Effects of Negative Interest Rates on Stability and Profitability of Commercial Banks
Bibliographic record
Abstract
The article examines the impact of the European Central Bank's (ECB) negative interest rate policy (NIRP) on commercial banks in Europe. It traces the historical context of the policy back to the financial market and banking crises, which eroded trust among banks, leading to liquidity issues and economic downturns. To counteract this, the ECB introduced negative interest rates in 2014, aiming to stimulate the economy.The research question focuses on how NIRP affects banks' annual reports, particularly concerning total assets, credit and risk volumes, proprietary trading, profits, and stability. The hypothesis suggests that NIRP significantly increases risks in bank profitability and stability. Analysis spanning from 2014 to 2022 compares negative interest rate changes with bank balance sheet and profit/loss account developments.Economic theory predicts that negative interest rates decrease bank profits but increase lending activity. Statistical analysis confirms a correlation between NIRP and increased credit risk volume, as well as a shift from bonds to shares in proprietary trading. However, there is no statistical evidence linking NIRP with profit and profitability declines.In conclusion, while NIRP does lead to a significant increase in credit risk volume, it does not halt falling profits and profitability. Therefore, the hypothesis that NIRP increases risks for commercial banks' profitability and stability holds true, posing a threat to financial stability.
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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 | 0.003 | 0.021 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".