Artificial intelligence (AI) has been the buzzword of the past few years but has kicked up a gear in 2025, with anything related to the latest market craze rising ever higher.
While companies are rebranding (or launching) with tech-related names in the hope of getting a slice of the pie, there has also been an unintended benefit for an investment approach that has been around for decades.
Quantitative approaches (or systematic processes) use machines to forecast future market movements and adjust portfolios accordingly.
They are not a new phenomenon, but Clive Selman, international head of sales at Federated Hermes, has noticed a considerable sea change in how investors view these vehicles in recent years.
In July, the firm Federated launched the Hermes MDT US Equity fund, a UCITS version of its US domiciled Federated Hermes MDT All Cap Core (40 ACT) fund.
It uses a quantitative approach, selecting stocks from within the Russell 3000 index by using sophisticated modelling techniques that harness machine learning to identify opportunities.
Those aren’t my words: those are the terms used in the press release that accompanied the launch in early July.
The terms chosen are interesting and, to hear Selman tell it, very deliberate.
It is part of a change in the way investors (and advisers) view these products. Some 20 years ago, when working for Man Group, he found selling these types of funds relied on convincing investors they were better than active management.
“In those days what we were saying was it was taking the human behavioural element out of investing. So you are actually making an emotionless decision,” he said.
The latest Federated Hermes launch, however, eschews this type of sales technique entirely, focusing instead on “machine learning” as its differentiator.
“That language has tailed off over the years. I feel the idea of the consistency and predictability of the outcome through the quantitative, machine-learning, AI way of doing things, is much more credible and much more acceptable to most people [nowadays],” he noted.
As a result, this type of language when referring to systematic and quantitative investing is “something you will see more of”.
Selman also noted that getting a foot in the door is also easier in the current tech-savvy climate, with investors willing to hear more about machine learning when previously they “weren’t really interested”.
“If we put out a piece of content that says we are launching a quantitative US equity fund with machine learning, based and leveraging AI to help us make more informed investment decisions, that resonates,” he noted.
While we may be some way from these strategies relaunching with names like AI-backed strategies (although I fear we may get some eventually), the tech boom in recent years has certainly improved the image of what was once a hard-to-sell vehicle.