Diversified Firms and Analyst Earnings Forecasts: The Role of Management Guidance at the Segment Level
Bibliographic record
Abstract
ABSTRACT Using a unique, manually collected dataset, we are the first to analyze the role that management guidance at the segment level plays for the financial analyst earnings forecasts of diversified firms. About half of the diversified European firms in the sample provide segment-level guidance (SLG), with considerable variation in precision and disaggregation. We find that (1) analyst earnings forecast errors are smaller, and (2) the magnitude of disagreement between individual forecasts and the average forecast is lower for firms that provide SLG, beyond the effect of group-level guidance. The results hold in matched samples and within-firm analyses around SLG initiation. We further show that the results are stronger in situations characterized by higher information asymmetry, but not in situations characterized by operational complexity. Overall, the results imply that SLG mitigates, to some extent, the difficult task that financial analysts face when valuing diversified companies.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".