NZH: fund managers beating AI-driven strategies so far

How good is artificial intelligence at managing money? To judge by the recent performance of some AI-driven strategies, it doesn’t look like the robots are going to take over from the humans anytime soon.

In August 2018, a quantitative team at Aberdeen Standard Investments started a US$10 million Artificial Intelligence Global Equity Fund, betting that an algorithm can be more effective at figuring out the complex world of factor investing than a human portfolio manager. A year later, the fund had underperformed the broader stock market’s powerful rally, and its assets had grown only 8 per cent. Institutional investors say they’ll hold off committing money until they see a longer track record.

The main problem is financial market data, according to Bryan Kelly, head of machine learning at US$194 billion ($307b) AQR Capital Management. Market data-unlike photos or road traffic information or chess games-is finite, and the algorithms can learn only from past performance. “This isn’t like a self-driving car where you can drive the car and generate enormous amounts of additional data,” Kelly told New Zealand Herald.

“The dual limitation of very noisy data and not a lot of it in financial markets means that it’s a big ask to want the machine to identify on its own what a good portfolio should look like without the benefit from human insight.”

PanAgora Asset Management, a $45 billion quant fund based in Boston, is looking at using AI to execute trades and spot accounting abnormalities that a simple analysis wouldn’t find. “We have tons of data [on the execution of trades], and now instead of making all these individual decisions using anecdotal evidence from the trading desk, we can make a much more quantitative decision given past results,” says George Mussalli, equities chief investment officer at PanAgora.

Boyan Filev, co-head of quantitative equity at Aberdeen Standard, says the advantage of utilizing machine learning to manage a portfolio is that it adapts to the market and improves over time. The fund’s underperformance, he contends, is mainly the result of challenging markets and changing behavior of so-called equity factors, which have led to losses at many quant funds in 2019.

“Our fund was positioned more defensively at the start of the year in line with the bear market of the end of 2018. However, the sharp reversal of equity markets this year hasn’t been particularly helpful,” Filev says. “A more stable and slower-evolving environment is more beneficial to our product. Very sharp reversals in market directions are very hard to position against in the short term.”

Filev expects the fund to adapt to conditions, he says. It just hasn’t done so yet.

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