Are There Externalities of Private Firm News Disclosure? Evidence from Public Firms’ Investment
Bibliographic record
Abstract
ABSTRACT This study examines whether and how voluntary news disclosure made by private firms affects investment sensitivities of public peer firms. Analyzing data from U.S. public firms from 1996 to 2018, we discover that public firms’ investment sensitivities intensify in industries with active private firm disclosures; a one standard deviation increase in private firm news disclosure raises public firms’ investment sensitivities by 14.5–17.6 percent. To mitigate endogeneity, we employ instrumental-variable methods, leveraging the staggered implementation of prudent investor rules and enforceability of noncompete agreements. Our results show that these effects are magnified in industries marked by the higher expected industry return volatility and less local newspaper coverage. We find that news from private firms significantly enhances public firms’ investment sensitivities, regardless of its sentiment. This research highlights the crucial role of private firm disclosures in influencing public firms’ investment decisions, enhancing our understanding of information spillovers in corporate disclosure. JEL Classifications: D80; G31; G32; M41.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".