The effect of loan-loss provision, non-performing loans and third-party fund on capital adequacy ratio
Bibliographic record
Abstract
This research was conducted in connection with the effective enactment of International Financial Accounting Standard IFRS 2020 to improve the concept of hedging accounting as well as basic measurement and classification of financial instruments. IFRS carries the concept of Expected loss backup which begins to acknowledge losses if there is a potential failure to pay even though it has not really happened, allowing the bank to form a larger loan-loss provision. The loan-loss provision is formed based on the number of failed pays in credits indicated by the ratio of Non-Performing Loans (NPLs). Fund distribution can be regulated by the Third-party Fund (TPF). The increasing number of loan-loss provisions and NPLs are feared to affect capital conditions for the bank. Therefore, the study aims to determine the partial and simultaneous influence of the loan-loss provision, Non-Performing Loans (NPLs), and third-party Fund (TPF) against the bank's capital adequacy ratio (CAR). The samples in this study are central government-owned banks, namely Bank Mandiri, Bank Negara Indonesia, Bank Rakyat Indonesia, and Bank Tabungan Negara period from 2011 to 2018. Data taken is a data time series of the quarterly financial statements published by the respective online website of the bank. The analysis used is a multiple linear regression analysis using SPSS Tools version 21 and Microsoft Excel. The results showed that a partial loan-loss provision had no significant effect on the bank's capital adequacy ratio, while the Non-Performing Loans (NPLs) and the Third-party Fund (TPF) were partially influential of the bank's capital adequacy ratio. Simultaneously the three independent variables have a significant effect on the dependent variable capital adequacy ratio (CAR).
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".