Bloomberg last week talked to several fund managers and consultants about why AI-powered investment funds still need humans at the wheel.
Ksenia Galouchko filed this report for Bloomberg:
The main problem is financial market data, according to Bryan Kelly, head of machine learning at $194 billion 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 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.
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, Société Générale’s long-short U.S. stock index based on a machine-learning model has been lagging this year, with a return of less than half that of HFRX. The Eurekahedge Artificial Intelligence Hedge Fund Index, which tracks hedge funds that use machine learning, has also underperformed in 2019: Its gain of 2.3% through Aug. 31 trailed the 6.9% return for the broader HFRX Index.