L’impact de la gestion du risque de crédit sur la performance des banques commerciales canadiennes
Bibliographic record
Abstract
La question générale de cette étude est de comprendre l’impact du risque de crédit sur la performancedes banques canadiennes. Les résultats permettent de confirmer que la gestion efficace du risque de crédit a un effet positif sur la performance des banques canadiennes et que l’augmentation du risque de crédit entraîne une diminution de la performance financière et boursière. Les résultats permettent également d’infirmer que certains ratios tels que les prêts sur les dépôts et sur l’actif affectent positivement la performance opérationnelle. Cette recherche met clairement en évidence un éclairage et une meilleure compréhension des déterminants du risque de crédit, et sa pertinence à promouvoir les politiques de gestion de ce risque, d’où une meilleure performance des banques. The general question of this study is to understand the impact of credit risk on the performance of Canadian banks. The results allow to confirm that effective management of credit risk has a positive effect on the performance of Canadian banks and that increasing credit risk leads to a decrease in financial and stock market performance. The results also confirm that certain ratios such as debt to deposit and debt to asset positively affect operational performance. This research clearly highlights a clarification and a better understanding of the determinants of credit risk, and its relevance to promote policies for managing this risk, hence the better performance of banks.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".