The Monitoring Effect of More Frequent Disclosure
Bibliographic record
Abstract
ABSTRACT This paper examines the monitoring effect of disclosure frequency from a shareholder perspective. For our analyses, we use a setting in the European Union in which reporting frequency requirements differed across and within countries before being harmonized by a directive requiring the implementation of quarterly disclosure. We investigate how both cross‐sectional differences in reporting frequency and their harmonization affect shareholders' ability to monitor managers. To gauge monitoring effects, we use shareholders' valuation of cash assets. We find that semi‐annual reporters exhibit lower cash valuation than quarterly reporters. Using a difference‐in‐differences approach, we show that these differences recede after semi‐annual reporters implement a higher reporting frequency. Our results are consistent with the notion that more frequent disclosure reduces expected agency costs by providing shareholders with the opportunity for timelier monitoring to constrain managers from expropriating corporate resources. In additional analyses, we find that this monitoring effect is robust to using alternative measures of the change in cash and agency costs as well as alternative benchmark groups. Further, we find stronger effects when corporate governance or earnings quality is low.
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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 | 0.007 | 0.075 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".