Trading off managerial and investor uncertainty in firm disclosure: Evidence from <scp>R&D</scp> investments and management guidance
Bibliographic record
Abstract
Abstract Classic disclosure theory suggests that investor uncertainty increases the probability of discretionary disclosure, while managerial uncertainty decreases this disclosure. Because R&D projects are inherently risky, R&D‐intensive firms face high managerial uncertainty as well as high investor uncertainty. This paper empirically examines how R&D intensity impacts the provision, horizon, and content of management earnings guidance. To address endogeneity concerns, state‐level R&D tax credits serve as an instrumental variable for R&D intensity. I find that high R&D firms do not provide less earnings guidance than low R&D firms. However, they issue more quarterly guidance but less annual guidance. This substitution strengthens when there is high managerial uncertainty about the success of R&D projects. Consistent with litigation risk leading to asymmetric disclosure incentives, the decrease in annual earnings guidance is concentrated in positive guidance. Overall, the results imply that firms modify the horizon and content of their earnings guidance by substituting long‐term positive guidance with short‐term guidance when managerial uncertainty discourages the issuance of the former.
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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 | 0.005 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".