To Guide or Not to Guide? Causes and Consequences of Stopping Quarterly Earnings Guidance
Bibliographic record
Abstract
In recent years, quarterly earnings guidance has been harshly criticized for inducing “managerial short‐termism” and other ills. Managers are, therefore, urged by influential institutions to cease guidance. We examine empirically the causes of such guidance cessation and find that poor operating performance — decreased earnings, missing analyst forecasts, and lower anticipated profitability — is the major reason firms stop quarterly guidance. After guidance cessation, we do not find an appreciable increase in long‐term investment once managers free themselves from investors’ myopia. Contrary to the claim that firms would provide more alternative, forward‐looking disclosures in lieu of the guidance, we find that such disclosures are curtailed. We also find a deterioration in the information environment of guidance stoppers in the form of increased analyst forecast errors and forecast dispersion and a decrease in analyst coverage. Taken together, our evidence indicates that guidance stoppers are primarily troubled firms and stopping guidance does not benefit either the stoppers or their investors.
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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 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".