What Are the Differences in the Area of Profitability and Efficiency When Early and Late Adopters Are Analyzed Regarding the Basel III Leverage Ratio?
Bibliographic record
Abstract
This research investigates whether banks that adopted new regulatory requirements earlier, such as Basel III, are more profitable, as well as more efficient, than banks that adopted these requirements later. In addition, all 138 banks are based in the G7 member countries, which are the most developed countries in the world. Also, banks are categorized into early and late adopters based on Basel III Leverage Ratio performance by using Fitch Connect. Moreover, profitability ratios, such as the Return on Equity, Return on Assets and efficiency ratio Operating Efficiency, were collected from Fitch Connect to analyze if early adopters were more profitable and efficient than the late adopters. Also, STATA is used to analyze descriptive statistics and a univariate analysis of both groups. Furthermore, the finding is that early adopters of the Basel III Leverage Ratio are not the more profitable or efficient firms compared to late adopters as anticipated. In addition, the results of early and late adopters do not differ that much in the analysis regarding profitability and efficiency ratios. This implies that it is not necessarily correct to assume that stricter regulation, such as Basel III, will negatively affect the profitability or efficiency of banks. In addition, these results are useful to regulators and policymakers of the G7 member countries for two reasons. Also, regulators can clearly see how banks are adopting new stricter regulation.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".