From barley to castor oil, robusta coffee, steel or soybeans, leverage sentiment
indicators on market-moving news.
RavenPack delivers an exceptionally broad
coverage of carefully curated news, social media,
and data sources:
Highly-granular indicators built using proprietary machine learning
deliver a quantitative analysis of sentiment for each entity involved in
detected events, including credit and sustainability risk.
Make data-driven decisions based on a refined view of various aspects
of news and stay ahead of commodities-related narratives.
Trends develop over weeks and months, but only a sophisticated analytical
framework can detect them as they shape. With RavenPack Edge data, you can analyze
co-mentions networks applied to commodities to identify emerging actors and market shifts.
Explore some of the research published on the use of RavenPack for commodities trading:
In this study, we show how RavenPack Analytics (RPA) can be used to uncover profitable trading signals for energy futures.
Brandt & Gao (Duke University / University of Luxembourg) use RavenPack's Big Data analytics to predict oil prices. They find that news related to macro fundamentals has an impact on the oil price in the short run and significantly predict oil returns in the long run.
Researchers at Differential Research present their findings in this paper titled 'Seeking a Behavioral Response to Information: News Analytics and VIX Futures Prices'.
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