Firm Risk and Disclosures about Dispersion of Asset Values: Evidence from Oil and Gas Reserves
Bibliographic record
Abstract
ABSTRACT The question we address is whether mandated disclosure about dispersion of nonfinancial asset values can provide information relevant to assessing firm risk. Using a sample of Canadian oil and gas (O&G) firms between 2004 and 2011, we find that the difference between the disclosed 10th and 50th percentiles from the O&G reserves distribution, which measures dispersion of the distribution, is positively associated with future total and idiosyncratic equity return volatility, systematic risk, and credit risk. We also find that disclosure of increased reserves dispersion is associated with weaker stock price reactions to increases in reserves and with increases in bid-ask spreads, both of which indicate the disclosures convey information about risk associated with reserves. Additional tests reveal little evidence of managerial opportunism in the reserves disclosures. Taken together, our evidence suggests that quantitative disclosures about the dispersion of nonfinancial asset values can provide information relevant to assessing firm risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".