Trading Behavior Prior to Public Release of Analyst Reports: Evidence from Korea
Bibliographic record
Abstract
Abstract This paper investigates information leakage from analyst reports prior to their public release. Previous studies document abnormal trading by institutions or short selling before announcement of recommendation changes. Such prerelease abnormal trading is interpreted as evidence of information leakage from analyst reports. However, if sophisticated investors obtain information similar to what analysts have from other sources, abnormal prerelease trading patterns would be observed even if there were no information leakage from analyst reports. This paper, using a unique data set from Korea, aims to determine whether a direct causal link between recommendation changes and prerelease trading exists, by comparing trading behavior of client investors with non‐client investors. We find that abnormal prerelease trading by client investors, especially client institutions, is earlier in timing and greater in magnitude than that of other investor groups, supporting the information leakage hypothesis. We further find that net buying by client institutions and client large individuals is positively associated with firm, analyst, and earnings forecast change variables that influence formulation of recommendation changes and their impacts.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".