| Trading on the
News: Turning Buzz Into Numbers;
Securities Industry News
September 21, 2009
By Katherine Heires
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 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. Thats 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.
There is a lot of financial information quoted on twitter
that is simply not true, says Timothy Sykes, an independent trader and frequent
Twitterer. Just the other day, there were rumors posted about a company going
bankrupt that were false. You have to take all this information with a grain of
salt.
Traders are using the most advanced technologies available
today to solve an age-old problem: How to derive maximum benefit from either rumor or
news, notes Roy Freedman, an adjunct professor at Polytechnic Institute at New York
University.
The old financial adage - Buy on rumor, sell on news - remains
valid today, he said.
The only difference is that, machines are doing most of the
trading today rather than humans, Freedman said, with the development of best
practices an evolving process.
The growing interest in trading on the news via
algorithms, observers and market participants say, is due in part to traders
never-ending quest for alpha - a return that exceeds the general market return.
But it also is a response to increasing investments in high-speed
technology, such as complex event processing software that can speed up the process of
building algorithms or provide real-time analysis of complex events such as a sudden
uptick in market prices after the president gives a speech or whether or not the CEO of Apple
is in good health.
Among providers in the category of machine-readable news services
and related, real-time text analysis services are a host of traditional news providers
such as Bloomberg, Dow Jones, Need to Know News (NTKN) and Thomson Reuters as well as
startup or smaller firms such as Acquire Media, Kinetic Trading, Psydex, Selerity Corp.,
StockMood and Streambase, a provider of algorithm development software. The firm launched
a service this spring that helps traders monitor tweets on twitter
for price sensitive data. The information can also easily be fed into algorithmic trading
strategies, via Streambases software.
Growing interest in trading on news is fueled by quant traders
seeking unexpected factors in the marketplace they can capitalize on. Feeding into this: stocktwits,
a service that aggregates conversations about stock trading on twitter.
A lot of quant traders have continued to trade off of the
same market data day after day and as a result, their algorithms have been less effective
and they have been losing their alpha.More recently, they are turning to alternative
content like machine-readable news or news analytics to factor into their models and
improve their strategies, noted Richard Brown, global business manager,
machine-readable news at Thomson Reuters.
His firm, working with UK-based Infonic, a news sentiment
specialist, has developed a NewsScope Sentiment Engine which assigns sentiment
scores to news articles to indicate the positive or negative sentiment they
represent so that the info can be swiftly fed into trading algorithms. In the firms
latest product update, announced in May, Thomson Reuters began to include scores for
real-time commodity and energy market news, including six years of historical news
sentiment data. This allows back-testing of strategies and modeling historic correlations
between sentiment and prices.
Services such as Bloomberg, Dow Jones and Thomson Reuters tend to
focus on the transformation of their own, branded news product into machine-readable news
and tout their ability to provide archived and reliable news data for back-testing of
strategies. Newer services are more experimental in the types of news and event
information they may process.
Psydex, a three-year old startup firm based in Atlanta, Georgia,
is a data mining and text analytics firm that operates on the premise that citizen
journalists, posting on services such as Twitter, Facebook and the Web, will often observe
and report on a market-moving event faster than any mainstream news outlet.
Our focus is on unscheduled events - an emerging area of
interest to traders - and using natural language and semantic-based algorithms, we analyze
mounds of real-time news and information flow from TV, business wires, Dow Jones, Thomson
Reuters, Twitter and blogs in a tiny fractions of a second, explained Rob Usey, a
former IBM executive and CEO and co-founder of Psydex.
Psydex uses large grids of computers, trillions of bytes of
memory and patent-pending technology to process traditional and non-traditional news. The
firm using topic model analysis (e.g., a search for information about Google
and acquire), analyzes the location and proximity of key words, the precise
time that they appear and the frequency of such words, to determine if the frequency of
references to a given subject or topic is increasing. The system is then able to turn this
into numbers and produce a machine-readable news feed, suitable for algorithmic use - all
within 20 thousandths of a second.
This allows the firm to assess the average number of mentions of
a particular topic over a specific time period and look at standard deviations from the
mean to produce a real-time, semantic tick feed. Just as a tick data feed allows you
to see if stock price levels are normal, unusual or highly unusual, we are able to do the
same with content news flow and see if its influencing a particular company,
Simpson said.
We are seeing that traders are now doing pure, black box
trading off of our event-based news; Clearly, among traders, the world of news does not
revolve around the established news providers, the Dow Jones and Thomson Reuters of the
world, Usey said.
Increasingly, people are looking at all forms of news and
building their own indicators around it in a semi-structured way, as they constantly
seek out new trading advantages said Rob Passarella, global director of strategy at Dow
Jones Enterprise Media Group. His firm provides both a low latency news feed and news
analytics for traders. Passarella also pointed to new academic research being conducted on
the degree to which frequent Google searches on various stocks can serve as trading
indicators, the potential impact of various phrases and words that may appear in
Securities and Exchange Commission statements and the latest wave of online communities
devoted to stock trading topics.
Markets are by their very nature conversations, having
grown out of coffee houses and taverns, he said. So the way conversations get
created in a digital society will be used to convert news into trades, as well, Passarella
said.
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