The Changing Behavior of Trading Volume Reactions to Earnings Announcements: Evidence of the Increasing Use of Accounting Earnings News by Investors
Bibliographic record
Abstract
ABSTRACT The increase in investor diversity over the last 35–40 years prompted us to revisit trading volume reactions to earnings announcements and how these reactions vary with firm size. We argue that this increase in investor diversity would likely increase differences in the precision of pre‐announcement information around earnings announcements, particularly for large firms. This suggests that the role of earnings announcements in resolving investor disagreement, as reflected in trading volume reactions, has increased. Over the 35‐year period 1977–2011, we find a dramatic increase in the magnitude and frequency of volume reactions to earnings announcements, particularly for large firms. The increase in large firms’ trading volume reactions is so pronounced that the relation between volume reactions and firm size has turned positive in recent years, reversing Bamber's ( , ) previously documented negative relation. We provide intuition and empirical evidence that our results are attributable to the resolution of differential prior precision among increasingly diverse investors following large firms.
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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 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".