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Artificial intelligence isn’t ready to take fund manager jobs yet

Artificial intelligence has penetrated almost every area of our lives, from online customer support to facial recognition to self-driving cars

ET Bureau|
Oct 03, 2019, 10.28 AM IST
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Artificial intelligence
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 $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.

Artificial intelligence has penetrated almost every area of our lives, from online customer support to facial recognition to self-driving cars. But investing is proving to be one of the toughest challenges for machine learning.

The main problem is financial market data, according to Bryan Kelly, head of machine learning at $194 billion AQR Capital Management LLC. 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 says. “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.”

People who try to predict the stock market or interest rates using AI might end up with flawed analysis that can lead to financial losses, warns Seth Weingram, director of client advisory at $97 billion Acadian Asset Management. “You see market-naive folks who are trying to apply these techniques get into trouble,” he says. “There’s a risk that you don’t actually have enough data to meaningfully train your algorithm.”

What’s being touted as a revolution has been used by quantitative whizzes for years. Almost all quant funds use machine learning to sweep through social media, news articles, and earnings reports.

PanAgora Asset Management, a $45 billion quant fund based in Boston, has been creative in using natural language processing to analyze Chinese equities. Its machine-learning tool spiders through online forum posts by retail Chinese traders and identifies cyber slang words they use to avoid government censors, who might crack down on negative language, such as discussions of poor earnings results. Canny Chinese bloggers, for example, replace the word “rubbish” with a phonetically similar expression, “spicy chicken.” PanAgora’s model identifies such similar-sounding words and the context in which they appear to gauge sentiment about Chinese companies.

AI isn’t ready to take fund manager jobs yet
PanAgora is also 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.

One reason Aberdeen Standard and others are turning to robots for help is the recent market environment. Investors are fretting over the end of the bull market as trade tensions and an inverted yield curve flash warning signs for global growth. But they’re afraid to exit too early and miss out on late-cycle returns.

Yet swings in investor sentiment are hard for machines to navigate, too. “If the market becomes unpredictable, it’s always more challenging for AI,” says Anand Rao, global artificial intelligence lead at consulting firm PwC.

“This time around, there are different forces acting. But [the collapse of the credit market bubble in] 2007 was also very different, and so was [the end of the dot-com bubble in] 2000. With more data and more history, AI funds will get better.”

So far, machines seem befuddled by these markets. After outperforming the Hedge Fund Research HFRX Equity Hedge Index in four of the last five years, Societe Generale SA’s long-short US stock index based on a machine-learning model has been lagging this year, with a return of less than half that of HFRX.

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