An Econometric Approach on Performance, Assets, and Liabilities in a Sample of Banks from Europe, Israel, United States of America, and Canada
Bibliographic record
Abstract
The 2008 financial crisis had a major impact on financial markets, especially on the banking system. Mortgage-backed security investments were among the causes that determined the tremendous shortage of cash. Before the crisis, American banks were considered important investors on these markets, as indicated by the structure of their assets and liabilities. How grounded were their investment decisions? To answer this question, the study examined the influence of financial performance on bank assets and liabilities of the most important 45 banks from Europe and Israel, United States of America, and Canada during the period 2006–2020. Through a panel generalized method of moments approach, empirical results indicated a strong impact of bank assets and liabilities ratios on financial performance indicators. The study emphasizes that bank managers, researchers, regulators, and supervisors should consider investment policies, especially for bank assets and liabilities. Therefore, a high level of interest income is an important tool for increasing assets and liabilities. At the same time, fees are other levers that could improve bank benefits and ultimately develop the lending activity when interest income enters a descending trend.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".