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Wall Street analysts and traders have a new weapon at their disposal: news analytics. In an effort to keep up with the deluge of news, events and alerts, some investment firms are turning to machines to read and score news for sentiment and word counts. This information is then inserted into trading models, which may be responsible for a huge buy or sell in your company’s stock. Marketers, with machines reading news and making buy/sell decisions in near real time, what are the implications for your PR, communications, and social media strategies?

Wall Street analysts and traders have long believed that stock prices jump on the release of positive and/or negative news. But the sheer number of news sources and volume makes comprehension a daunting task for individual traders. Adding insult to injury, most of the data in the world is unstructured, meaning that it is not in a database and may consist of text, JPEG images, flash videos, etc. So, interpreting the “meaning” of unstructured data often takes too much time.

Enter analytics. With the assumption that news flow is a good indicator of trading volume and stock price volatility, traders are using real-time data feeds, advanced algorithms, and computer power to digest and execute trades on “news” in sub-seconds. Machines are reading press releases, news stories, analyst reports, stock alerts, and more to gauge the sentiment, relevance, novelty, and volume of news. And trading firms are busy designing models to forecast stock prices based on historical news volumes.

Machines reading the news are scanning for two key criteria: sentiment and counts.

Let’s tackle sentiment first. Reading for sentiment, algorithms are scanning news looking for key phrases such as “better than expected” or other verbiage. They score news on how relevant a news item is to your particular company, whether the news is unique, the source of news (key analyst vs. small time shop), and what the specific headline says.

When examining counts, news algorithms seek how many times a key phrase shows up in the news, how often that key phrase is used over a time period (e.g., last 24 hours, past three days, and even how many articles were placed over a specified time frame to discern news volume.

This trend has significant implications for marketing and PR professionals. While we may not know the “weighting” system of what is most important to these algorithms (e.g., word counts might be more important than uniqueness), we should definitely bear in mind that in addition to human readers, we’ll now have to contend with machines.

At some point, most marketers have solely written, edited or approved a corporate press release. However, with machines starting to “read” the news items, your communication strategies might need more than a simple tweak. Ultimately, this means that press releases may need to be optimized for machine scanning. In addition, as these algorithms monitor news feeds from analysts, commentators, and other news professionals, one strategy might be doubling down on press and analyst relations to help shape content before the computers read it.

Machines are now reading the news and trading on what they discern. And news analytics isn’t just for large cap stocks! In fact, any company that trades on an exchange is fair game. Knowing this, your company’s stock price might go significantly up or down depending on your future marketing, social media and PR strategies. Fortunately news analytics is in the early adopter phase, but if there’s money to be made then surely this will be a growing trend.


  • Other than those listed above, what are the implications of machines reading the news?

  • What might be some PR, marketing, and social media strategies to take advantage of this trend?

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Paul Barsch directs services marketing programs for Teradata, the world's largest data warehousing and analytics company. Previously, Paul was marketing director for HP Enterprise Services $1.3 billion healthcare industry and a senior marketing manager at global consultancy, BearingPoint. Paul is a senior contributor to MarketingProfs, a frequent columnist for MarketingProfs DailyFix, and has published over fifteen articles in marketing, management, technology and healthcare publications. Paul earned his Bachelors of Science in Business Administration from California Polytechnic State University, San Luis Obispo. He and his family reside in San Diego, CA.