Millburn Ridgefield Corp. placed robots at the very heart of its open systematic strategies after a six-year experiment. Now, the New York firm is raising cash for a new computer-powered strategy trading single-name stocks.
Millburn is banking on artificial intelligence as it moves away from its 1970s-era tradition in trend following, which typically uses futures contracts to surf the momentum of assets. Co-CEO Barry Goodman said statistical-learning programs scanning a broader set of data can figure out the nervous system connecting markets. That’s how the firm plans to beat the increasingly crowded world of quantitative investing.
Systematic traders of all stripes are investing in machines designed to improve without explicit human instruction in order to get ahead of the pack.
Millburn’s new equity fund will use machine learning to decipher signals from exchange-traded funds in order to make long bets on the underlying securities such as members of the S&P 500 and MSCI World. The algorithm, for example, might discover that momentum trades work best during seasonal shifts in volatility — something often buried in masses of data.
“Figuring this out is not trivial, and not something humans could do,” Goodman said.
The Ai buzzword encompasses a wide range of techniques. To skeptics, it all remains untested, complex and prone to humanlike pitfalls. But that is not stopping a herd of money managers betting computers will uncover patterns undetected by the human eye. JPMorgan Chase’s asset management arm is planning a strategy to invest in statistical-arbitrage hedge funds powered by machines that learn. Berkeley, Calif.-based Voleon Group, which depends on the technology for trading, has seen assets in its hedge fund double to $5.1 billion in the year through June 1.
Millburn hasn’t given up entirely on trend following despite its pursuit of systematic strategies with a more macro and cross-asset tilt. But with more and more cash chasing the same quant strategies, Goodman reckons it’s time to shake things up.
“Positions began to look more similar across various trading firms and we had what from time-to-time could be pretty significant crowding,” he said. “This meant less diversification for investors and potentially higher volatility.”
Lee works for Bloomberg News.