The Post-Earnings Announcement Drift. Evidence from the Finnish Stock Market
Bibliographic record
Abstract
According to the semi-strong form of market efficiency all publicly available information should immediately be reflected in the stock prices as soon as it is available. This study aimed to determine whether this is true in the case of earn-ings announcements in Finland. The sample consisted of 30 Finnish firms active in the industrial sector. The sample period reached from the first quarter of 2004 to the third quarter of 2009. Their quarterly earnings announcement eps were collected and compared with the consensus median analyst eps estimate ob-tained from the I/B/E/S. The estimation window for the market model was 110 days before the earnings announcement and the abnormal returns were studied in four different event windows [i.e. (0,0), (-3,1), (1,5) and (1,10)]. The results in the first window indicate that a strong reaction in the same direction as the earnings surprise is apparent for both positive and negative earnings surprises. In the second window test statistics imply that there is a positive reaction asso-ciated with a positive earnings surprise, but in case of negative earnings sur-prises the test statistics were not unanimous. In the third window a statistically significant negative reaction was associated with a negative earnings surprise. In the fourth window no statistically significant results were obtained.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".