It's almost 11 p.m. in Man Group's offices in London. The humans have all gone home.
But the computers are just waking up.
Man runs about $43 billion in assets through quantitative trading. Algorithms do most of the work, with people writing the code to build them and monitor for any anomalies after the fact. The machines are trading about 21.5 hours per day, from the open of the Asian markets to the close in the U.S.
It's a strategy the firm has been utilizing for 30 years. And now, it's seeking to be a pioneer in the next phase of quant: machine learning.
The difference between machine learning and traditional quant is that with the newer technology, the computers do not need to be told what and how to trade — they find patterns themselves and put their own buy and sell orders on accordingly.
"We view it very much as the future; it's where we're spending more money and research than any other place today," said Sandy Rattray, chief investment officer of Man Group, in an interview from the firm's London offices. "We will see it steadily increase in all areas and environments where we are less dictating what the model should do and more letting the model learn from the data on its own."
He said machine learning is already helping to deliver returns better than that of traditional quant. Rattray declined to specify the exact prevalence of machine learning in its funds today, but said he would not be surprised if in five years, half of its quant trading will be supported by machine learning.
It's not just limited to quant. Man is applying machine learning to help discretionary managers interpret data better. One example is instructing computers to listen to multiple earnings calls at a time, whereas the individual portfolio manager can only listen to one.
"Can they interpret the earnings calls as well as a human can? No," Rattray said of the machine learning-enabled computers. "But they can do a lot more. You get a lot more breadth out of the computers than you do out of the humans."