Event Study: The Effect of the Passing Away of Queen Elizabeth II on the UK Stock Market
Bibliographic record
Abstract
This study assesses the heterogeneous impact of the death of Queen Elizabeth II on industry leaders in the United Kingdom. It reveals the firm-specific characteristics that lead to this heterogeneity. This study applies an event study methodology to a sample of 20 leading UK companies across a 250-day estimation window and a 40-day event window. According to the event study, the death of Queen Elizabeth II has had a differential impact on most UK companies for different industries. In contrast, companies in the Biotech, Oil & Gas, Mining, and Tobacco industries were less affected. However, companies in the Retail, Telecommunications, Banks, and Defense Contractors sectors show significant negative CARs. This article has implications for investors in identifying company sector-specific characteristics that drive premium returns and guiding diversification by improving the sectoral diversity of portfolios. However, the study is limited by a relatively small sample size. There is a gap in earlier research on the related change of kingship and stock market performance, and this study makes two contributions to the literature. The authors first analyze the impact of the death of Queen Elizabeth II on British firms. Secondly, the study also provides guidance on how investors can diversify across different sectors in the UK to reduce risk.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".