RavenPack News Analytics

Turning text from traditional & social media into a structured data feed for quantitative applications



  • Automated event & entity detection
  • Systematic sentiment scoring
  • Low latency
  • Extensive historical data



  • Quantitative & algorithmic traders
  • Automated market-makers
  • Portfolio managers
  • Surveillance analysts


RavenPack News Analytics (RPNA) provides real-time structured sentiment, relevance and novelty data for entities and events detected in the unstructured text published by reputable sources.

Publishers include Dow Jones Newswires, the Wall Street Journal, Direct Regulatory and PR feeds and over 19,000 other traditional and social media sites. 

RavenPack News Analytics is used to enhance returns or improve efficiency by quantitative & algorithmic traders, automated market-makers, portfolio managers, risk managers and surveillance analysts.

Up to 16 years millisecond time-stamped data is available for backtesting.


Global Equities

RavenPack detects news and produces analytics data on over 40,000 listed stocks from the world's equity markets. Coverage is spread across the Americas, Europe and Asia-Pacific.


Global Macro

RavenPack analyses news and delivers data on over 2,500 financially relevant organisations, 138,000 places, 155 currencies and 82 commodities.



  • Unstructured data converted to structured data – the data is 100% machine-readable

  • Reputable content sources - RavenPack ingests newswire content from Dow Jones and the Wall Street Journal and web content from over 19,000 online sources

  • Automated event detection – RavenPack monitors published content to identify and alert users to key scheduled and unexpected geopolitical, macroeconomic and corporate events

  • Proprietary entity detection – unique identifiers provide easy mapping to client databases or models and point-in-time sensitive classification helps avoid survivorship bias

  • Systematic sentiment scoring – RavenPack applies entity-specific relevance, novelty and sentiment scores to all events in news articles using both traditional language processing (NLP) and proprietary techniques

  • Speed – all news articles are assessed within milliseconds of receipt and the resulting data is immediately pushed to users

  • Historical data – Up to 16 years of millisecond time-stamped data available for backtesting