Bibliographic record
Abstract
My dissertation is divided into four parts. Chapter one summarizes and analyzes data on the Canadian banking system using data from CANSIM and a little used dataset from OSFI. I describe how the Canadian banks earn revenue, fund business activities, and pay expenses and a broad overview of the data, accounting rules, and trends in Canadian banking. Chapter two is an in-depth study on cost efficiency and returns to scale (RTS) in Canadian banking, and I estimate a transcendental log cost function for the six largest Canadian commercial banks. To my knowledge, this is the first work to find evidence of constant RTS among the Canadian banks and it is robust to a number of different asset and price specifications. In Chapter three, I compare the U.S. and Canadian banks from 1996 to 2015 and estimate both a trans-log cost function and, for robustness, an input-oriented distance function. Among the ten largest U.S. commercial banks, I find that constant returns to scale best describes the average bank in this period. However, the smaller banks in the sample exhibited increasing RTS but this became exhausted as bank size increased. In Chapter four, I apply a stress test model based on the work of Duarte and Eisenbach (2015) to estimate potential fire-sale losses in the Canadian banking system from 1996 to 2015. I find that the major banks are resilient to all but the most extreme event. This is because (i) Canada has strong macroprudential regulation that improves the quality of assets, (ii) if given a scenario of severe losses, banks retain a sufficient quantity of liquid assets that these could be used to meet short-term liabilities, and (iii) sufficient equity is available to absorb significant losses. However there remain some areas of concern. Using aggregate vulnerability (AV), I find that the Canadian banking system has become more vulnerable to a fire-sale episode since 2011 which could suggest a rising probability of future losses. The concentration of loans-to-households, including residential mortgages and consumer loans, should be of some concern to regulators.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".