The Market's Assessment of the Probability of Meeting or Beating the Consensus
Bibliographic record
Abstract
Abstract We investigate to what extent the market uses information that is predictive of whether earnings will meet or beat the analyst consensus forecast of earnings (MBE henceforth): measures of a firm's incentives to engage in MBE behavior, measures of constraints on MBE, measures of past MBE practices by firm and industry, and other variables. Using the Mishkin test framework and Bonferroni‐adjusted p‐values, we document that of a total of 21 variables, the market inefficiently uses information in one difficulty measure and four other predictors, suggesting that strong empirically and theoretically grounded relationships concerning MBE behavior are more likely to be unraveled by the market. We further show that a portfolio based on the difference between the objective MBE probability and the market‐assessed MBE probability generates significant abnormal returns. The documented return anomaly is distinct from other known anomalies and cannot be fully explained by arbitrage risk or transaction costs.
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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 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 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".