Leveraging sentiment approaches underpinned by machine learning algorithms and millions of rule sets, RavenPack Analytics empowers researchers to construct innovative signals that deliver insights on many investment universes, geographic scopes, and asset classes.
With a model covering over 12 Million named entities, from public and private copmanies to persons, locations, sectors, and services, RavenPack automatically tags textual content with topic tags covering entities and events. The RavenPack taxonomy extends to over 7,000 event topics, and sentiment indicators are delivered on entities even in absence of detected events.
Drawn from highly-regarded feeds like Dow Jones, Wall Street Journal, Benzinga, MT Newswires, and Barron's, and web content from national, regional, and local sources including reputable blogs and aggregators, RavenPack updates are provided in real time with over 20 years of historical archives for back testing.
RavenPack delivers relevance scoring, impact analysis, and novelty tracking, for each detected entity and event, with additional temporal data around each event.
RavenPack High-quality datasets are ideal for innovative and fast-paced research
Explore patterns, relevance, and influences in time series using quantitative models across an unprecedented breadth of topics
Highly-structured, 100% machine-readable alternative data
Records augmented with sentiment and media attention scores let you study events as they unfolded
RavenPack’s team of data scientists and researchers are ready to help you navigate the datasets and use cases
The most detailed coverage of corporate, economic, societal, environment and political events, even unscheduled ones
RavenPack data is available on Wharton Research Data Services (WRDS) the leading business intelligence, data analytics, and research platform to global institutions—enabling historical analysis and insight into the latest innovations in academic research.
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