Market-Wide Effects of Off-Balance Sheet Disclosures:
Bibliographic record
Abstract
This paper studies the spillover effects of firms' off-balance sheet disclosures. We focus our analysis on the mandatory disclosure of oil and gas (O&G) reserves, a setting in which off-balance sheet information is particularly important to understand industry competition. Using a comprehensive sample of Canadian and US O&G producers we document two novel results. First, in contrast to prior research on the informational effect of peers' earnings announcements, we find evidence that firms' exhibit lower stock returns when their peers announce more positive news about O&G reserves. Second, consistent with peers' disclosures affecting managerial decision making, we document that larger increases in peers' reserves are accompanied by an increase in firms' investment. We corroborate our results by exploiting three sources of institutional variation. First, the North-American pipeline infrastructure constrains the supply of natural gas, and thus competition in the gas market, but not the supply of oil. Second, the introduction of the fracking technology substantially altered the competition dynamics in the natural gas market. Third, mandatory O&G disclosure rules were modified in Canada and the US in a similar fashion, albeit at different points in time. Overall, our evidence suggests that off-balance sheet disclosures have substantial market-wide effects in the form of both financial and real externalities.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".