The Capital Requirements (Basel III) and the Banking Sector Business Activity
Bibliographic record
Abstract
Active development by international organizations and national regulators of the emergent standards purporting prevention of crises and increase of banking stability is typical for the last years. However, practical implementation of the standards is not so definitive. This article is devoted to the analysis of impact of new requirements in the field of control over the quality and adequacy of the capital of banks, introduction of the additional parameters of risk-related load on the basis of financial leverage on business activity of banking sector. The issue of correlational study of capital adequacy ratio of banks and their credit activity was considered by different scientists over the last years; however, no decisive results were obtained. At the same time, the belief on the change of capital requirements and bank loans prevails. Generally, after strengthening of capital requirements, the banks reduce the loan growth. The authors of research prove this conclusion for the Russian economy. Following carried out analysis, the conclusion was also drawn that against the background of essential excess of the planned level of financial leverage, banks generated a highly risky asset portfolio, where the new standard did not address. It is the authors' opinion that for the purpose of impact on financial activity, the leverage levels must be differential for banks having various business models.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".