By Kevin Plumberg
It takes a person about 10 minutes to read a 2,500-word, front-page
feature story in the Wall Street Journal. Computer programs
increasingly being used by investors to parse news stories can process
one in about three one-hundredths of a second.
Algorithms -- problem-solving programs based on mathematical formulas
-- are making it easier for investors to filter the massive amount of
text produced by news wires, newspapers, industry journals, clinical
studies, and legal filings for kernels of information, and trade on
them in the blink of an eye.
Though the expanding array of news on nontraditional media like blogs
and chat pages is a challenge for the robot readers, the speed and
efficiency offered by news mining algorithms are helping hedge funds
with just a handful of staff generate as many trades as a giant
investment bank and becoming a potential boon to the media industry.
"This is a new class of information technology," said John Partridge,
vice president of industry solutions with StreamBase Systems, a
technology provider that specializes in processing and analyzing
real-time streaming data.
High-frequency investors such as hedge funds are using news mining
platforms like those offered by StreamBase to troll through thousands
of electronic feeds of streaming text to identify key phrases on which
to trade.
Popular phrases include "lowers its outlook" or "raises guidance" or
even buzzwords like "stellar performance" that could potentially push
a stock lower or higher.
Hedge funds, with their rapid-fire trading style, often allow the news
mining platforms to make trades on their own, capitalizing on the
technology's speed.
However, longer-term investors are less interested in flooding the
market with orders after a particular headline. They are using the
platforms to keep track of developments that may affect companies in
their portfolios or influence their strategies, technology developers
said.
VELOCITY, VARIETY, VOLUME
News mining is not just for stock trading, either. For example, French
investment bank BNP Paribas' "weakness indicator" counts the number of
times the words weak, weakness or weakening are used in the Federal
Reserve's Beige Book report on regional U.S. economies.
More than 50 references in a report typically signals the economy is
on the brink of a recession.
Hedge fund investors familiar with news mining technology said an
algorithm based on the "weakness indicator" could easily be created to
sell dollars and U.S. stocks and buy bonds if more than 50 references
were found.
"What the machine is looking for is the same thing that the human is
looking for. It can just find it more quickly," said Richard Brown,
business manager of NewsScope, a company owned by Reuters Group Plc
that produces machine-readable news.
Rather than just highlight words or phrases, some of the most
sophisticated news mining platforms can take multiple strands of news
from wire agencies and Web sites and score the significance of various
items.
For example, headlines from a reputable news organization with the
words "Middle East," "tension" and "hostility" would be given a higher
score, especially if oil prices are rising, than an anonymous blog
entry with the same key words.
The same headlines would be given an even higher score if other
reputable news agencies carried similar stories.
"A lot of times, the content that's important is not in a single
article or document," said David Leinweber, a financial technology
consultant with Leinweber & Co. "The idea of considering individual
news stories only as atomic events misses some things," he said.
On his own blog "Nerds on Wall Street," Leinweber noted the example of
Accentia, a pharmaceutical company whose share price shot up 70
percent one morning in October 2006 after the successful trial of a
human cancer vaccine was announced in a press release.
However, the press release was based on an article from a medical
journal published a month earlier. Also, local press in St. Louis,
where Accentia has a plant, reported on the testing a week before the
press release, and a blog for patients discussed the drug days before
the stock jump.
An investor using news mining technology could have been buying into
the company days, if not weeks, before the big share price rise.
MISS STEAKS
Computers, however, are not perfect when it comes to reading the
various forms of language in both standard and nonstandard media.
Consultant Leinweber added that machines often have difficulty with
subtle double negatives and vague pronouns that human readers can
understand easily with context.
For example, machines could potentially stumble when it comes to a
sentence such as: "The company's chief executive said he did not
dislike the way that that product sold well there." A person could
scan the sentence and understand it.
The growing amount of text and information available on blogs, chat
rooms and online forums also pose challenges to robot readers.
"That's one of the limitations. When you look at chat room and blog
content, it's the emoticons, it's the profanity, it's sarcasm or all
caps," said NewsScope's Brown.
Still there is growing interest in the investment community in being
able harness the information available in so-called social media.
Darren Kelly, senior vice president at Collective Intellect, a company
that specializes in filtering and ranking media content, said blogs
and online forums can provide a unique window on sentiment surrounding
an issue or a stock.
"The usual multiscreen setup that everyone has used in finance for the
last 20 years no longer gives them all the information that's
available," Kelly said.
Copyright 2007 Reuters Limited.
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Kevin Plumberg, Reuters (reuters@telecom-digest.org)