A study on effect of bank resources and consumption on liquidity
Bibliographic record
Abstract
Liquidity management in banks is a conflict between risk and return.On the one hand, the lack of liquidity, in addition to imposing heavy costs of providing resources (including borrowings from the central bank at elevated rates), may also lead banks to face with bankruptcy.On the other hand, the maintenance of excessive liquidity than needed will destroy investment opportunities and potential profitability.Therefore, for proper liquidity management, it is necessary to understand the factors affecting this sector to be able to exert control on each of the elements, and to prevent the incidence of problems or even crisis thereby optimize the bank profitability as far as possible.This study aimed to analyze the resources, expenses, and liquidity operating as the main activity parameters in Bank Shahr.During the study timeframe (2010 until the end of 2012), every 15 days was selected as the sample community, which were selected due to a 15-day trend analysis using census method and not by random sampling.The related data concerning each of the items, i.e. resources, expenses, and liquidity were collected and analyzed by linear regression based on time or periods of time.The above items were compared through analysis of the relative contribution to the total ratio and correlation analysis.The results showed that the growing trends of resources and expenses almost coincide and followed a specific trend and, in general, the growing trend of the resources was rising with a higher slope compared with those of expenses and liquidity.In addition, the growing trend of liquidity was almost identical throughout the entire period of study and no unusual trend was observed.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".