Components of Financial Stability of Credit Institutions: A New Perspective and New Horizons
Bibliographic record
Abstract
The article discloses a financial model characterizing the stability of credit institutions. In addition to the traditional quantitative indicators of the bank's activities, such as capital, assets, profit of the credit institution and others, relative indicators are of particular importance for assessing the effectiveness of banking activities. It is necessary to evaluate both quantitative and qualitative indicators of the activity of credit institutions, the synergy of which will enable them to identify the components of financial soundness and their assessment. An assessment of the financial stability of an individual credit institution is possible only based on the results of a comparison with the industry average components of financial stability. Particular attention is paid to such a component of assessing the financial stability of banks, as the effectiveness of the settlement of troubled debts. The authors of the article developed an alternative system for choosing a strategy for resolving the problem debt of credit institutions based on the qualimetric model. The idea and motivation (idea, purpose, motivation) The idea of the analysis is to study the validity and completeness of the hypotheses in accordance with which a study was made of financial stability of credit institutions and its impact on the willingness of customers and investors of banks to place their funds with them, as well as their possible outflow or counteraction to it depending on compliance their market discipline, the level and quality of risk management, as well as the availability of transparent and reliable information about the financial situation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".