Strategic disclosure and informed trading with short‐selling constraints
Bibliographic record
Abstract
Abstract Security prices are affected by information strategically disclosed by managers as well as by informed trading of outsiders and vice versa. However, market frictions, such as short‐selling costs and constraints, significantly affect trading in financial markets. In this article, we examine the joint determination of voluntary disclosure, security prices, and short‐selling, and address the following issues: How do major market frictions affect managerial disclosures? How do disclosures influence strategic informed trading in the presence of frictions? What does the interaction of strategic disclosure and informed trading imply for price efficiency? We find that short‐selling (trading) costs have a substantial impact on the equilibrium disclosure policy and its interaction with informed trading and price efficiency. Because of endogenously binding short‐sale constraints, better‐informed traders can either deter or encourage disclosure, thus reconciling mixed available evidence on the relation between short‐sale constraints and managerial disclosure. Furthermore, price efficiency need not improve with managers' information endowment because greater disclosure can endogenously inhibit informed short‐selling in equilibrium. Our analysis also generates novel empirical predictions relevant to the literature on managerial disclosure, shorting, and price efficiency.
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 | 0.004 | 0.031 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".