Impact of Risk Management on the Performance of Commercial Banks in Ghana: A Panel Regression Approach
Bibliographic record
Abstract
The financial sector is an integral part of the economy, playing a vital role in the overall economic development of a nation, but commercial banks in this sector face a myriad of risks. This has made understanding the impact of risk management on bank performance crucial. This study sought to examine the effect of risk management on the performance of commercial banks in Ghana. The study used a quantitative research approach, relying on secondary data from the yearly financial statements of the selected banks. Seven commercial banks were purposively sampled. According to the 2017 Ghana Banking Survey, the seven commercial banks selected represent more than 50 percent of Ghana’s financial market by proportion of industrial deposits, which was a criteria for selecting the seven banks. The results of the study showed that of the four types of risks examined vis-à-vis credit risk, operational risk, liquidity risk, and market risk, only operational risk was found to exert a significant influence on bank performance. Operational risk accounted for 99.24% of the variability in bank performance. Furthermore, it was observed that total risk management had a significant impact on bank performance, explaining 74.74% of the variance in bank performance. Since operational risk appears to exert far more influence on bank performance in Ghana than any other risk factor, it is recommended that banks, regulators, and policymakers place more emphasis on curbing operational risks when designing their risk management programmes, as this particular risk, among all the other risk types examined, seems to be the one that exerts the greatest influence on banking performance.
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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 | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".