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Record W3036095137 · doi:10.1108/afr-06-2019-0065

Factors affecting Canadian credit unions' financial performance

2020· article· en· W3036095137 on OpenAlex
Eman Almehdawe, Saqib Khan, Manish Lamsal, Angèle Poirier

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAgricultural Finance Review · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCooperative Studies and Economics
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsPanel dataBalance sheetDiversification (marketing strategy)VariablesFixed effects modelEconomicsInterest rateEconometricsReturn on assetsFinanceActuarial scienceBusinessProfitability indexStatistics

Abstract

fetched live from OpenAlex

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.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score0.787

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.035
GPT teacher head0.203
Teacher spread0.168 · 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