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%. 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 infor-
mation 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 quanti-
tative 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.
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 frettingGood News, Fund Managers. AI Isn’t
Ready to Take Your Jobs—Yet
By KSENIA GALOUCHKO
Investing
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