. It thus suggests that MBAs are good at pre-deal screening, and might further explain why they would seek outside expertise when needed. Finally, we find that the strongest syndi- cation match that enhances value is the "Harvard MBA-and-Harvard MBA" pair. Hence, Harvard MBAs may syndicate with each other because a personal acquaintance enables a better match of skills. For other teams, syndication is likely for the purpose of diversification or future deal reciprocity. Keywords: Leveraged Buyouts, Syndication, Top Management Teams
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".