Impact of COVID-19 Pandemic on Canadian Banks’ Performance: Non-Performing Loans, Capital Adequacy, and Profitability
Bibliographic record
Abstract
COVID-19 pandemic has impacted many financial institutions due to nonpayment of debt by borrowers and unavailability of funds for further lending.Canadian banks have performed well during COVID-19 pandemic by managing non-performing loans (NPLs) and maintaining capital to risk weighted assets ratio higher than 8%.NPLs of Canadian banks was expected to be high due to high interest rates set by Bank of Canada and soft economic growth during and after COVID-19 pandemic.NPLs are associated with different financial indicators of the bank.As per the guidance issued by OSFI, Government of Canada in October 2023, banks/financial institutions are required to maintain minimum capital to risk weighted assets ratio of 8%.The purpose of this study to check the Canadian banks performance by considering the impact of COVID-19 pandemic based on three financial soundness indicators; regulatory capital to risk weighted assets ratio and earnings, profitability, return on assets ratio and non-performing loans to gross loans ratio.Analysis is done by using multivariate regression model based on last 10 years (Q1 2013 to Q3 2023) quarterly data.Based on the analysis results, it is found that Canadian banks have performed well during and after COVID-19 with little effect of pandemic.Canadian banks have maintained regulatory capital to risk weighted assets ratio above 12% over the study period which is higher than 8% required ratio.Canadian banks' non-performing loan to gross loan ratio is less than 0.5% and return on assets ratio is above 1% during the study period except COVID-19 quarters.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".