{"id":{"repo_id":"brock","oai_identifier":"oai:brocku.scholaris.ca:10464/19388"},"canonical_url":"https://search.dev.ndltd.org/etd/brock/oai:brocku.scholaris.ca:10464/19388","repository":{"repo_id":"brock","name":"Brock University","base_url":"https://brocku.scholaris.ca/server/oai/request"},"display":{"title":"Decoding Relations between Geopolitical Risk and Financial Markets","abstract":"This thesis examines the relationship between geopolitical risk and financial markets by analyzing two major geopolitical events: the Russia-Ukraine conflict and China-related tensions. Using Latent Dirichlet Allocation (LDA) topic modeling, it identifies key topics discussed in business media. Through ordinary least squares and quantile regression analyses, the thesis examines the relationship between topic sentiment (polarity) and hype (intensity), and financial market returns under different market conditions. For the Russia-Ukraine conflict, the study analyzes 11,929 Wall Street Journal articles published between 2014 and 2024, identifying 12 distinct topics. Among them, the Global Economy and Oil topics exhibit the strongest positive relation with oil returns in both sentiment and hype, particularly during bullish oil markets. Regarding China-related tensions, the thesis examines 13,784 Wall Street Journal articles from 2009 to 2024, uncovering 21 topics. The sentiment and hype associated with these topics demonstrate varying relationships with U.S. and Chinese stock market returns under different market conditions. This research provides valuable insights into the relationship between media discussions and market behavior during periods of geopolitical uncertainty.","abstract_html":"This thesis examines the relationship between geopolitical risk and financial markets by analyzing two major geopolitical events: the Russia-Ukraine conflict and China-related tensions. Using Latent Dirichlet Allocation (LDA) topic modeling, it identifies key topics discussed in business media. Through ordinary least squares and quantile regression analyses, the thesis examines the relationship between topic sentiment (polarity) and hype (intensity), and financial market returns under different market conditions. For the Russia-Ukraine conflict, the study analyzes 11,929 Wall Street Journal articles published between 2014 and 2024, identifying 12 distinct topics. Among them, the Global Economy and Oil topics exhibit the strongest positive relation with oil returns in both sentiment and hype, particularly during bullish oil markets. Regarding China-related tensions, the thesis examines 13,784 Wall Street Journal articles from 2009 to 2024, uncovering 21 topics. The sentiment and hype associated with these topics demonstrate varying relationships with U.S. and Chinese stock market returns under different market conditions. This research provides valuable insights into the relationship between media discussions and market behavior during periods of geopolitical uncertainty.","abstract_has_math":false,"creators":["Babalola, Moyosore"],"institution":"Brock University","degree_name":"M.Sc. 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Using Latent Dirichlet Allocation (LDA) topic modeling, it identifies key topics discussed in business media. Through ordinary least squares and quantile regression analyses, the thesis examines the relationship between topic sentiment (polarity) and hype (intensity), and financial market returns under different market conditions. For the Russia-Ukraine conflict, the study analyzes 11,929 Wall Street Journal articles published between 2014 and 2024, identifying 12 distinct topics. Among them, the Global Economy and Oil topics exhibit the strongest positive relation with oil returns in both sentiment and hype, particularly during bullish oil markets. Regarding China-related tensions, the thesis examines 13,784 Wall Street Journal articles from 2009 to 2024, uncovering 21 topics. The sentiment and hype associated with these topics demonstrate varying relationships with U.S. and Chinese stock market returns under different market conditions. This research provides valuable insights into the relationship between media discussions and market behavior during periods of geopolitical uncertainty."]},{"key":"dc:title","label":"Title","values":["Decoding Relations between Geopolitical Risk and Financial Markets"]}]}],"canonical_facts":{"dc:contributor.advisor":["Biktimirov, Ernest"],"dc:contributor.department":["Faculty of Business Programs"],"dc:creator":["Babalola, Moyosore"],"dc:date.accessioned":["2025-05-23T20:22:19Z"],"dc:date.available":["2025-05-23T20:22:19Z"],"dc:date.issued":["2025-05-23T20:22:19Z"],"dc:description.abstract":["This thesis examines the relationship between geopolitical risk and financial markets by analyzing two major geopolitical events: the Russia-Ukraine conflict and China-related tensions. Using Latent Dirichlet Allocation (LDA) topic modeling, it identifies key topics discussed in business media. 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