Dotaciones para los deterioros de los créditos. Un estudio por ciclos económicos
Bibliographic record
Abstract
Este trabajo estudia los determinantes de las dotaciones para provisiones del deterioro de los créditos en las entidades de crédito españolas desde 1983 al segundo trimestre de 2013. Resultan significativos, además de la morosidad, las provisiones genéricas, el margen de interés, la estacionalidad centrada en el cuarto trimestre y los periodos de crisis. Al ser un periodo extenso se estudian cómo actúan los determinantes en cada uno de los 4 ciclos económicos que se han dado. Se encuentran similitudes importantes en los periodos de crisis y en los periodos de crecimiento, junto a las singularidades de los propios ciclos. Las aportaciones de esta investigación son: i) el diferente comportamiento de las mismas variables explicativas en diferentes periodos del ciclo económico, ii) el diferente comportamiento de los deterioros y la morosidad en los periodos de crisis y iii) la estacionalidad detectada en el cuarto trimestre de cada año. Lo que afecta a los resultados trimestrales y semestrales que publican las entidades financieras en la CNMV. This paper studies the determinants of the provisions for impairment of loans in the Spanish credit institutions in the period 1983 to the second quarter of 2013. There are other significant factors in addition to the loan default, such as generic provisions, the interest margin, and the seasonal nature focused in the fourth quarter and in periods of crisis. As it is an extended period, how the determining factors affected each one of the four economic cycles are studied. Important similarities were found in periods of crisis and the growth, with singularities in their own cycles. The contributions of this research are: i) the different behaviour of the same explanatory variables at different periods of the economic cycle, ii) the different behaviour of the losses and defaults during periods of crisis, and iii) the seasonality detected in the fourth quarter of each year. Furthermore, how it affects the quarterly and half-year results that financial institutions publish in the CNMV.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".