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
Those programs may be useful, but they are not A.I. because they are static; they do the same thing over and over until someone changes them. A.I. involves machine learning, in which a program updates itself as new information comes in. Whatever goal the program was created to achieve remains the same, but the problem-solving tools it uses keep changing and reflect the sum of the information it has to work with.