Managers' EPS Forecasts: Nickeling and Diming the Market?
Bibliographic record
Abstract
ABSTRACT: Nearly half of managers' forecasts of annual earnings per share (EPS) end in nickel intervals, whereas only about 20 percent of actual EPS end in nickel intervals. We provide evidence on the attributes, determinants, and consequences of this systematic wedge between managers' predictions and firms' ex post actual performance. Managers' nickel forecasts are not simply a benign response to uncertainty about upcoming earnings, because nickel forecasts are not only less accurate, but also they are more optimistically biased than non-nickel forecasts. In addition to uncertainty, efforts to protect the firm's proprietary information and self-serving opportunism in response to managers' economic incentives also play incremental roles in explaining managers' propensity to issue forecasts heaped at nickel intervals. We also find that managers' nickel forecasts spur even active analysts to issue forecasts heaped at nickel intervals, although analysts' forecast revisions partially adjust for the optimism and noise in managers' nickel forecasts.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".