A multi-pronged analysis of common and market-specific equity outliers across the G7, China, and global markets using country ETFs
Bibliographic record
Abstract
• The study analyzes equity outliers across G7, China, and global markets. • Tukey fences, ARMA, and Wavelet analysis are utilized to detect return anomalies. • Outliers are most prevalent during the GFC and post-COVID-19. • The GFC produces more outliers in the U.S. and Canada. • The post-COVID-19 period highlights more outliers in China and France. This study analyzes unexpected upside and downside outlier movements in equity returns across G7 countries, China, and the world from July 2008 to September 2024. Using methods like Tukey fences, ARMA, and Wavelet analysis, it identifies the frequency, characteristics, and origins (common or country-specific) of these movements. By controlling for global co-movements, country-specific outliers are isolated, revealing distinct patterns for each market. Outliers were most prevalent during the Global Financial Crisis (2008–2011) and the post-COVID-19 period (2020–2022), with significant variations across countries. The results enhance understanding of market disruptions, offering insights for risk management and investment strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".