TRADING ON THE NEWS: Turning Buzz Into Numbers
September 21, 2009
Trading decisions based on news developments are nothing new. Whether the market-moving news arrives by boat, carrier pigeon or Blackberry, traders have always been eager to be the first to exploit and act on information that may impact a given market.
Yet now news is only new for a fraction of a second. Algorithms and rules-based engines filter text as it appears online, identify its underlying meaning, assess its importance and then - when warranted - execute trades based on it. All in a matter of milliseconds. Ideally, a thousandth or two of a second before competing traders' algorithms do so.
At the same time, the definition of news as it applies to trading markets is changing as well. In the age of Facebook, Twitter and social networks "we are seeing many new and different kinds of data sources that can be analyzed and mined for tradable insights," which in turn can be turned into machine-readable text or numbers and assessed for value by trading algorithms, notes Roger Ehrenberg, an independent investor in financial technology startups and former CEO of DB Advisors, a quantitative trading operation owned by Deutsche Bank that managed more than $6 billion.
At this point, there is not a set template for such efforts. Executing trades based on such automated forensics is an evolving science that is attracting more practitioners. Particularly interested in turning news into numbers are traders who employ quantitative strategies.
Early efforts have involved the use of high-speed news feeds that combine news as it traditionally has been defined: the stuff of newspapers, magazines, TV and radio newscasts and official-source-issued economic data.
According to Don Williams, managing director with Ravenpack, a news sentiment specialist firm that works closely with news provider Dow Jones, there are several steps to turning a traditional news article into machine-readable news or numerical info for use by a trading algorithm:
News content or text is usually analyzed simultaneously by five different natural language or sentiment analysis algorithms. The software studies the degree to which a particular article conveys positive or negative language about a given company, for example, or the degree to which the text may impact volatility in a given stock. A score ranging from 0 to 100 is produced for each one of the natural language analyses conducted. This numerical information can then be used in a customized way by quantitative traders as a factor for consideration in their trading models.
But this is just one way to speed up news filtering efforts. Increasingly, event-based information that can move markets and is being culled from blog postings, social network conversations, Facebook and Twitter text and being considered, weighted and in some instances, factored directly into trading algorithms.
"When the CEO of a major company says something at a conference, there is no official press release, the event is mentioned on Twitter and we see a massive move in the stock price in one day," says Don Simpson, chief technology officer of Psydex, a startup. That's information that can be factored into trading algorithms so machines can act on it far faster than a human trader would. His firm supplies text analysis and data mining software that can facilitate such activities. .
Others are wary of such practices.