Observers noted the machine-learning capabilities of traditional money management firms significantly trail the technological prowess of quantitatively managed hedge fund firms such as AQR Capital Management LLC, D.E. Shaw Investment Management LLC, GAM Systematic, Man Group PLC, Renaissance Technologies LLC, Two Sigma Investments LP and Winton Group Ltd., which collectively pioneered the use of artificial intelligence-fueled investment processes over the past two decades.
Critical advances over the past few years in computing power, newly available vast pools of data, and "very clever mathematical algorithms" are reasons traditional money managers and companies in other industries are rushing to adopt machine learning, said Matthew Killeya, head of research, Cantab Capital Partners LLP, Cambridge, England.
He stressed, however, the application of machine learning processes to investment management is very different from other industries because of the "very noisy" attributes of financial markets.
"There is much less agreement about structural patterns in global markets. It's much harder to recognize patterns than in other industries," Mr. Killeya added.
Cantab is part of GAM Systematic, which used machine learning to manage $4.7 billion in systematic quantitative strategies as of Dec. 31.
The common definition of machine learning is a computerized system that uses algorithms to detect and act on repeatable patterns it learns from large sets of data without human instruction about what patterns to find.
In the investment management context, at the simplest level, managers want to access, scrub and wrangle huge pools of data and let their algorithms spot patterns that will lead to better predictive pricing of stocks and bonds, said observers.
The massive data sets available for analysis span the spectrum from basic market and economic data to data from social media sources, satellite imagery, written documents and weather forecasts.
"We find that machine learning is very powerful in taking large unstructured data and simplifying it into something with more structure that we can more easily interpret," said Osman Ali, managing director and a portfolio manager on the quantitative investment strategies team of Goldman Sachs Asset Management, New York.
"You need a foundational, economically motivated belief for why something makes sense to invest in, and then you can see how analysis of big data can help in confirming or disproving that belief," Mr. Osman said.
GSAM managed a total of $1.29 trillion as of Dec. 31, of which $135 billion was managed by QIS in quantitative equity strategies and $35 billion of that was managed using machine learning.
Sources said the primary use of machine learning by traditional managers is for stock and bond analysis that provides portfolio managers with more precise information about securities from a much larger data universe than was previously available at a much faster pace than human analysts could ever achieve.
However, sources said only a handful of money managers are using machine learning meaningfully in portfolio construction, including Acadian Asset Management LLC, AlphaSimplex Group LLC, AQR Capital Management LLC, BlackRock, Cerebellum Capital LLC, GAM Systematic, Man Group, J.P. Morgan, Two Sigma Investments and Vanguard.
Casey Quirk's Mr. Levi observed that for most firms, machine learning is still in the early stages of being applied at the portfolio management level, noting "new data requires looking at portfolio construction through a new lens."
He did stress, however, that machine learning applications for true portfolio construction are on managers' radar, given "the huge opportunities" for managers in solutions-based strategies.
For example, forward-looking manager research now is focused on using machine-learning processes to break down return streams into their underlying components and recombine them in customized portfolios focused on specific factors to meet specific investor needs, Mr. Levi said.