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Learning robots are hottest weapon in the investing arms race
MAYBE machines can figure out this crazy stock market. At least that's what quantitative traders who have struggled to beat the market for years will be hoping as a band of their peers roll out computer-driven strategies that learn from their own mistakes.
Lynx Asset Management, for one, is planning a new fund in October that executes strategies thought up by a machine - an approach that helped the US$5 billion Swedish hedge fund beat most of its trend-following rivals in 2018. It won't take long for funds managed entirely by robots to be everywhere. Two to three new ones will start trading each month this year, reckons Alex Allen, who runs a fund for EFG Asset Management that invests in machine-learning strategies.
"It's the next evolution in the investing arms race," said London-based Allen, who already invests in eight machine-learning funds, and plans to add two more soon.
Machine-learning takes quant investing to the next level because the robots are programmed to adapt and improve their performance based on the data they sample over time, without needing explicit human instructions.
That tech-savvy, data-driven quants are dabbling in the field is hardly new - it's been touted as the next big thing for years, and the tools have been getting cheaper and easier to use all the while. What sets the latest flurry of activity apart is the backdrop. Whipsawing markets and the over-crowding of many quant strategies have battered their performance and started to undermine investor enthusiasm for this once red-hot corner of the investing world.
Proponents say machine-learning has the potential to give quants the winning edge they're missing, in part because it opens up the prospect of uncovering new signals in large masses of complex data to boost returns.
Yves-Laurent Kom Samo is so much a believer that he left his career at Goldman Sachs Group Inc and JPMorgan Chase & Co in 2013 to get a doctorate in machine learning at Oxford University. Now, he's getting his own business, Pit.AI, in San Jose, California, off the ground with a machine-learning hedge fund KXY Singularity, which started trading in March.
"A machine is going to be considerably faster than human beings when it comes to detecting new alphas and a machine is going to work at a scale that no human being can," Mr Kom Samo said.
Lynx Asset Management AB will unveil its new offering, the Lynx Constellation fund, on Oct 1 and it will essentially be a long-short strategy investing in futures and forwards across various asset classes. The actual strategies used could involve everything from trend-following and contrarian trading to relative-value.
That's part of the challenge in judging the effectiveness of machine learning for quantitative trading. Quants use advanced mathematical models to determine investments in what is already a huge and varied field of finance, and the application of these techniques promises even more complexity.
Machine learning itself covers a wide spectrum of methods, from traditional statistical analysis to mimicking the way neurons in the brain provide layers of learning.
"In terms of the scope of data that we can analyse, it's much greater than before," said Boyan Filev, the London-based co-head of quantitative equities at Aberdeen Standard Investments. "We're analysing billions of data points every month."
A Eurekahedge index tracking those using artificial intelligence and machine learning has outperformed the overall hedge funds gauge by 27 percentage points over the past five years. Still, because the index covers funds with a wide range of strategies, it's hard to draw broad conclusions.
Quant researchers at Societe Generale SA have developed a US equity index based on the bank's machine-learning model. The long-short gauge beats the HFRX Equity Hedge Index handily on a one- and five-year basis. But so far in 2019 it has fallen behind, so it's not quite conclusive.
In a 2017 paper on why most funds based on machine learning fail, AQR Capital Management LLC's departing head of machine learning Marcos Lopez de Prado said the "pervasive misuse" of such techniques by quants will continue to lead to false positives, losses and failures.
But, believers like Mr Kom Samo, the Oxford grad, are stepping up. "The role of the quant in the future is not going to be to find investment ideas, but to design mathematical processes to empower machines to find investment ideas," he said. BLOOMBERG