Veteran Trader Says Systematic Funds Not Sexy, But Stable

Dec 7 2010 | 10:50am ET

Quants—traders who use computers to make high-speed investment decisions based on mathematical algorithms—didn’t emerge unscathed from the financial meltdown. In fact, they are frequently blamed for the financial meltdown—hence the title of a new book by The Wall Street Journal's Scott Patterson: The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It.

But Alexei Chekhlov, co-founder of New York-based Systematic Alpha Management, a fully-systematic managed futures fund, says the truth about quants is far less exciting.

“It’s really hard to write about them in a magazine that’s supposed to get high subscription rates because they’re really not sexy enough,” Chekhlov told FINalternatives in a recent phone interview.

SAM manages $680 million in two such "unsexy" funds: Systematic Alpha Futures, a short-term, market-neutral, mean reversion-based strategy established in 2001, and Systematic Alpha Diversified, a recently-launched short-term trend-following strategy.

Chekhlov helped design the company’s high-frequency trading program and is SAM’s head of research, but he’s quick to share credit: He works, he says, with a team of 30 “very bright” people that includes theoretical physicists, mathematicians and computer scientists.

The idea is to bring “rationality” to the financial arena, something Chekhlov notes you can’t always expect from people.

“The human mind, for historical, evolutionary reasons, is designed to respond very, very rationally to certain durations of changes to the environment and not to other ones," he explains. "For example, if something changes in three seconds, you would hardly be likely to react rationally; you might just react based on your instincts, which are fairly simplistic and... can be erroneous. You’ll only react rationally if you’re given time to think. Particularly if you’re given time to go and Google things, right? But what if you aren’t?”

When humans make snap decisions, Chekhlov says, the time between the "push" (whatever causes you to make a decision) and the response (your decision) is very short. “So, you may not be the most rational analyzer, making the most appropriate decision…. It could be me, it could be  Karpov or Kasparov, for that matter…. They will still be animalistic humans.”

Which means, he says, that in some situations a “well-trained algorithm will outperform a human mind.”

SAM’s “well-trained algorithms” are constantly evolving because “markets are constantly evolving.”

“They’re really not stationary in the scientific sense,” says Chekhlov. “There are some absolutely constant properties that do not change over time.... but some parameters do change because markets get modified, new regulations are being put in place, various external influences change—look right now, we have many different big events going on, but one that I will pick is what they refer to as European contagion debt crisis… .That’s been going on for quite some time and it’s increasing now and if you’re using the same parameters for your model that you took from 10 years ago, I wouldn’t agree that this is a rational thing to do.”

Chekhlov says he and his team try to hear what the markets are telling them. They believe market “noise”—what Chekhlov describes as “transaction-level details that are normally overlooked”—contain important information. And if you can analyze that information appropriately, he says, you can “derive some rational ways of behaving in that market.” He compares the process to hydrodynamics engineers testing cars in wind tunnels—using sensors to gather reams of data which they then measure and analyze to derive something valuable.

Chekhlov says the firm specializes in futures because they are “the most liquid instruments you can buy and sell.” They tend to be traded in one geographical location at rates as high as several thousand “events” (trades) per second.

“People make these decisions very quickly, for a variety of reasons, and news is bombarding this market very quickly and I think that’s the reason why these markets can be potentially forecast using non-human thinking devices, like computers.”

The information they gleaned allowed Chekhlov’s fund to outperform in 2008—a year that battered many funds, particularly what he terms “classical, conventional managers.”

“We had a year of outperformance in 2008, why? Because—and this is a little controversial—because this was at the very end of yet another bull market... and  the signature of a bull market is many speculators are attracted based on, in part, public domain information, news and word of mouth of their friends. They’re attracted to speculate but they are not well-capitalized and not really rational, but that’s very, very juicy for us because then our model should be able to capture all the irrationalities that they produce.”

This “less rational part of the crowd” disappears from the market, he says, because “their capitalization is finite.”

“As soon as they lose they walk away and they’re not really replaced,” says Chekhlov. “Then the more institutional investors come forward... as the crisis gets into this prolonged stage... more professional, more rational investors remain fighting with each other…. That’s why we outperformed in the crisis year, 2008 and our performance has been kind of decaying in 2009, 2010, yet remaining positive.”

Most of their inflow over the past year, has, in fact, come from institutions, he says, and as of the end of October 2010, they were up about 6.5% for the year. So are quants on the rebound?

“It really depends which quants and I think that this environment remains difficult even though, I think up until the end of October, many funds became positive," Chekhlov says. "But whether they are rebounding or not, I don’t think this invalidates the approach at all because they will be down, they will be up, but on average, the concept has proven itself fairly well and they seem to have produced the most diversified, most de-correlated and most stable returns. These are not gambler returns, these are not returns that you hit at blackjack—these are truly statistical returns with certain statistical properties which do have many reasons to recur.”

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