Private Information Production, Public Disclosure, and the Cost of Capital: Theory and Implications*
Bibliographic record
Abstract
Abstract Both private information production by market traders and public disclosure by firms contribute to dissemination of financial information in the capital market. However, the motives and economic consequences of the two are quite different. In general, private information production is intended by investors to increase their trading profit, which has the effect of widening the information gap between informed and uninformed investors and increasing the firm's cost of capital. On the other hand, public disclosure can be used to narrow this information gap and to lower the cost of capital. This paper provides a theoretical model to examine the economic incentives behind these two forms of information dissemination and their consequences on the cost of capital. By simultaneously considering the firm's and the information traders' decisions, the paper derives an equilibrium in which the amount of private information production, the level of public disclosure, and the cost of capital are all linked to specific characteristics of the firm, of information traders, and of the market. In contrast to conventional beliefs, the paper predicts that, across firms, the cost of capital can be either positively or negatively related to the firm's disclosure level, depending on the specific factors that cause the variation within a particular sample. Similarly, the extent to which investors follow a firm and the firm's disclosure level can be either positively or negatively related to each other. Implications for empirical research are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".