Banking Transparency, Financial Information and Liquidity Risk Management: Case of Saudi Banks
Bibliographic record
Abstract
The article aims to assess the impact of banking transparency on liquidity risk. To do so, we first test the determinants of Liquidity Coverage Ratio (LCR) as well as ensure the resilience of the Saudi banking system over the period from 2014 to 2021. Using System GMM with bank-specific and macroeconomic variables, results show that capital adequacy ratio, SIZE, GDP growth as well as past LCR levels significantly influence the LCR. Secondly, we adopt the Panel Vector Auto Regression (PVAR) approach to assess the response of the LCR to various shocks. Impulse Response Functions (IRF) and variance decomposition demonstrate that the shocks to past LCR, AQ, CAR and GDP increase future liquidity risk. Thirdly, we prove that Saudi banks implement less than 50% of the transparency dimensions. They mainly disclose financial information and information on information credibility. Barely 18% of information on non-financial components of banking activity is made available to the public. Information on liquidity risk and on the timeliness of information is not available either in annual reports or on the bank's website. On average, the banks in the sample do not give importance to the publication of reports. These results may undermine the effectiveness of the guidelines of the Basel Committee agreements to reduce risk-taking by Saudi banks.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".