A Novel Framework for Analyzing Economic News Narratives Using GPT-3.5: Conclusions and References

This story was originally published on HackerNoon at: https://hackernoon.com/a-novel-framework-for-analyzing-economic-news-narratives-using-gpt-35-conclusions-and-references. Analyzing economic news with GPT-3.5 and network analysis to detect evolving topics and narratives, and linking news structures to financial market volatility. Check more stories related to finance at: https://hackernoon.com/c/finance. You can also check exclusive content about #financial-markets, #ai-in-finance, #economic-news-analysis, #hedging-strategies, #gpt-3.5-applications, #sentiment-analysis, #network-analysis, #financial-market-volatility, and more. This story was written by: @hedging. Learn more about this writer by checking @hedging's about page, and for more stories, please visit hackernoon.com. Researchers analysed economic articles from The Wall Street Journal. They found that lower sentiment within news is more likely to be associated with weeks of market dislocation. This suggests that the interconnectedness of news’ topics and structure therein are meaningful aspect to further analyse within financial research, for which our study desires to serve as a first baseline.