Determinants of Banks Profitability: Empirical Evidence from Ghana’s Commercial Banking Industry
Bibliographic record
Abstract
Over the years, Ghana’s commercial banking industry has been bedeviled with numerous challenges. The unbridled effect of this is the 2018 banking sector megrim which led to the collapse of seven major banks. This pointed out that it is very crucial to identify and mitigate the factors that negatively affect the performance of the banking sector. This paper is used to investigate the effect of banks specific variables (BSVs) and macroeconomic variables (MEVs) on the profitability of commercial banks (NIM, ROE, and ROA) in Ghana using FRED annual data of 25 years. In order to avoid endogeneity problems and aggregation bias, we used the SURE model to run the estimates simultaneously. The result reveals that profit earned by Ghana’s commercial banks is largely influenced by both internal factors such as KA, AQR, LMGT, MEFFI, and Z-Score and fluctuations in the macroeconomic environment (GDP and FOREX). The impact of KA, LMGT, MEFFI, and Z-score is significantly positive whereas AQR (NPLs) is found to have a negative effect on banks profitability. GDP has a significant negative impact on Ghana’s commercial bank’s profitability whiles forex induced commercial banks profitability positively, but inflation CPI does not determine the profitability of commercial banks in Ghana.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".