Commitment and Cost of Equity Capital: An Examination of Timely Balance Sheet Disclosure in Earnings Announcements
Bibliographic record
Abstract
Abstract In this paper, I examine the relation between disclosure commitment and cost of equity capital using accelerated earnings announcement disclosures as a measure of commitment. In settings characterized by imperfect market competition, I find that firms which consistently disclose balance sheet detail in relatively timely earnings announcements have lower costs of capital compared to other firms. This result is statistically significant and economically meaningful, and is robust to various alternative measurements for cost of capital, and alternative designs addressing endogeneity and underlying information quality. Overall, this result is important because it highlights additional dimensions of disclosure commitment (consistency and timeliness), while incorporating important features from theoretical models (information quality and market competition). In particular, my results suggest that consistency and timeliness are salient features of firms' disclosure behavior that have predictable and robust relations with capital market outcomes. This result is robust to controlling for underlying information quality; however, consistent with theory, it is conditional on low levels of market competition.
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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 | 0.004 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".