Financial Performances of Microfinance Institutions in Cameroon: Case of CamCCUL Ltd.
Bibliographic record
Abstract
Microfinance institutions (MFI) aim at reducing poverty. To achieve such an amazing objective, microfinance institutions in Cameroon have to perform financially well as financial supports from donors are dwelling and irregular. Therefore, to what extent do MFI and industry specific factors determine CamCCUL’s financial performance? By using OLS estimation method to measure the effect of internal and external determinants of CamCCUL’s financial performance in terms of its return on assets, the study exploited a thirty two years secondary data obtained from mix market, CamCCUL’s annual balance sheets and reports to run the regression. The results on the one hand, showed that portfolio at risk, size and operational expenses significantly affect the financial performance of CamCCUL. On the other hand, market concentration had a negative but statistically insignificant effect on CamCCUL’s financial performance. The study therefore recommends that since inefficiency is the bottleneck of CamCCUL, the management should pay great attention to a good expense of management policies or reduce operating costs and credit risk management by employing different technologies to minimize cost. Also, CamCCUL managers should promote training in financial operations, portfolio management, risk assessment and management, management of loan arrears, and strategies, among others.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".