What Affects the Interest Rate on Deposit From Households
Bibliographic record
Abstract
CThe contribution aim to identify the factors influencing the level of the interest rate in the conditions of the Slovak banking sector. The analysis itself is carried out using a correlation and regression analysis based on quarterly data from the database of National Bank of Slovakia for the period from 1st quarter 2006 to the 4th quarter of 2017. The research has been motivated by the three research questions about the impact of the selected market and intra-bank variables (Does the growth of market concentration lead to a decrease in deposits interest rates? Does the interest rate on the interbank market have a positive impact on deposits interest rates? Does the growth of liquidity lead to a decrease in deposits interest rates?). Based on the results of regression model we have found out, that the increasing concentration, declining interbank interest rate, disinflation, increasing bank capitalisation and declining bank liquidity have a significant impact on the decline in interest rates on deposit products under the conditions of the Slovak banking sector. This support the Structure-conductperformance hypothesis which states that higher market concertation leads to less favourable pricing to customers. We have also found out, that highly capitalised banking sector has a lower pass-through for deposits, which means that the pricing behaviour of this banking sector is least tied to market development. So when the market rate decrease, the deposit rate of highly capitalised banking sector must also decrease and this decrease must be higher than the decrease in market rate.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".