The Effects of Russia's 2022 Invasion of Ukraine on Global Markets
Bibliographic record
Abstract
This chapter aims to test the efficient market hypothesis, in its weak form, in the capital markets of Germany (DAX), USA (Dow Jones), France (CAC 40), UK (FTSE 100), Italy (FTSE MIB), Russia (MOEX), Japan (NIKKEI 225), Canada (S&P TSX), China (Shanghai and Shenzhen), as well as the exchange rates Rouble/Canadian, Rouble/Euro, Rouble/Swiss, Rouble/UK, Rouble/US, over the period from January 2, 2017 to May 6, 2022. The time series do not exhibit normal distributions and are stationary in first differences. To answer the research question, the authors use the detrended fluctuation analysis (DFA) method, which allows evidence of an increase in DFA exponents. Capital markets and exchange rates, for the most part, moved from equilibrium to persistent, while Russia's market in the tranquil period shows signs of equilibrium and moves to anti-persistent in the crisis period.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".