Effects of firm's Return on Assets on non-performing loans of deposit taking Sacco's in Africa-case study Kenya.
Bibliographic record
Abstract
Massive Non –performing loans present negative effect on the level of investment, level of deposit liabilities, returns. Non-performing loan can aggravate the already high pressure on government revenues as endeavor to resolve it may force government to provide financial support to delinquent financial institutions. ROA as characteristics related to deposit taking SACCOS was analyzed to determine at what extent it influence level of NPLs. Return on assets was determined by net profit after tax divided by total assets. The period of the study was 6 years, from 2012 to 2017.The data used was derived from financial statements which are submitted by DT-SACCOs to SASRA offices whereby a total of 119 deposits taking SACCOs was used as the target population of the study. The study recommended that DTS should have and maintain prescribed level on return on assets since the relationship of ROA and NPL was positive indicates an increase of return on assets leads to increase of NPL and this will reduce alarming level of NPL. Study was carried out for only six years hence results obtained can only be applied within that period of study hereafter long term inferences cannot be drawn from results interpreted. Other studies should be undertaken and examine variables which were not used in this study hence the high rate of NPLs will be controlled.
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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 | 0.000 |
| 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 | 0.000 |
| 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".