Bibliographic record
Abstract
Abstract Firms with multiple pieces of information can disclose the information concurrently (bundled disclosure) or sequentially (unbundled disclosure). This paper examines the pricing implications of (un)bundled disclosure in a rational expectations equilibrium model. The model considers a firm whose liquidating cash flow consists of two components. Some investors possess private information about one component (e.g., earnings) and all investors are uninformed about the other component (e.g., unexpected events). We analyze three disclosure policies. In bundled disclosure, both cash flow components are disclosed concurrently; in unbundled disclosure, the informed cash flow component is disclosed early, and the uninformed component is disclosed later; and in alternative unbundled disclosure, the uninformed component is disclosed early, and the informed component later. We find that the disclosure policy influences the cost of capital prior to any disclosure through a risk allocation effect and a price informativeness effect. Unbundled disclosure results in a lower cost of capital compared to bundled disclosure, as it improves risk allocation and enhances the informativeness of the stock price prior to any disclosure. However, for alternative unbundled disclosure, the cost of capital can be higher or lower than that of the other disclosure policies. This is because late disclosure of the informed cash flow component alters investors' risk exposure to the noisy supply of shares, thereby influencing the risk allocation and price informativeness effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".