How Useful Are Banks' Earnings-At-Risk And Economic Value Of Equity-At-Risk Public Disclosures?
Bibliographic record
Abstract
This paper examines the information content and the usefulness of banks' interest rate risk public disclosures. ALM managers use Earnings at Risk ( EAR ) and Economic Value of Equity at Risk ( EVEAR ) as measures of the dollar amount of potential loss to net interest income and common shareholders' equity as a result of unforeseen interest rate changes. These two interest rate risk management metrics are now recognized benchmarks for measuring interest rate risk exposure, and its potential impact on a bank's financial position. At the explicit request of regulators, financial analysts and competitive pressures, more commercial banks are now reporting EAR and EVEAR numbers in their annual financial reports. To examine preliminary evidence on the information content of such public disclosures, we composed a sample of some of North America's largest commercial banks. The Canadian peer group is based on Canada's seven largest banks, and the U.S. peer group is composed of twelve of its largest banks. In particular, we investigate if "ex ante" EAR and EVEAR numbers help regulators, financial analysts and investors to explain the subsequent variability of commercial banks' net interest income and net income 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 | 0.010 | 0.147 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".