Market Misreaction? Leverage and Mergers and Acquisitions
Bibliographic record
Abstract
Using a large database of U.S. mergers and acquisitions (M&As) announced from 2010 through 2017, we examine the effects of capital ratio (leverage) on the announcement period stock price reaction as well as on longer-term stock returns and performance, for banks, making comparisons with non-banks. We compare announcement period reactions (computed in different ways) for lower (lower than sample median) capitalized banks and non-banks with that for higher capitalized banks and non-banks. We confirm our results using multivariate analyses—after controlling for year and industry fixed effects—and we check the associations of capital ratio with announcement period abnormal returns, longer-term performance, as well as certain bank-specific and non-bank specific performance measures. For banks, we find that a lower capital ratio of acquirers at the time of the announcement of the M&A is significantly associated with negative announcement period abnormal returns. However, for these banks, the longer-run abnormal returns and performance are positive. The opposite is true for non-bank M&A announcements: higher equity ratios (lower leverage) of acquirers as at the time of the announcement is significantly associated with negative announcement period abnormal returns. Yet, for such non-banks, the longer-run abnormal returns and performance are positive. This shows that the market may misreact, on average, to both bank and non-bank M&A announcements based on the acquirer’s leverage at the time of the announcement.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".