Avvikelseavkastning i samband med resultatöverraskningar på OMXS30 - En eventstudie
Bibliographic record
Abstract
This thesis investigates if abnormal returns exist in connection to the release of quarterly reports depending on if the presented results overperform, underperform or are in line with the analysts expectations. An event study method is applied where the market model is used. The study is performed on the companies listed on the OMXS30 index on their last eight quarterly reports, from the first quarter 2021 to the fourth quarter 2022. An event window of three days and an estimation window of 40 days are applied. Two different event studies are performed, one with the 27 of the 30 stocks on OMXS30 where all the information is available and one where one more company is excluded due to distinctly deviant results. The first study shows significantly negative abnormal returns for the quarterly reports that are in line with analyst expectations, while no significant results were found for the quarterly reports over- or underperforming. The second study shows, in addition to negative abnormal returns for the quarterly reports in line with analyst expectations, significantly positive abnormal returns for companies exceeding analyst consensus, and significantly negative abnormal returns for companies failing to meet the analyst expectations.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.012 |
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".