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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".