The Effect of Oil and Gas Producers' FRR No. 48 Disclosures on Investors' Risk Assessments
Bibliographic record
Abstract
We hypothesize that oil and gas producers' sensitivity and value-at-risk (VAR) disclosures, mandated by SEC Financial Reporting Release Number 48 (FRR No. 48), convey useful information to investors about commodity betas (defined as the sensitivity of firms' equity price changes to commodity price changes). Consistent with the hypothesis, we find that first-time sensitivity and VAR disclosers experience greater commodity beta shifts at 10-K filing dates than do nondisclosing firms in a matched control sample. To enhance confidence that the observed shifts are associated with FRR No. 48 disclosures, we repeat the analyses in the year before the release was effective, when firms did not disclose sensitivity or VAR. At the prior year 10-K filing dates, we find that firms in the disclosure sample do not exhibit significant commodity beta shifts. We conclude that the results are consistent with FRR No. 48-mandated sensitivity and VAR disclosures providing useful information to investors.
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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 | 0.007 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".