Investor Reaction to Celebrity Analysts: The Case of Earnings Forecast Revisions
Bibliographic record
Abstract
ABSTRACT We examine the effects of analysts' celebrity on investor reaction to earnings forecast revisions. We measure celebrity as the quantity of media coverage analysts receive in sources included in the Dow Jones Interactive database, and find that media coverage is positively related to investor reaction to forecast revisions. The effect of celebrity on the reaction to forecast revisions remains significant after controlling for forecast performance variables examined in prior studies (ex post forecast accuracy, ex ante accuracy, award status, and other variables shown to be related to forecast accuracy). While these results are consistent with the familiarity of the analyst's name affecting the market reaction, we cannot rule out that our measure of celebrity is correlated with error in the performance measures we examine and/or correlated with other unexamined dimensions of forecast performance. A content analysis of a random subsample of the media coverage of our sample analysts suggests that our findings likely are not due to the increased availability of forecast revisions. Finally, an investigation of the excess returns around the quarterly earnings announcement date suggests that market participants react too strongly to forecast revisions issued by analysts with high levels of media coverage. Taken together, these findings suggest that an analyst's level of media coverage can affect the initial market reaction to his forecast revisions.
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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 | 0.006 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".