Market Reactions to Fintech M&A: Evidence from Event Study Analysis of Financial Institutions
Bibliographic record
Abstract
The rise of fintech has disrupted traditional financial services, prompting banks and asset managers to respond strategically, often through mergers and acquisitions. This study investigates the short-term market reaction to M&A announcements involving fintech targets by incumbent financial institutions. Using an event study methodology centered on different event windows and cumulative abnormal returns computed via the market model, the analysis incorporates regression models with bidder-, deal-, and target-level variables to identify the drivers of performance. The results show that, on average, financial institutions experience negative abnormal returns around announcement dates, suggesting limited short-term value creation. Higher market-to-book ratios and tax rates are positively associated with CARs, while lower profit margins are linked to better market reactions. Subsample comparisons reveal that U.S. acquirers underperform their European peers, commercial banks fare worse than asset managers and investment banks, and pre-COVID-19 deals yield more favorable returns than post-COVID-19 ones. Robustness checks using different market benchmarks demonstrate that key patterns—especially those related to geography and timing—are sensitive to benchmark selection. Overall, this study highlights market skepticism toward fintech acquisitions by traditional financial institutions, particularly in specific contexts, and emphasizes the importance of controlling for structural factors when interpreting abnormal returns.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".