How banking sanctions influence on performance of foreign currency portfolio management
Bibliographic record
Abstract
A good portfolio optimization on banks' currency holdings not only helps meet their needs but also it increases banks' total profits.During the past few Years, sanctions against Iran have influenced profitability of banking currency portfolio holding.The proposed model of this paper considers the weekly information of two years before and two years after sanctions occurred in Iranian banking system.Therefore, the study uses 210 weekly data and proposes a method to analyze the data to measure the performance of banking currency portfolio before and after sanction happens.The proposed model of this paper provides lost profit and unrealized loss and using the idea of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to measure the performance of foreign currency portfolio management.We rank the resulted data.Next, we use some parametric and non-parametric methods to see whether there is any change as a result of sanction on the performance of the portfolio.The results indicate that both of the performance of foreign currency portfolio management and its variance are changed after sanction happened
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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 | 0.002 | 0.014 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".