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.