Firms with Inconsistently Signed Earnings Surprises: Do Potential Investors Use a Counting Heuristic?
Bibliographic record
Abstract
Abstract Although prior research reports that firms that consistently beat their earnings expectations are rewarded with a market‐valuation premium, most firms are inconsistent in the sign of their benchmark performance, sometimes missing and sometimes beating. In this paper, we report the results of multiple experiments to test the idea that potential investors, evaluating firms that have inconsistent benchmark performance, use a counting heuristic to discriminate among them. Our results provide strong support for the hypothesis that these investors distinguish among firms by counting the number of beats and misses they experience over an observed time interval. The judgmental effect of this beat‐frequency is incremental to the effect of the magnitude of the beats and misses of the benchmark. Our study has implications for firm managers who have inconsistent benchmark performance, suggesting that market participants do make systematic discriminations among such inconsistent firms. It also has implications for researchers by introducing a new theoretical construct to the literature—namely, the counting heuristic.
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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 | 0.014 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".