Determinants of Bank Performance in Ghana, the Economic Value Added (EVA) Approach
Bibliographic record
Abstract
Previous Ghanaian governments attempted to use Ghana’s well-developed banking system to grow the economy. Bad loans caused the banks to suffer great losses during the late 1980s, and decline in the cedi value caused a rise in the banks external loans. In 1988, the government initiated financial reforms to strengthen the banking sector. The reforms aimed to improve profitability, efficiency and productivity of banks. In spite of these reforms in 1990s, banks’ performance has remained poor with substantial gaps in service delivery to private agents. There is sufficient empirical evidence that poor performance is manifest in low performance of bank indicators, including: high levels of credit risk to private agents, poor quality loans, limited and or inadequate capitalization, operational inefficiencies, higher incidences of non-performing loans, higher levels of liquidity risk; among others. Empirical evidence clearly shows that studies focusing on Ghana’s financial sector are still scanty and limited. The study seeks to investigate the determinants of banks ‘profitability in Ghana for the period 1988 to 2011 using Economic Value Added (EVA) technique to measure performance. The study evaluates two performance yardsticks to determine the best alternatives. The result of the study suggested economic value added as the best measurement as against the standard accounting measurement namely; ROA. Inflation was registered not to be affected Ghana’s bank performance. The study results draw some implications for policy that helps to improve performance of the banking sector in Ghana.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".