Christopher Clack is sitting in a noisy university cafeteria and what he really wants to talk about is sex. In this case, however, he means sex between consenting computer programs.
Clack is describing to Automated Trader his research in genetic programming at University College London in order to determine what combinations of factors will form the best financial models.
Genetic programming - or GP - is a Darwinian evolution technique that creates an artificial version of survival of the fittest, where solutions (programs) that work well get to survive and have 'sex' with other good solutions to create a child solution that inherits characteristics from both parents. The successful parents and the children stay in the computational gene pool to battle it out again with other solutions in future generations. Solutions that prove less successful simply die off. Since sex and death are two drivers of evolution, the idea of GP is that you can 'evolve' your way to optimum models - after thousands of simulated generations, the computational gene pool is full of very successful solutions.
Clack, who runs a financial computing programme, is one of a growing number of academics and technology executives who are hunting for successful ways to use artificial intelligence methods in finance. It's a far cry from the pulpy plotline of 'Fear Index' by Robert Harris or the ghost-in-the-machine scare tactics of Hollywood blockbusters such as 'I Robot'. The reality in the halls of academia and meeting rooms of tech firms is much less dramatic, with progress coming in fits and starts and resembling nothing like the sleek images of science fiction thrillers. But these researchers are optimistic that they are making genuine headway, even if many don't even like using the much-hyped term AI.
"AI, artificial intelligence, as a term is problematic for two reasons," Clack said. "First, it's been around for too long with too many links to science fiction, overblown expectations and disappointments. Second, it means too many different things to different people - even amongst scientists there are deep divisions between different types of AI research."
Whatever baggage may come with the term, there's little doubt that some companies are serious about AI's potential to make or save investors' money, either by generating trading signals or on the execution side.
Soldiers of fortune
Tucker Balch, like Clack, has an academic pedigree. In addition to an executive role at a tech company he co-founded, he's an associate professor in computational finance at Georgia Tech. Years ago he was working on a project commissioned by the US military.
"What I had built was a system for the Department of Defense that allows a soldier in the field to drive a robot around with a remote control and the robot observes what the soldier's telling it to do," Balch said.
"It thinks, 'Hmmm, I see this set of obstacles in front of me and the human drove me this way'. It remembers all that and then later when it's allowed to run by itself, it sees what's in front of it, looks up in its memory to find the closest situation it has seen before and does what the soldier had told it to do before."
Getting the robot to learn from the past was one breakthrough. Seeing what that meant in other fields was another. "I realised about six years ago that these same sorts of algorithms also make perfect sense for trading and investing."
Fast forward, and he is now chief technology officer of Lucena, which is marketing three AI-based products. One of them generates five-, 10 and 20-day price forecasts for about 13,000 equity symbols in North America. The product uses the same sort of technology as the robotic companion of the soldier in the field.