A Multivariate Analysis of the Financial Characteristics of Foreign and Domestic Banks in the UK
Bibliographic record
Abstract
For the banking sector in the EU, the UK is one of the most important countries, since over a quarter of all banking assets in the EU are held in the UK and it is the largest single international banking centre, accounting for 20% of the world's cross-border lending. The UK banking sector has traditionally been one of the most open and it is characterized by a rapidly increasing foreign bank presence. Foreign banks account for 55% of the total assets of the UK banking sector. The objective of this paper is to investigate the performance of the banking sector in the UK focusing on the performance of the domestic banks as opposed to the performance of the foreign banks operating in the UK. For this purpose, a multivariate analysis is performed to identify the existing differences between the financial characteristics of domestic and foreign banks, considering profitability, liquidity, risk and efficiency factors. The data sample covers 26 domestic and 32 foreign banks operating in the UK over the period 1998-2001.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".