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News Score by RavenPack - Facts Sheet:

News scores represent a quantification of financial news and specifically news sentiment about traded assets. These scores are produced by detecting the tone and potential impact of market-moving stories published about a specific company. Calculations are performed in real-time to produce continually updated scores between 0 and 100. Higher values indicate positive news sentiment, whereas lower values indicate negative sentiment. Scores hovering around 50 depict neutral sentiment.

Company-level news scores include one master score and individual supporting scores. Each score is calculated using a different linguistic classifier. A linguistic classifier is an algorithm designed to assess the overall sentiment of a stock by detecting language within stories that is likely to drive prices upward or downward over a given period of time.

Some classifiers specialize in deriving news sentiment according to the type of story (i.e. corporate actions like earnings announcements or press releases on product recalls and layoffs). Others emulate how a group of sophisticated analysts would be likely to rate a news item in terms of potential impact on price or traded volume.

Finally, RavenPack classifiers take into account how markets tend to respond historically to particular types of news by mining years of news archives.

News Sentiment in Milliseconds: By leveraging diverse news sources, sophisticated machine learning sentiment engines and intuitive delivery formats, RavenPack’s news analytics represents the new alpha-capturing inputs being increasingly sought after by financial institutions. RavenPack’s news sentiment accuracy and analytics have been rigorously tested and are in use by some of the world’s largest financial institutions.

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