Market reactions to the US-Houthi conflict: an event study of the US stock market
Bibliographic record
Abstract
Purpose This study aims to examine the market response to the US-Houthi conflict in the US stock market, focusing on sectoral differences, company size and growth rates. Design/methodology/approach Using daily closing prices of 1,832 companies listed on major US stock indexes from December 1, 2022, to February 29, 2024, this study applies the event study methodology to assess market reactions. Multiple event windows, including 15-day pre- and post-event periods, are analyzed to capture comprehensive market responses. January 11, 2024, is designated as the event date, marking the declaration of war between the US and the Houthis, with a 250-trading-day estimation window used for benchmarking expected returns. Findings The findings indicate that the US-Houthi conflict significantly impacted the market, with defensive sectors such as healthcare and utilities responding positively, while sectors like energy and financials showed negative reactions. Smaller companies exhibited greater volatility, with a pattern of positive reactions before the event, negative responses during, and a recovery afterward. In contrast, large companies showed consistent positive reactions. Market reactions also varied by growth rates, with low- and medium-growth companies experiencing volatility and recovery, while high-growth companies, particularly in the energy sector, demonstrated resilience. These results highlight the differential impacts of geopolitical events based on sector, company size, and growth potential. Originality/value This study is the first to examine the impact of the US-Houthi conflict on the US stock market. It provides novel insights into how sectoral differences, company size and growth rates influence market reactions to geopolitical events.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".