Bibliographic record
Abstract
This study tests market efficiency theory by examining the effect of the Brexit announcement on the risk adjusted stock price returns of a sample of firms with strong economic ties to the EU using the standard event study methodology in the finance literature. How did the market react to UK leaving the European Union? We hypothesize that the June 24, 2016 Brexit announcement would negatively affect the risk adjusted returns of the sample of firms analyzed. The purpose of this study is to test market efficiency by analyzing the effect of Brexit’s announcement on the market. Specifically, we analyze the semi-strong market efficiency hypothesis predicting that stock price returns react so fast to all public information that no investor can earn an above normal return by acting on such news. Consistent with market efficiency and behavioral finance theories, we observe an overreaction to the bad news up to 14 days following the announcement followed by a return to equilibrium. Overall, results support semi-strong form market efficiency for the Brexit announcement. Implications of this study suggest that efforts to determine the “right” value of stock are useless since the market is semi-strong form efficient. The “right” price is themarket price that instantly impounds all available and relevant information.
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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 | 0.009 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".