Vector error correction model approach in explaining the relationship between non performing loan, capital ratio and earning before tax and provision towards loan loss provision practice by Public Bank Bhd / Yumiza Kamal
Bibliographic record
Abstract
Loan loss provision is directly related to the manipulation of accounting number by bank in order to control the performance in future. Based on the previous literature, loan loss provision is associated with earning management, capital management and signalling mechanism. The study done by Faridah and Wahida, (201 1) highlight that Islamic and conventional banks in Malaysia use loan loss provisions in their earnings and capital management. Therefore, the overall objective of this study is to examine the determinant and to suggest the motivation on loan loss provision practice by bank. Public Bank Bhd has been chosen as the sample of this study from the fourth quarter 2004 until third quarter 2012 by using econometric model which is vector error correction model to see the relationship between depended variable with independent variable. The source of data is obtained from Bursa Malaysia Bhd. An empirical result indicates that EBTP is inverse relationships between LLP compare to other factor such as NPL CR that show positive relation with LLP. The limitation in this study will be on the difficulties to obtain the detail disclosure information on loan loss provision. In addition, this research will beneficial for banking institution in order for them to preserve in future by cautiously screening giving loan and avoid defaults and it is also to crystallized view on manipulation the accounting number towards academician.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".