Why Do Large Firms' Prices Anticipate Earnings Earlier than Small Firms' Prices?*
Bibliographic record
Abstract
Abstract This paper presents evidence that the positive association between firm size and price leads of earnings is not solely a function of private search incentives for firm‐specific information. Specifically, we find that small‐firm prices also lag large‐firm prices with respect to industry‐wide information. Our empirical analysis extends Collins, Kothari, and Rayburn 1987 and Freeman 1987, who document that security‐price leads of earnings are positively associated with market capitalization. In particular, we examine the association between firm size and the timing of security returns for two components of annual earnings changes: the average change for a firm's industry and the firm's idiosyncratic change. We find that large firms' prices have a longer lead than small firms' prices with respect to both components. Large firms' early lead on industry‐wide earnings suggests that returns of large firms predict returns of same‐industry small firms. To test this implication, we construct a portfolio of long (short) positions in small firms when the prior month's returns of large firms in their industry are above (below) average for large firms in other industries. This zero investment portfolio earns 4.5 percent over 12 months.
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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 | 0.001 | 0.019 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".