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Record W4281778210 · doi:10.1522/revueot.v31n1.1449

L’impact de la gestion du risque de crédit sur la performance des banques commerciales canadiennes

2022· article· fr· W4281778210 on OpenAlexaffvenueabout
Raef Gouiaa, Mikaela Ouedraogo

Bibliographic record

VenueRevue Organisations & territoires · 2022
Typearticle
Languagefr
FieldEconomics, Econometrics and Finance
TopicInsurance and Financial Risk Management
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsPolitical scienceEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.214
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2022
Admission routes3
Has abstractyes

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