The Impact of Past Syndicate Alliances on the Consolidation of Financial Institutions
Bibliographic record
Abstract
The impact of past syndicate alliances on the consolidation of financial institutions is examined. The odds of two lenders combining increases with the intensity and exclusivity of their prior syndicated loan alliances. The impact is higher for international mergers and acquisitions (M&As) and for prior syndicate co‐relationships where the acquirer and target were participant and lead, respectively. The odds of a particular lender being a target decreases as its return on equity (ROE) and earnings/price (E/P) ratios increase and as its size and growth opportunities decrease. The intensity and exclusivity of the syndicated loan alliances leading up to M&A announcements are significantly higher for non‐US versus US M&As. The significantly lower short‐ and long‐term performances for both acquirers and targets with prior syndicate co‐involvements disappear in the presence of control variables that account for the less frequent use of cash payments, the greater incidence of divestitures, and the higher percentage of shares acquired through their M&As. Acquirers with versus those without past syndicate target co‐involvements exhibit greater outperformance for control‐firm benchmarked ROEs and lower underperformance for control‐firm and prior‐to‐M&A benchmarked ROEs.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".