On the informativeness of unexpected exclusions from street earnings
Bibliographic record
Abstract
Abstract Exclusions from street earnings can include both expected exclusions, forecasted ex ante by analysts, and unexpected exclusions, revealed after earnings are reported. While prior research largely examines total exclusions from street earnings, unexpected exclusions reflect the news or surprise in exclusions. We investigate the properties and informativeness of unexpected exclusions for future profitability, benchmark beating, analyst forecast errors, and future stock returns. We find that unexpected exclusions represent a mix of transitory and recurring items and are informative about future street earnings. In an analysis of hand‐collected analysts' reports, we find that unexpected exclusions are more likely to reflect misestimated recurring items when analysts forecasted exclusions, and unexpected transitory items when analysts did not forecast exclusions. We also examine benchmark‐beating behavior, in which street earnings meet street forecasts but GAAP earnings miss GAAP forecasts. We observe that benchmark beating is more likely to occur when analysts forecast exclusions than when they do not. Moreover, we find unexpected exclusions are more persistent when street earnings meet street forecasts but GAAP earnings miss GAAP forecasts. These findings are consistent with recurring earnings amounts being opportunistically shifted to excluded items to meet analysts' street forecasts. Finally, we find some evidence that analysts and investors react to, but do not fully incorporate, the information in unexpected exclusions, based on forecast revisions and stock price reactions.
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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 | 0.007 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".