M&A between financial services firms or banks and fintech’s
Bibliographic record
Abstract
Objective: This paper investigates whether acquiring fintech firms enhances the financial performance of traditional financial service firms and banks, and how these effects vary across countries and sectors. Methodology: We employ a panel-data difference-in-differences (DiD) approach with Mundlak correction to estimate the causal effects of fintech mergers and acquisitions (M&As). Our sample includes 6,460 M&A deals from 2014 to 2023. Results: The subgroup analysis reveals that acquiring fintechs from Japan and Australia significantly improves ROE, while acquisitions from Spain, India, Germany, and Canada show heterogeneous and often negative performance outcomes. Limitations: The analysis is limited by the availability of financial data and potential unobserved confounding factors. Findings are not generalizable beyond the studied period or sectors. Originality: This study contributes new causal evidence on the financial outcomes of fintech acquisitions utilizing a novel subsample search algorithm. Conclusions: Fintech M&A outcomes are highly context-dependent. Sectoral and geographic characteristics must be considered in strategic acquisition planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".