ЭКОНОМИЧЕСКИЕ И ФИНАНСОВЫЕ ПОКАЗАТЕЛИ БАНКОВСКОГО СЕКТОРА АРМЕНИИ ПОСЛЕ COVID-19 И ТРЕТЬЕЙ КАРАБАХСКОЙ ВОЙНЫ
Bibliographic record
Abstract
The purpose of the article is to put forward and justify that the pandemics of Covid-19 and the third Karabakh war influenced the economy of Armenia and especially the banking system. The article aims to go deeper into the analysis of the banking system with concentration on the main performance evaluation indicators. Our task is to reveal and express in monetary terms the influence of the war and the pandemics on the performance of banking system. In the course of study, scientific and (analysis, calculations) and parametric (VaR calculation parametric method) methods were used. Based on the study it should be concluded that, as a result of the war, the depreciation doubled, and in a quarterly breakdown, the total net profit of the banking sector for the 4th quarter decreased by 9 times compared to the 3rd quarter, while the return on equity fell from 7.9% to 0.8%. Accordingly, the exposure to market risk has also increased, which is reflected in the Value-at-Risk index of the General Market Index. After the war, the 1% VaR GMI increased by 0.5 percentage points, and the 5% VaR increased by 0.6 percentage points.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".