What is News Analytics?

Let us first define what is meant by News Analytics. According to Wikipedia, News Analytics refer to metrics derived from textual news stories for the purpose of representing their qualitative nature in a quantitative manner. Measurements of any particular qualitative property are expressed as a specific quantity or unit. Many attributes in textual news stories including sentiment, relevance, and novelty are studied as quantitative properties. Expressing news stories as numbers permits the manipulation of everyday information in a mathematical and statistical way.

News analytics are used in financial modeling, particularly in quantitative and algorithmic trading. They are usually derived through automated text analysis and applied to digital texts using elements from natural language processing and machine learning such as latent semantic analysis, support vector machines, "bag of words" among other techniques.

News Analytics are delivered in a variety of formats, often as machine readable XML documents or .csv files. They include numerical values, tags, and other properties that tend to represent underlying news stories. For back-testing purposes, historical information is often delivered via flat files, while live data for production is processed and delivered in milliseconds through direct data feeds or APIs.

Being able to express news stories as numbers permits the manipulation of everyday information in a mathematical and statistical way that allows computers not only to make decisions once made only by humans, but to do so both faster and more efficiently. Since market participants are always looking for an edge, the speed of computer connections and the delivery of news analytics, measured in milliseconds, have become essential. News analytics not only allows market participants to capture alpha opportunities in the market, it can also be used to improve on risk management and trading execution.