It’s been a long time since humans have been able to hold a candle against top chess programs, Kasparov famously lost to Deep Blue back in 1997 and amateurs have been losing to computers for even longer, but that doesn’t mean that people have become obsolete. Freestyle teams, which combine human and computer players, are still consistently better than machines playing on their own.
“We believe that some of the lessons of freestyle chess are useful. Properly done, a melding of fundamental and quantitative methods may well yield better results than either of them on their own,” write Credit Suisse managing director Michael Mauboussin and VP Dan Callahan in a September 10 report. “If we had to say which camp had the most to gain from the other, we’d say that the fundamental analysts have more to learn from the quants than the other way around.”
Process beats skill in freestyle chess
One of the early lessons from freestyle chess is that the skills necessary to win change dramatically as soon as you bring a machine into the mix. In 2005 Zackary Stephen and Steven Cramton, neither of whom is a chess expert by ELO ranking, beat grandmaster Vladimir Dobrov and another highly ranked player to win a freestyle tournament. Without computer assistance Stephen and Cramton wouldn’t have had a prayer, so what changed?
Stephen and Cramton were credited with having a better process that let their different chess programs do the computational heavy lifting and only interjected with human judgment when those programs suggested different moves. Dobrov relied more on his own (impressive) chess skills and didn’t make the most of the software at his disposal.
Freestyle chess: parallel with fundamental and quantitative trading
The obvious parallel that Mauboussin and Callahan are trying to draw is between fundamental and quant investment strategies, and they think both sides of the cultural divide has something to learn from the other.
Fundamental investors already use investment screens to make their search for value stocks more manageable, including quant strategies only at the beginning of the process. But one of the challenges that value investors face is updating their watch list of interesting stocks as prices and financial data shifts, and automating this step could free up even more of the fundamental investors time and mental energy for making important judgments about where the market could be off the mark.
Adding fundamental analysis to quant strategies is a bit trickier, but that doesn’t mean there’s nothing to be gained. Quant strategies tend to fail when there is a sudden regime change that challenges their assumptions, like in the 2007 sub-prime crash, that fundamental analysis may identify.