Bank-specific factors and non-performing loans in the banking sector: Comparative analysis of the Canadian and United States banking sector
Bibliographic record
Abstract
This study aims to examine the nexus between bank-specific factors and non-performing loans, comparing the level of activities managers undertake towards non-performing loans, using data from Canadian and United States banks. There has been a lack of comparative studies researching the effect of bank-specific factors on non-performing loans in Canada and the United States in a single study of lending behavior and the extent of manager efficiency in mitigating the issue of non-performing loans. Consequently, in bridging the gap in the literature and contributing to knowledge, this study examines the effect of bank-specific factors on non-performing loans using a panel regression analysis of standard fixed and Driscoll-K fixed effects. The study explored credit growth, loan loss provisions, bank diversification, operating efficiency, net interest margin, and return on assets as the explanatory variables to measure bank-specific factors. The results of the regression showed that, comparatively, loan loss provisions, bank diversification, operating efficiency, and net interest margin exhibited positive and significant effects on non-performing loans, whereas credit growth and return on assets exerted negative effects on the non-performing loans of banks listed on the Toronto Exchange. On the other hand, while bank diversification, operating efficiency, and net interest margin exhibited positive and significant effects, credit growth, loan loss provisions, and return on assets exerted negative effects on the non-performing loans of banks listed on the New York Stock Exchange. The study recommends that managers implement stringent credit risk assessment frameworks and ensure a loan monitoring system to proactively manage and reduce non-performing loans.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".