Amonth ago many computer- driven investors watched astonished as markets were wracked by a financial tempest, with once-hot stocks tumbling and previously shunned sectors enjoying a revival.
But for Michael Kharitonov, the chief executive of San Francisco-based Voleon Group, the rapid rotation out of momentum stocks that wrongfooted many investors was “boring”.
The $6bn-in-assets hedge fund hardly noticed the brief but dramatic reversal. “We saw nothing,” he says, with a chuckle.
Voleon sees itself as at the vanguard of the next wave of quantitative investing, using machine learning to unearth patterns too faint for others to detect. Machine learning is a hot field of artificial intelligence, where algorithms work in a dynamic fashion, continuously adapting to new data.
The methods of the machines can be hard to explain, even for the fund managers presented with a raft of positions. Investors in those funds cannot go to their internal investment committees and talk about how the results were obtained. According to Mr Kharitonov, the majority of the patterns “have no simple economic rationale”.
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Still, he predicts machine learning will be the next wave of innovation across the asset-management industry. “The tools are complex, but eventually others will have to move in this direction if they want to remain differentiated,” he said.
The Bank of England concurs. In a report on Wednesday on machine learning in UK financial services, it found that two-thirds of respondents already use it and that many expected to more than double the number of business areas in which they drew on it within the next three years.
Traditional trading algorithms are given a set of rules by human coders — for example, buy if a stock has climbed above its long-term moving average. But an algorithm derived from machine learning can extrapolate rules from the data itself. As a result, it learns as markets fluctuate.
This differs from mainstream quantitative investing, which primarily revolves around “factors”. These range from established longterm trends such as the tendency for cheap or trendy stocks to do well, to niche bets, such as exploiting the difference between the stock market’s actual volatility and the volatility implied by options prices.
These typically start as a hypothesis, which a quant then tests against the historical data. If it appears true they then start a new test on live data, and see if the factor, or signal, remains. Voleon, however, says it needs no economic theory to underpin its trades.
“Quants automate decisions that used to be made by humans. We automate the decisions that are made by quants,” Mr Kharitonov said.
This approach has advantages. September’s dramatic reversal in fortunes for the big quant factors knocked many investors such as Transtrend of Rotterdam and Man Group’s AHL unit. But Voleon’s main fund gained 1.2 per cent over the month, taking its gains this year to 9.2 per cent, according to people familiar with the matter.
Voleon’s approach reflects the background of its senior management. Mr Kharitonov has a PHD in computer science from Stanford, and its chief investment officer Jon Mcauliffe has a PHD in statistics from the University of California. Both worked at DE Shaw, a pioneer of quantitative investing, before founding Voleon in 2007.
