The Impact of Russia-Ukraine Invasion on Oil and Gas Stocks in 7 Countries by Using Event Study Approach
Bibliographic record
Abstract
The purpose of this research is to analyze the effect of the announcement of Russia-Ukraine invasion on the market reaction as reflected in abnormal returns and trading volume activity in 7 countries, namely Saudi Arabia, USA, Canada, UAE, Nigeria, Kuwait, and Norway. This research uses secondary data and there are 29 firms of oil and gas listed in each state as a total sample for this research. This research employs the event study method. The event window is 5 days before and 5 days after the occurrence, and the estimating period is 100 days. Normality test analysis and statistical hypothesis testing were carried out in this study. The results showed that there is no significant difference in abnormal returns but there was a significant difference in trading volume before and after the announcement of the event. This result is supported by the results of the analysis based on countries that are not members of OPEC, countries with semi-strong form of efficient market, and countries with weak form of efficient market as comparisons.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".