Analyst Coverage and the Likelihood of Meeting or Beating Analyst Earnings Forecasts
Bibliographic record
Abstract
Abstract This paper examines the relation between analyst coverage and whether firms meet or beat analyst earnings forecasts. We distinguish between whether a firm's reported quarterly earnings meet (i.e., equal or exceed by one cent) or beat (i.e., exceed by more than one cent) its consensus analyst earnings forecasts. We find a positive relation between analyst coverage and whether a firm meets or beats analyst forecasts. However, the more pronounced relation is that between analyst coverage and meeting analyst forecasts. Also, when we consider exogenous shocks to analyst coverage due to brokerage mergers or closures and conglomerate spinoffs, we continue to find a robust positive relation only between analyst coverage and meeting analyst forecasts. To shed light on the causal relation involved, we examine and find that greater analyst coverage is associated with a significantly larger market reaction to negative earnings surprises. We also document that firms with greater analyst coverage are more likely to guide analyst earnings forecasts downwards. Taken together, our evidence suggests that greater analyst coverage raises the pressure on managers to meet analyst earnings forecasts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".