Determinant of loan loss provision in Malaysia conventional bank / Noradilah Samsudin
Bibliographic record
Abstract
A loan loss provision is charge to bank profit and loss statement that creates a reserve on their balance sheets. The research problem of this study is to explore the main factor driving the changes in amount of loan loss provision in Malaysian Conventional Bank. The hypotheses testing employed regression with Panel Data Ordinary Least Square for four independent variables which is return on average assets (ROA), earning before tax and provisions (EBTP), non performing loans (NPL) and Gross Domestic Product GDP). This research will collect the data from chosen conventional bank in Malaysia that provides the complete data for this study over the period 1st quarter 2004 until 2nd quarter 2012. The result show there is positive relationship between Loan Loss Provision with Non Performing Loan, Return on Asset and Gross Domestic Product. Managers of Conventional banks can now comprehend better the factors that influence the changes in amount of loan loss provision. The findings of this study should be value to Malaysian Conventional Bank in terms of better manage their reserve to avoid from loss for their futures and smooth their earning to attract customer.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".