Effect of Synergy on Financial Performance of Merged Financial Institutions in Kenya
Bibliographic record
Abstract
Mergers and Acquisitions deals that create value constitute at least one or a combination of financial and operational synergy. This paper investigates the effect of synergy on financial performance of merged institutions in the financial services sector in Kenya. The paper adopted a mixed research design, pre and post-merger secondary data was collected from 40 (forty) institutions in the Kenyan financial services industry that had concluded their merger processes by 31 December 2013. Financial synergy was proxied using the liquidity ratio while operating synergy was measured using growth in sales. Primary data was used to explain the results of the secondary data. Panel data analysis was used to determine the change in the study variables and trends over time between 2009 and 2013, event window (pre-merger and post-merger) analysis was used to test for any significant difference in performance means before and after merger as a result synergy, while regression analysis was used to determine the relationship between synergy and profitability. Results show that there is a positive relationship between performance, operating synergy and financial synergy, and that there was significant improvement in performance post-merger. From these findings, the study recommends that institutions should critically evaluate the overall business and operational compatibility of the merging institutions and focus on capturing long-term financial synergies as this has a positive effect on the performance.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".