Multilingual X / Twitter Sentiment Analysis of Geopolitical Risk Using Granger Causality Focusing on the Ukraine War and Financial Markets
John Burns et al.
What the paper says
This paper investigates the changes in the financial assets and markets from December 1st, 2021, to April 30th, 2022, during the start of the Ukraine War. These dates roughly correspond to the prelude to the War in December 20211 to a few weeks after Russian troops withdrew from the Kyiv area on April 7th, 20222. We used the Goldstein 19923 Results Table to create Positive and Negative Geopolitical Risk bigrams (Goldstein, 1992, Pg. 5–6). With these bigrams, we collected over 3.6 million tweets during our research period in seven different languages (English, Spanish, French, Portuguese, Arabic, Japanese, and Korean) to capture worldwide reaction to the Ukraine War. Using various sentiment analysis methods, we constructed a time series of the change in the daily Geopolitical Risk sentiment and explored its relationship to 39 different financial assets and markets at various time lags. We found through granger causality that the geopolitical risk time series contained predictive information on several assets and market changes at different lag times.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.