Earnings Non‐Synchronicity and Voluntary Disclosure
Bibliographic record
Abstract
Earnings non‐synchronicity reflects the extent to which firm‐specific factors determine a firm's earnings. Prior research suggests that high earnings non‐synchronicity impedes corporate outsiders' ability to process information. This study examines the impact of earnings non‐synchronicity on managers' decisions to provide earnings forecasts. We propose that high earnings non‐synchronicity motivates managers to issue earnings forecasts to reduce information asymmetry between managers and investors and to preempt costly information acquisition by outsiders. Consistently, we find a positive relation between earnings non‐synchronicity and managers' propensity to issue earnings forecasts, particularly long‐horizon forecasts. This positive relation is weaker when earnings are easier to predict based on the firm's earnings history and is stronger when the firm has higher institutional ownership and greater analyst following. We also find that the market's reaction to management forecasts increases with earnings non‐synchronicity. Overall, the evidence suggests that managers voluntarily provide earnings forecasts to alleviate the adverse consequences of earnings non‐synchronicity. These findings provide a more complete picture about the impact of earnings non‐synchronicity on a firm's information environment, and highlight the effect of the nature of information asymmetry on voluntary disclosures.
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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 | 0.003 | 0.035 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".