"Buy low, sell high, that's my motto." - An event study examining the Post Earnings Announcement Drift on the Swedish market
Bibliographic record
Abstract
The post earnings announcement drift (PEAD) is a well studied market anomaly. However, few academic papers have focused their attention on Swedish market. Therefore, this paper evaluates if the PEAD can be observed on the Swedish market between the first quarter of 2006 and the last quarter of 2019. Using a sample of 121 firms consisting of both small and large sized firms, this paper found statistically significant drift for firms that had quarterly reports above and below market expectation. Although, the strongest drift was observed in firms that had reports below market expectations. In line with past research, the result also points towards that small sized firms are the main driver behind the drift. On the other hand, the practical implications of the results are limited. In contrast to past papers, there exists a large inherent risk in taking a position that would exploit the effect. Further, there is a large likelihood that transaction cost would eat up any profit generated from the strategy. Thus, creating a trading strategy based on the results will probably not be profitable.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".