Liquidity Issues in the Banking Sector from an Accounting Perspective
Bibliographic record
Abstract
The recent financial crisis highlighted the inability of financial markets of being always able to cope with the liquidity needs of banks. This gave rise to a great attention to the issues related to the liquidity in the banking sector.Stakeholders interested in assessing the liquidity profile of a financial institution can rely on data provided through its financial statements. This demonstrates the strong influence that the accounting discipline can have on it. Accounting standards can play an important role in depicting the liquidity profile (and the associated risk) of an entity, as they contribute to produce information useful to predict timing, uncertainties and amounts of its future cash flows.The objective of this theoretical study has been to investigate the contents of the IASB Conceptual Framework and of some of its standards, i.e. IAS 7, IFRS 7, IFRS 9. In particular, the aim of the analysis has been to verify if the financial information requested by the regulation is adequately useful and relevant in order to assess the liquidity profile of a financial institution. In our opinion, the IASB discipline still presents some deficiencies on this aspect, in particular for entities operating in the banking sector.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".