Análisis de tendencias en los indicadores financieros de la banca mexicana
Bibliographic record
Abstract
Resumen: Este artículo presenta un análisis de las tendencias en los indicadores fi nancieros siguientes: índice de morosidad, índice de cobertura e índice de capitalización de la banca mexicana, durante el periodo de 1997 a 2008.Los bancos analizados son: BBVA Bancomer, Banamex, HSBC, Santander, Banorte, Interacciones e Ixe.La selección de los bancos en estudio, obedece a que éstos representan el 80% de participación del mercado en el otorgamiento de crédito.Además, se utiliza la cartera de crédito desagregada en sus tres principales tipos; los bancos objeto de análisis son los únicos que presentan dicha cartera desagregada durante todo el periodo analizado.El objetivo del análisis de tendencias es sugerir alguna explicación del desempeño fi nanciero de los bancos, refl ejado en sus razones fi nancieras.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".