The Influence of Liquidity Risk on Financial Performance: A Study of the UK’s Largest Commercial Banks
Bibliographic record
Abstract
The Basel III regulations turned the banking industry around worldwide and created new challenges for banks’ financial stability, particularly in liquidity management. As the demand for compliance with the rules started to grow, the inability of banks worldwide to meet the Basel III requirements about liquidity shifted the way they work. This paper highlights the complex relationship between liquidity and bank profitability in the post-Basel III era. Based on market presence and influence, 10 publicly traded UK commercial banks were selected for 2015–2021. Panel data, using FGLS regression models, were tested to elaborate in detail how the liquidity risk indicators determine banks’ performance, as measured by different profitability indicators. The findings were diversified: some showed that the relationship between liquidity risk indicators and bank profitability is contingent upon the interaction of several dimensions that range from the internal aspects of the banks themselves to general macroeconomic factors. This study provides vital insights into the current literature on risk management, especially about liquidity risks and their effect on bank performance. The findings of this study contribute meaningfully to the knowledge base for banks, regulators, and policymakers. This will contribute to better decision-making, financial stability, and long-term development within the UK’s banking industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".