There’s a paucity of scientists who can create profitable strategies. The wizardry is hard for investors to grasp, keeping some on the sidelines. And the high costs of the technology and data are a burden to firms already suffering fee pressure from the flow of assets to passive funds.
But machine learning’s prowess in finding investing opportunities beyond the reach of humans makes the technology too alluring to ignore. Firms now use AI to prep reams of messy social media and smartphone data, forecast company earnings and sales faster than analysts, decipher the sentiment of executives from documents and create entire strategies.
“Machines will be doing more of the grunt work of discovering opportunities,” said Vasant Dhar, who 20 years ago founded one of the first machine-learning hedge funds, the $350 million Adaptive Quant Trading program at SCT Capital Management. “They can generate hypotheses, test them, and then tell humans, ‘This is interesting, go dig deeper.’ As machines add more value, it changes the nature of work humans do.”
AI strategies also have to wrestle with the assault from passive investing as BlackRock Inc. and Vanguard Group gobble up assets on their way to potentially managing $20 trillion. Index and smart-beta funds threaten to arbitrage away AI’s edge in picking value or growth stocks. But machine learning is showing it can get ahead of the passive wave and exploit patterns in markets that haven’t been discovered, almost becoming a superior version of smart beta.
Investors, fed up with years of lackluster performance by discretionary firms, are buying in. Assets in quant funds, many of which use AI, have surged by 86 percent to $940 billion since 2010. Last year, when fundamental hedge funds suffered $83 billion in outflows, quants took in $13 billion, according to Hedge Fund Research. The trend continued this year through September.
For all of AI’s power with data, its limitations are just as profound. AI lacks imagination, or the human ability to anticipate events — from political to macroeconomic — if such occurrences haven’t happened in the same way many times before. While hedge fund manager John Paulson saw the subprime mortgage meltdown coming, AI would have had no clue, because it wouldn’t have had enough relevant historical data to make comparisons and form an opinion.
A Timeline, continued ...
1990s: AI advances in machine learning, case-based reasoning, data mining, virtual reality
1997: IBM computer Deep Blue beats world chess champion Garry Kasparov
1990s: Web crawlers, other AI-based information programs, become Internet mainstays