Artwork

Content provided by HackerNoon. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by HackerNoon or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Player FM - Podcast App
Go offline with the Player FM app!

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

9:38
 
Share
 

Manage episode 423443687 series 3474376
Content provided by HackerNoon. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by HackerNoon or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

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.

  continue reading

125 episodes

Artwork
iconShare
 
Manage episode 423443687 series 3474376
Content provided by HackerNoon. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by HackerNoon or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

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.

  continue reading

125 episodes

All episodes

×
 
Loading …

Welcome to Player FM!

Player FM is scanning the web for high-quality podcasts for you to enjoy right now. It's the best podcast app and works on Android, iPhone, and the web. Signup to sync subscriptions across devices.

 

Quick Reference Guide