The Russia–Ukraine conflict and foreign stocks on the US market
Bibliographic record
Abstract
Purpose The authors investigate how market quality diverges between foreign firms and domestic firms on the US stock market in response to the Russia–Ukraine conflict. Design/methodology/approach With an event study approach, the authors compare foreign firms with domestic firms in their market responses over the three-day window around the outbreak of the war. Further, with Difference-in-Difference (DID) analyses, the authors study the change in foreign firms' market quality upon this outbreak in comparison with their domestic counterparts. Finally, the authors compare the foreign firms across firm specific characteristics and home country characteristics. Findings The authors find that foreign stocks listed in the US experience more severe market quality deterioration compared to the stocks' domestic counterparts. This effect is especially strong for companies from countries considered friendlier towards Russia and companies that are not cross-listed. The authors' findings are consistent with the information asymmetry hypothesis concerning market quality. Moreover, US market investors have more concerns over political risks with non-US-aligned political standings during war times. Research limitations/implications The authors' findings are consistent with the information asymmetry hypothesis concerning market quality. Moreover, US market investors have more concerns over political risks over non-US-aligned political standings during war time. Practical implications Since both countries in the conflict are in Europe, the US stock market, to a certain degree, becomes a safe haven for capital from Europe and other countries. In the meantime, American Depository Receipts (ADRs) have been important for US investors to create a globally diversified portfolio, and the knowledge regarding ADRs' vulnerability to international geopolitical events is valuable. The author' results are informative for stock market investors to understand the market dynamics for international and domestic companies during this extremely uncertain time. Originality/value This is the first study that examines the market quality divergence between foreign firms and domestic firms on the US stock market in response to the Russia–Ukraine conflict. The authors provide novel evidence on the change in ADRs' market quality associated with significant political uncertainty. The authors show that ADRs' market quality is more vulnerable to international geopolitical risks relative to otherwise comparable domestic firms.
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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 | 0.001 | 0.004 |
| 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) | 0.002 | 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".