Unbalanced Liquidity Management Evaluation of the Russian Banking Sector
Bibliographic record
Abstract
The monetary policy content both in the world and in Russia is changing. The past five years confirm that banking systems are experiencing unprecedented influence of both external and internal macroeconomic factors. Autonomous factors in the banking sector liquidity formation are factors that are not related to the Central Bank operations for its management. However, at present, there are no studies related to the study of the autonomous factors influence on the banking sector liquidity. This article presents a model that fills this gap. We use this model to answer a number of theoretical questions: how is the influence of autonomous factors on the banking sector liquidity carried out and in what stages of development are their manifestations stronger? The calculated model is able to test hypotheses that are informally discussed in political and academic circles. Based on the objectivity of the model, one can estimate the reliability of each of the hypotheses put forward in this study. For calculating the model, time series were used for each day for the period 2013-2016, taken at the site of the Central Bank of Russia. On the basis of the panel regressions device it is shown that among the autonomous factors of liquidity formation the largest impact on the Russian banking sector liquidity is made by the change in balances on the accounts of the enlarged government with the Bank of Russia. The conducted research will allow the Central Bank to forecast the banking sector demand in liquid funds, taking into account the autonomous factors influence.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".