Reduce risk and increase efficiency by systematically incorporating the effects of public information into your workflows.
Since 2003, RavenPack has pioneered investment-grade sentiment analysis in financial services. We do not believe in “one size fits all” and have developed multiple sentiment techniques where some leverage millions of rule sets while others use sophisticated machine learning algorithms.
Every news story is automatically tagged with topic tags including entities and event categories. RavenPack can recognize over 12 Million named entities including global companies across all industries, both public and private sectors, individuals, executives, insiders and influencers, geographic locations, products, and services. Our proprietary taxonomy covers more than 7,000 different event topics.
Full historical and real-time updates from premium sources including Dow Jones, Wall Street Journal, Barrons, MT Newswires, Benzinga, among others. Our web content captures local, regional, and national newspapers and reputable blogs and content aggregator sites.
For every entity and event detected in a story, RavenPack provides advanced analytics including relevance scoring, novelty tracking, and impact analysis. In addition, temporal data provides useful dates for when events take place.
More than 70% of the best performing quantitative hedge funds and asset managers use RavenPack News Analytics for alpha generation and risk management.
Derive signals to support trading models across all asset classes, trading styles and frequencies
Improve asset allocation decisions by measuring sentiment on markets, sectors, and asset classes
Forecast stock, sector, market and global economic outlooks
Predict liquidity, volatility, and directional movements caused by news events
Detect market abuse, mitigate risk quickly when events happen, and build early-warning systems
Conduct pre-trade and post-trade analysis by connecting trading activity to key news announcements
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