The Effect of Regulation FD on Transient Institutional Investors' Trading Behavior
Bibliographic record
Abstract
ABSTRACT We assess the impact of Regulation Fair Disclosure (Reg FD) on the trading behavior of transient institutional investors in the quarter prior to a bad news break in a string of consecutive earnings increases. Bad news breaks are defined as breaks that are by growth firms, preceded by longer strings of consecutive earnings increases, followed by longer strings of consecutive earnings decreases, and associated with larger declines in earnings. Pre–Reg FD transient institutions have abnormal selling of stocks in the quarter immediately preceding a bad news break. This abnormal selling is confined to firms that hold conference calls in the pre–Reg FD period. However, in the post–Reg FD period transient institutions do not exhibit similar abnormal selling of stocks in the quarter before a bad news break. Furthermore, after Reg FD transient institutions allocate less of their stock portfolios to conference call firms relative to non–conference call firms in the quarters prior to a bad news break. These results demonstrate that Reg FD has had an impact on management's selective disclosure behavior and significantly changed the trading behavior of transient institutions.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".