What Motivates Banks and Other Financial Services Firms to Merge? An Empirical Analysis of Economic and Institutional Factors 1
Bibliographic record
Abstract
With globalization and deregulation, the financial services industry in many areas has consolidated significantly since the mid-1990s. What drove financial services mergers among key segments and geographic regions? This paper uses a comprehensive data set comprising 1,434 mergers in 62 countries from 1995-2011 to explore empirically the motivations behind financial services mergers, examining the factors that impact the deal premium paid to effectuate the merger. We find stronger regulatory environments, especially lower corruption, to have a positive effect on the synergies projected to arise from financial services mergers. In contrast, higher financial freedom levels were found to have a negative impact on the deal premium. Also, higher measured levels of legal protection are associated with higher deal premiums in banking mergers, though the opposite is true for insurance. Acquirers also pay higher premiums to purchase targets that are relatively small and easier to integrate. Finally, there is evidence that acquirers pay more to consummate cross-border versus domestic mergers, a result driven by cross-border, investment banking mergers.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.005 | 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".