The Effect of Legal Environment on Voluntary Disclosure: Evidence from Management Earnings Forecasts Issued in U.S. and Canadian Markets
Bibliographic record
Abstract
Citing fear of legal liability as a partial explanation, prior research documents (1) managers' reluctance to voluntarily disclose management earnings forecasts, and (2) greater forecast disclosure frequencies in periods of bad news. We provide evidence on how management earnings forecast disclosure differs between the United States (U.S.) and Canada, two otherwise similar business environments with different legal regimes. Canadian securities laws and judicial interpretations create a far less litigious environment than exists in the U.S. We find a greater frequency of management earnings forecast disclosure in Canada relative to the U.S. Further, although U.S. managers are relatively more likely to issue forecasts during interim periods in which earnings decrease, Canadian managers do not exhibit that tendency. Instead, Canadian managers issue more forecasts when earnings are increasing, and their forecasts are of annual rather than interim earnings. Also consistent with a less litigious environment, Canadian managers issue more precise and longer-term forecasts. These findings hold after controlling for other determinants of management earnings forecast disclosure that might differ between the two countries—firm size, earnings volatility, information asymmetry, growth, capitalization rates, and membership in high-technology and regulated industries.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".