The Importance of Director External Social Networks to Stock Price Crash Risk*
Bibliographic record
Abstract
ABSTRACT Prior research documents that information transmitted via director networks affects firms' policies and real economic activities. Given a manager's potential monopoly over firm information, it is important to analyze whether information transmission through director social networks undermines the manager's control. Specifically, we explore whether information flow through director networks influences managers' ability to hoard bad news. We predict and find that the extent of external connections of the board of directors is negatively associated with future stock price crash risk. Additional analysis implies that this evidence is driven by firms with more powerful executives, with weaker auditor monitoring, or subject to strong investor protection, and by directors with greater monitoring incentives or responsibilities and directors with less firm‐specific knowledge. Collectively, our research lends empirical support for the monitoring view under which better‐informed directors narrow the scope for bad news hoarding evident in stock price crash risk. In another series of tests, we fail to find evidence consistent with the information leakage view under which directors pass sensitive firm‐specific information to connections that trade on the information before its public release. Other analysis helps dispel the concern that the endogenous match between directors and companies is spuriously responsible for our core results. Our empirical findings have important implications on how social networks affect the proper functioning of capital markets.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".