The Effect of Meeting or Beating Revenue Forecasts on the Association between Quarterly Returns and Earnings Forecast Errors*
Bibliographic record
Abstract
Recent studies in the accounting literature provide evidence of a market premium whenever firms meet or exceed analysts' earnings forecasts. Financial analysts typically issue revenue forecasts in addition to earnings forecasts. In this study, we draw our motivation from the cue consistency theory to examine whether meeting or exceeding revenue forecasts serves as an additional cue to the market in pricing earnings performance. Consistent with this theory, we show that the market premium (penalty) to meeting or beating (not meeting) earnings forecasts is accentuated when revenue forecasts are also met (not met). Meeting earnings forecasts but not meeting revenue forecasts generally results in a significantly negative market penalty, and the magnitude of the earnings response coefficient jointly depends on whether the earnings and revenue forecasts are met or not. Finally, consistent with previous research, we document a significant association between revenue forecast errors and quarterly abnormal returns. However, we show that after allowing for differential market reactions depending on whether earnings and revenue forecasts are met, this association becomes insignificant. This result suggests that the value of meeting revenue forecasts is arguably of greater importance to market participants than the magnitude of the revenue forecast error.
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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 | 0.008 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".