Variations in the Financial Reporting Environment and Earnings Forecasting
Bibliographic record
Abstract
Abstract This paper examines variations in the financial reporting environment internationally. In particular, I investigate the relation between variations in accounting‐related institutional factors (choice, accrual accounting and enforcement) and the accuracy of analysts' earnings forecasts. Controlling for firm‐ and country‐level factors, I document that the extent of choice among accounting methods is associated with lower forecast accuracy. This finding is consistent with analysts' performance suffering from the increased task complexity (and/or managers using flexibility for purposes other than to provide information). The degree of prescribed accrual accounting is positively correlated with forecast accuracy, consistent with both accruals providing useful information and with the smoothing function of accruals. Enforcement of accounting standards is positively related with forecast accuracy, suggesting that enforcement encourages managers to follow prescribed rules, which, in turn, reduces analysts' uncertainty. Finally, I examine whether the roles of accrual accounting and choice vary with the level of enforcement. Although univariate tests support these interaction hypotheses, multivariate tests do not.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".