Financial Resources Management in Commercial Banks: Evidence From Latvia
Bibliographic record
Abstract
Financial resources management issues are relevant for each commercial bank. Those banks that operate with an excess of free financial resources lose profitability, but the liquidity of such banks is quite high. Other banks that conduct aggressive policy aiming to place all the available resources with maximum efficiency, are forced to seek solutions to the problem of locating additional liquid funds, in order to ensure timely fulfilment of liabilities. In this article, the authors analyze the state of the resource base of Latvian commercial banks, identify reserves for increasing the efficiency of banks and propose a methodology for optimizing the attraction and placement of resources in commercial banks. Since bank resources management involves two interrelated issues – equity capital management and management of liabilities (attracted and borrowed funds), the authors successively consider two sides of this process. Besides, the process of equity capital and liabilities management is inseparably linked with active transactions, i.e. with the use of resources, therefore the research also focuses on the interrelation between passive and active transactions of the bank and on the development of a methodology for optimizing the attraction and allocation of bank resources. The purpose of the research is to analyse passive operations in Latvian banks, to assess the formation of their resource base and its management, as well as to work out some recommendations on how to update the management of the combined (aggregate) resource base in Latvian commercial banks. The authors have put forward a following hypothesis: the decline in financial resources leads to an increase in the bank's efficiency, and vice versa, the increase in financial resources leads to an inefficient redistribution of funds in the banking activity. During the study, this hypothesis was justified. The theoretical and methodological basis for the research includes scientific works by Latvian and foreign authors, special economic literature, Latvian legislation, regulations of the Bank of Latvia and of the Financial and Capital Market Commission, recommendations of the Basel Committee on Banking Supervision, EU Directives. The authors used such research methods as economic analysis, method of comparison, economic grouping, statistical methods, etc. The application of the proposed methodology for optimizing the attraction and placement of resources is considered on the example of Latvian commercial banks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".