The Relative Importance of Industry- & Country-Specific Factors for Bank Performance in Developed and Emerging Economies
Bibliographic record
Abstract
This study examines relative importance of industry- and country-specific factors for profitability of banks operating in emerging and developed economies. A period spanning between two major crises is examined: since 2002, the end of high-tech bubble burst lasting 1999-2001, until mortgage-driven one in 2008. The empirical support is provided for the idea that industry- and country specific factors are much more important for bank performance in emerging rather than in developed economies due to higher level of uncertainty and as results due to higher magnitude of the reaction of banks to external shocks. The issue of higher level of sensitivity of bank performance to external settings in emerging economies is closely associated with a high level of diverging expectations of market participants with respect to the overall economic situation and the higher agency problems in these economies. Further, this effect is more pronounced for performance of leading banks across-the-board, which is due to their higher ability to deal better with challenges coming from the external environment compared to lagging banks. In addition to that, the findings of this research support the study’s hypothesis that suggests that the importance of the exchange rate regime for bank profitability increases when approaching a semi-flexible regime.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".