Machines are starting to take the place of the people who flip burgers, drive across town and, lately, manage stock portfolios.
Artificial intelligence is taking on a bigger role in making investment decisions.
A.I., including an ability to analyze data and actually learn from it, is considered useful in executing certain investing models, such as high-frequency trading, and in helping fund managers with tasks that rely on gathering and interpreting reams of information. Going a step further, an exchange-traded fund introduced in October uses A.I. algorithms to choose long-term stock holdings.
It is to early to say whether the E.T.F., A.I. Powered Equity, will be a trendsetter or merely a curiosity. Artificial intelligence continues to become more sophisticated and complex, but so do the markets. That leaves technology and investment authorities debating the role of A.I. in managing portfolios. Some say it will only ever be a tool, valuable but subordinate to its flesh-and-blood masters, while others envision it taking control and making decisions for many funds.
“We are just beginning to see a rise of the machines in investment management,” said Campbell Harvey, a professor of finance at Duke University. Although, he said, “it’s hard to define what the markets will look like” if human judgment is usurped, he predicted that “in the end, it will be a good thing for investors.”
Artificial intelligence is a term that may be spoken more than understood. Many investment firms use software to sift through data and perform rudimentary analysis by following fairly simple rules. The programs can create portfolios by screening universes of stocks to select ones that meet criteria related to corporate results, valuation metrics or trading patterns, or by tweaking the proportions of the constituent companies in an index based on certain factors
Large fund management companies like Fidelity and Vanguard say they use A.I. for a range of purposes, but they decline to be specific.
BlackRock says it relies on it for heavy cognitive lifting, often by scouring data to tease out patterns that might remain obscure to human eyes and brains. Examples offered by Jessica Greaney, a company spokeswoman, include identifying and trying to exploit nonintuitive relationships between securities or market indicators, perusing social media “to gain insights on employee attitudes, sentiment and preferences,” and monitoring search engines for words being entered on particular topics, say cars or luxury goods.
These algorithms play a supporting role in BlackRock’s investing, Ms. Greaney said. Decisions on what to buy and sell are made by living, breathing managers.