Information Overload and Disclosure Smoothing
Bibliographic record
Abstract
This paper examines whether managers can reduce the detrimental effects of information overload by spreading out, or temporally smoothing, disclosures. In our initial set of analyses, we attempt to identify managerial smoothing behavior. We find that when there are multiple disclosures for the same event date, managers, on average, spread the disclosures out over several days. We also find that managers are more likely to delay a disclosure (from its event date) when there has been a previous disclosure made within the three days before the event date. Finally, we show that managers are more likely to engage in disclosure smoothing when disclosures are longer, when the information environment is more robust, when firm information is complex, when uncertainty is high, and when disclosure news is more positive. In our second set of analyses, we examine whether there are market benefits to disclosure smoothing. Using two different measures of disclosure smoothing, we find that smoothing is associated with increased liquidity, reduced stock price volatility and increased analyst forecast accuracy. Finally, in additional analyses, we show that managers are less likely to engage in smoothing when they have negative news; they also release good news more quickly after bad news. Combined, our results suggest managers smooth disclosures and the smoothing is associated with several beneficial market outcomes.
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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 | 0.006 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 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".