Bibliographic record
Abstract
ABSTRACT Management earnings forecasts expressed as a range have become the most common form of quantitative management guidance. Traditionally, the proxy for the sign and the magnitude of the information conveyed by these forecasts—the forecast news—is calculated as the difference between a pre‐forecast earnings expectation and the midpoint of the forecasted range. We provide strong evidence that this traditional measure understates the amount of information conveyed by range forecasts. More importantly, we demonstrate that information conveyed by the upper and lower bounds of the forecasts can be used to improve the classification of forecasts as conveying good or bad news and for calculating the magnitude of that news. We rely on these findings to suggest alternative methods of classifying management range forecasts as conveying good versus bad news and to refine the calculation of forecast news to include the broader information set. Our analysis also suggests that the information conveyed by the range when the forecasts are bundled (issued concurrent with an earnings announcement) is significantly different than when forecasts are not bundled. Overall, our study documents the importance of incorporating range‐related information when assessing the sign and the magnitude of the information conveyed by management range forecasts.
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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 | 0.005 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".