QIM: Quantifying profits

After much contemplation, Jaffray Woodriff settled on quantitative behavioral finance to describe his trading methodology. It is a mouthful, as is behavioral finance academics, artificial intelligence and evolutionary programming — all apt descriptions of what he does. It also is understandably complex considering he has designed software that has created and tested hundreds of billions of trading models. The best models are employed in Quantitative Investment Management’s (QIM) Global program.

Woodriff, system designer and one of three principals at the Charlottesville, Va., based QIM, created his first trading system in 1987 as a college student. And while his trading was successful, that program and the subsequent commodity trading advisor (CTA) he formed never gained traction. In 1997 he moved to New York to trade his unique models for a bank. He ended up at Societe Generale were he traded commodities, currencies and individual stocks.

In New York he worked with Michael Geismar, another QIM principal, who was an old college buddy from the University of Virginia. Greyson Williams, pictured, is also a principal.

In December 2001 they went off on their own to trade Woodriff’s methodology with proprietary money. In October 2003, QIM started trading customer funds. The program produced returns of 35.69% in 2003, 22.96% in 2004 and is one of the few CTAs enjoying a positive 2005.

Woodriff’s software, which is based on mathematics, utilizes artificial intelligence techniques that take a group of relatively simple inputs and create a vast number of trading models. Those models are tested throughout 25 years of market data in 40 different markets and the best 1,500 models are used in the program. The models are scored on performance and correlation to each other. They create an overall score, between -500 and 500, for each market. A score above 100 or below -100 will generate a buy or sell signal. It is a compilation of the all the models that recognize market patterns; some are trend following and some are countertrend, while others are neither.

“The models are generated by computer code, they’re not selected because I went out to try and find a countertrend model that would do great at long-term market turns. The models are screened algorithmically based on how well they predict the movement of markets at any time,” Woodriff says. He ends up with many long- and short-term models. The composite signals tends to be the short- to medium-term type signals, holding trades an average of eight to 10 days.

He separates his methodology from other managers involved in behavioral finance who create methodology on the psychology of market participants. “We take a much more systematic approach to finding patterns,” he says. They will come up with a hypothesis that makes sense and then test it quantitatively. “While we have created a software program that goes out and searches for possible mathematical formulas for patterns, it is a much broader search,” he says. “We take a very generalized approach that there are patterns in the market and we are going to generate potential patterns and see how well they do by generating billions of them and see what comes to the top.”

The program at times acts as a reversal system. If the system’s score in a market goes from more than 100 to -100 or worse, it will reverse. This came in handy in Japanese Government bond futures (JGB) in April when QIM profitably flipped its JGB position several times.

Geismar points out, “For five consecutive days we went from long to short and short to long in the JGB, and made money on each trade. It is not uncommon for us to trade actively in speculative markets.”

While not the overall goal of the program, the models allow QIM to exploit market corrections. “We tend to catch pullbacks in the middle and near the end of trends and then get back in where trend followers would just be having a down day or getting stopped out. A lot of time we will take the other side of the trend for a day or three and then maybe get back into the trend for another several weeks after that,” Woodriff says.

That flexibility proved enormously beneficial in the volatile crude oil markets where Geismar notes QIM made money in each of the last six months from November 2004 to April 2005. It explains why QIM earned 7.26% in April, a very difficult month for nearly all money managers.

“We know that we do better in markets that are more bubbly, more speculative, with more people jumping on board and more tension. Crude oil has been that type of market and we have traded it very well in different phases. The more speculatively a market is acting, the better we trade it,” Woodriff says.

Despite its success and complexity, Woodriff’s methodology continues to evolve. “We haven’t set the trading system in stone, it is evolving slowly. If we add new models based on the same filtering process as the models that have already worked, then that should only improve performance,” Woodriff adds.

About the Author

Editor-in-Chief of Modern Trader, Daniel Collins is a 25-year veteran of the futures industry having worked on the trading floors of both the Chicago Board