Winning is not enough: Changing landscapes of earnings surprises and the market reaction
Bibliographic record
Abstract
Abstract We document strikingly opposite time‐series patterns of analyst forecast errors (FEs) and associated market reactions, illustrating that analyst forecasts have become a less useful benchmark of the market's earnings expectations in recent years. The mean FE has increased from negative one to two cents in the 1990s to positive one to two cents in the 2010s, whereas average earnings announcement returns have declined from 0.30% in the 1990s to −0.30% in the 2010s, turning negative in the past 17 years. Underlying the time‐series pattern of increasing FEs is a secular trend where firms move away from just meeting or beating, to which the market reaction has become increasingly negative, toward a large beat, while the frequency of meeting or beating the consensus analyst forecast remains stable during the same period. We develop a parsimonious predictive model of earnings surprises based on peer and past analysts' FEs and find that our predicted FE closely mirrors reported FE, with the average value hovering around one to two cents in most years of the past two decades. The market reaction to “around zero” unexpected FE (FE minus predicted FE) is indistinguishable from zero over time, suggesting that our model serves as a good benchmark of the market's expectation. Our evidence has broad implications for appropriate earnings benchmarking, for the disappearing discontinuity of the earnings surprise distribution around zero, for earnings management to beat analysts' forecasts, for empirical designs when examining the earnings‐return relation, and for the disappearing earnings announcement premium.
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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 | 0.002 | 0.027 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".