Bank Size and Financial Risk Exposure on Financial Performance of Commercial Banks in Kenya
Bibliographic record
Abstract
The performance of banks in Kenya has become a major concern for economics and policy makers due to the role of banks remaining central in financing economic activities. The study sought to establish the effect of bank size and financial risk exposure on financial performance of commercial banks in Kenya. The descriptive research design and a positivist approach were adopted. The Berger and Hannan approach was used to establish the relationship between bank size, financial risk exposure and the moderating effect of macroeconomic variable on the financial performance of commercial banks in Kenya. Various diagnostic tests were carried out and the study data structure was panel hence Stata was employed to determine the relationship between the variables. In conclusion, banks need to grow bank sizes where they enjoy both economies of scale and scope. The Kenyan Treasury should design policies that would increase the capital size, liquidity requirements and deposit insurance premiums; this may assist in enlarging the size of banks to a level where they are fairly equal with none having relative market power to drive the market. Areas of further research may include but not limited to considering other variables besides the financial risk exposure and bank size in determining their effect on the financial performance of commercial banks in Kenya. The research may as well be done in the East African or African context. The further studies should seek to leverage on mixed research approaches that utilize both quantitative and qualitative research.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".