Factors affecting Canadian credit unions' financial performance
Notice bibliographique
Résumé
Purpose The purpose of this paper is to identify the factors that affect the Canadian credit unions' financial performance which play an important role in providing financial services to the agriculture sector. Design/methodology/approach We surveyed the literature to identify different performance metrics of credit unions and a set of possible factors that might affect their performance. We collected data related to different dependent and independent variables from financial statements and balance sheets of 189 credit unions and from general websites like Statistics Canada and Bank of Canada. Then, we imputed the missing data and developed fixed effect and random effect panel data regression models. First, we used return on asset as the main dependent variable. Afterwards, we used six performance metrics to check the robustness of our models. Findings From an initial list of 16 possible factors that might affect the financial performance of a credit union, we were able to narrow the factors down to the nine most significant ones. It was observed that credit unions in the prairies were more likely to perform well financially as compared to other provinces. Membership size, the size of a credit union in terms of total assets, capital adequacy ratio, market penetration, diversification of income, inflation rate and provincial GDP and interest rates were significant. The cross-sectional analysis performed confirmed the findings of the fixed effect panel data models. Research limitations/implications This study has a limitation concerning the number of years included into the time series analysis. Only ten years worth of data were available. Practical implications Results provide credit union management, service providers for credit unions and market analysts with a current understanding of how different internal and external factors might affect return on assets, return on equity, delinquency, cash ratio, efficiency ratio, asset growth and loan growth. Our models can be used to predict financial performance of credit unions based on the defined significant variables. Originality/value Although there is a wide body of literature that studies performance of banks, not many studies focus on credit unions. Moreover, the existing studies are based on credit unions in United States or Europe, and literature on Canadian credit unions is scarce. The data collected covered 189 Canadian credit unions. To our knowledge this is the first study that looks at the various internal, external and regulatory factors together that affect the credit unions in various jurisdictions of Canada.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».