Home' Technology Review : November December 2007 Contents 40 FEATURE STORY
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CDO pays the most income. Created by quants and priced by
quants, CDOs have become a popular way for hedge funds,
pension funds, insurance companies, and other investors
to buy pieces of high-risk but high-pro t sectors like sub-
prime loans. According to the Securities Industry and Finan-
cial Markets Association, annual issues of CDOs worldwide
nearly doubled between 2005 and 2006, going from $249.3
billion to $488.6 billion.
The quants who devise such derivatives work more or
less in public view. They re obscured mainly by the
complexity of their work. But our knowledge of the
quants who design trading strategies is additionally occluded
by the secrecy of the big fund operators like Renaissance
Technologies. I did manage to speak with some current trad-
ers, who gave me a general idea of their approach, and with
some ex-traders, who were slightly more speci c.
One common method that quants use to identify market
opportunities is pairs trading. Pairs trading involves trying
to nd securities that rise in tandem, or that tend to go in
opposite directions. If that relationship falters---if, say, the val-
ues of two stocks that travel together suddenly diverge---it s
likely to indicate that one stock is under valued or overvalued.
Which stock is which is irrelevant: a trader who simultane-
ously bets that one will go up and the other one down will
probably make money. It s a strategy that lends itself to the
use of computers, which can sort through huge numbers of
price correlations over many years of stored data---although
the nal decision to speculate on the relative pricing of paired
stocks generally rests with a fund s managers.
Quants have also been pursuing a strategy known as "capi-
tal structure arbitrage," which seeks to exploit ine cient
pricing of a company s bonds versus its stocks. Again, com-
puters do the searching, looking for instances where, for one
reason or another, the securities are slightly misaligned.
In a similar technique, Max Kogler, a principal at the
newly launched MM Capital in New York, uses computers
to look for inconsistencies in value between the option on
an index fund and the options on the stocks that compose
that index. Kogler has a master s from the University of
Cambridge in pure mathematics with a focus on statistics.
He says his algorithms look for "baskets of options that are
not doing what they re supposed to be doing." When his
computers nd such a basket, he and his partners discuss
whether or not to buy.
Kogler r uns his algorithms on "one Linux box." "Part of
the allure of our algorithm," he said in an e-mail, "is that it
cuts down computational requirements dramatically. None-
theless, you ll want to have a speedy machine with pretty
decent clock speed and a couple of parallel CPUs."
In what s called nondiscretionary trading, computers
both nd the ine ciencies and execute the trades. The
Aite Group, a nancial-services research rm, estimates
that roughly 38 percent of all equities may be traded auto-
matically, a number it expects to increase to 53 percent in
Computers also underlie another developing frontier,
high-frequency trading, which is a fantastically exaggerated
form of day trading. The computer looks for patterns and
ine ciencies over minutes or seconds rather than hours or
days. An algorithm, for instance, might look for patterns in
trading while the Japanese are at lunch, or in the moments
before an important announcement. There is a massive
amount of such data to crunch. Olsen Financial Technolo-
gies, a Zürich-based r m that o ers data for sale, says it
collects as many as a million price updates per day.
One trader I spoke with at a $10 billion hedge fund
based in New York said that his computer executed 1,000
to 1,500 trades daily (although he noted that they were not
what he called "intra-day" trades). His inch-thick employ-
ment contract precluded my using his name, but he did talk
a little bit about his approach. "Our system has a touch of
genetic theory and a touch of physics," he said. By genetic
theory, he meant that his computer generates algorithms
randomly, in the same way that genes randomly mutate.
He then tests the algorithms against historical data to see
if they work. He loves the challenge of cracking the behav-
ior of something as complex as a market; as he put it, "It s
like I m trying to compute the universe." Like most quants,
the trader professed disdain for the "sixth sense" of the tra-
ditional trader, as well as for old-fashioned analysts who
spent time inter viewing executives and evaluating a com-
pany s "story."
High-frequency trading is likely to become more com-
mon as the New York Stock Exchange gets closer and closer
to a fully automated system. Already, 1,500 trades a day is
conser vative; the computers of some high-frequency traders
execute hundreds of thousands of trades every day.
Linked with high-frequency trading is the developing
science of event processing, in which the computer reads,
interprets, and acts upon the news. A trade in response
to an FDA announcement, for example, could be made in
milliseconds. Capitalizing on this trend, Reuters recently
introduced a ser vice called Reuters NewsScope Archive,
which tags Reuters-issued articles with digital IDs so that
an article can be downloaded, analyzed for useful informa-
tion, and acted upon almost instantly.
All this works great, until it doesn t. "Everything falls
apart when you re dealing with an outlier event," says the
trader at the $10 billion fund, using a statistician s ter m
for those events that exist at the farthest reaches of proba-
bility. "It s easy to misjudge your results when you re suc-
cessful. Those one-in-a-hundred events can easily happen
twice a year."
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