RavenPack for Research

Expand the scope and depth of your research with events from news, sentiment analytics, and media attention, available on WRDS

Why Use News analytics in Research?

Sentiment Analysis
CATEGORY LEADER SINCE 2003

Explore and Combine Independent Signals

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.

Sentiment Analysis
Semantic Tagging

Metadata that Contextualize Insights

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.

Semantic Tagging
Premium Content

40,000 Curated Sources of News and Social Content, in 13 Languages

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.

Premium Content
Advanced analytics

Indicators that Help You Make Sense of News

RavenPack delivers relevance scoring, impact analysis, and novelty tracking, for each detected entity and event, with additional temporal data around each event.

Advanced Analytics

RavenPack empowers your research

RavenPack High-quality datasets are ideal for innovative and fast-paced research

Academic research

Explore Research Based on RavenPack Data:

J. Deveikyte, H. Geman, C. Piccari, A. Provetti (University of London)

A Sentiment Analysis Approach to the Prediction of Market Volatility
M.J. Jung, J.P. Naughton, A. Tahoun, C. Wang (New York University)

Corporate Use of Social Media
, L. Chen, Y. Chen (University of Exeter), A. Kumar (University of Miami), W.S. Leung (University of Edinburgh)

Firm-Level ESG News and Active Fund Management
S. Luo (Massachusetts Institute of Technology)

Identifying Investors with Sentiment-based Investment Strategies and Predicting their Trading
M. Samadi (University of North Carolina)

Intermarket Competition: Evidence from Trading Venue Short Sales
B. Twedt (Texas A&M University)

Spreading the Word: Capital Market Consequences of Business Press Coverage of Management Earnings Guidance
R. Bushman (University of North Carolina), C. Williams (University of Michigan), R. Moerman (University of Southern California)

The Informational Role of the Media in Private Lending
N. Babolmorad, N. Massoud (University of Melbourne)

When Sentiment Is News: The Polarity Pattern Approach

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