The Informational Content Of The VaR Measures Associated With The Trading Activities Of Canadian Banks
Bibliographic record
Abstract
This paper examines the informational content and the usefulness of Canadian banks' market risk public disclosures. Risk managers use Value at Risk (VAR) as a measure of the dollar amount of a large potential loss to a bank's trading income and common shareholders' equity as a result of extreme and low-probability market price changes. Five different VAR metrics (high, low, range of estimates, average and end-of-period values) are now published and recognized benchmarks for measuring market risk exposure, and its potential impact on a bank's financial position. At the explicit request of regulators, financial analysts and competitive pressures, most large commercial banks in North America are now reporting the five forms of VAR numbers described above in their quarterly and annual financial reports. To examine preliminary evidence on the informational content of such public financial disclosures, we composed a sample of seven of Canada's largest commercial banks. In particular, we investigate if "ex ante" VAR numbers help financial analysts, investors, and regulators to explain the subsequent variability of commercial banks' trading income and of their ratio of market value to book value of common shareholders' equity over time.
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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 | 0.004 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".