A study on relationship between CAMELS Index's and Risk taking: A case study of Iranian banking industry
Bibliographic record
Abstract
Among the activists of the money market, banks as the most important financial institutions undertake an important role in optimal appropriation of financial short-term resources.Furthermore, they allocate the short-term surplus funds to enterprises, which have a short-term need.Holding a main part of the funds in economy circulation, banks have a critical role in adjustment of economic relations.Banks are facing different types of risks in their daily operations.In the banking system, the CAMELS indictors are used to evaluate and rate of the performance of banks.The CAMELS rating model is one of the most effective systems of financial assessment in banks.Therefore, in this research, the effects of CAMELS indicators of banks on risk taking of Iranian banks are studied.The statistical population of the national banking system includes all governmental and private banks.The whole statistical population is studied as a research sample during 2006-2011.Taking into consideration the fact that the research data or section-bounded and time-bounded, a combinational regression analysis has been used.The results of the combinational regression analysis have supported the presence of a reverse and meaningful effect of the indicators of assets quality and sensitivity of market risk on risk taking in national banks.In addition, the results have supported the direct and meaningful effects of capital sufficiency and quality of profit-making on risk taking, however, the effects of the indicators of management quality and liquidity quality on risk taking have been rejected.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".