Inside the 'Black Box' of Private In-House Meetings: Implications for Fair Disclosure and Insider Trading Regulation
Bibliographic record
Abstract
While corporate private in-house meetings between investors and management are common across the world, there are generally no detailed reporting requirements for these meetings. The Shenzhen Stock Exchange in China is an exception and thus provides a unique opportunity to look inside the ‘black box’ to examine the structure and consequences of private in-house meetings. We develop a unique large-scale hand-collected dataset by accessing over 17,000 private meeting reports over 2012-2014 and use reported meeting details to examine the consequences of private in-house meetings. We find that, on average: (i) the stock market anticipates positive news in these private meetings as there is a significant stock price run-up starting about 30 days before the meeting date, (ii) the market reacts strongly and positively around these meeting dates, and (iii) the market reacts again around the subsequent public disclosure of the meeting notes. Further, we find that company insiders engage in significant trading activities around these meeting dates, selling over $12 billion USD of their shares – almost 62% of the total value of all insider trades for Shenzhen-listed firms in our sample period. Most importantly, it appears that company insiders are able to time their transactions: they tend to sell more shares before negative news disclosures but hold off selling when there is positive news to be disclosed in the meeting. Overall, our results suggest that firms disclose material non-public information during these private meetings, and that at least some meeting participants and company insiders trade on this information before it is publicly available. Finally, it appears that disclosure of private meeting details can be beneficial for market participants who are unable to attend such meetings. We discuss implications of these findings for disclosure requirements in the US.
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 | 0.018 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".