Quantitative asset managers once focused on giving investors systematic exposure to factors such as size and value. With the proliferation of smart beta funds, which do the same thing but at a lower cost, investors now are pushing quants to make more tactical decisions and calibrate factor exposures according to their market outlooks. Investors also want quants to look beyond the handful of well-known factors, including momentum and low volatility, and come up with new price signals.
“It’s not that quant investing has gone away,” said Harin de Silva, a portfolio manager for the Systematic Edge team at Allspring Global Investments. “But investors are saying, ‘Show me something that is active, not something that works in the long run.’” (Allspring is the new name of Wells Fargo Asset Management, which was spun out of Wells Fargo & Co. and taken private last year.) Quant approaches to portfolio risks are also in demand, according to de Silva.
There are two challenges now facing quants. One is a move away from a heavy reliance on decades of historical data and back tests to tying this in-depth research to the realities of the current economic and market environment. “We’re a lot less data dependent. And a lot more reliant on judgment. That’s a huge change,” said de Silva.
With that comes another challenge: getting the right people. Many quant managers historically hired people with expertise in data; they didn’t need to have an understanding of economics. Now it’s the background in economics and finance that’s become critical.
Quants need to answer a new set of questions. De Silva rattled off a number of potential queries: “Given where we are in the market, how should we position the portfolio? Should we be short inflation? How do you measure inflation sensitivity? And what’s the clever way to do that and how do we build it into the portfolio? You have to be more creative.”
The shift at quants is in contrast to what’s happening with fundamental asset managers, whose human stock pickers have a deep knowledge of the markets that they use to analyze specific companies and sectors. On the whole, these managers are in the early stages of integrating data science and artificial intelligence innovations to give their human portfolio managers an edge. Many of these traditional managers are fiercely competing for data and AI talent.
De Silva gave an example of the type of work Allspring’s systematic team now is doing. A client recently asked Allspring and a fundamental manager to create a portfolio around the theme of robotics. De Silva’s team started its analysis by using natural language processing to systematically “read” public documents and management discussions to determine which companies were using robotics in a way that was integral to their business strategy. “We ended up with 400 stocks, in everything from health care to trash disposal,” he said. The fundamental manager came up with 30 stocks.
Another question from investors that de Silva frequently fields concerns greenhouse gas emissions. Even though emissions are a risk to companies and every portfolio is affected, there are few commercially available risk models that factor in emissions, he said. But investors care. As a result, quants need to make sense of the approximately five years of comprehensive data on emissions that is available, even though it’s not the 30 years worth of historical information that quants like to work with. “You still have to be able to draw inferences from limited amounts of data. It’s not the data that’s the edge, it’s how you use it,” he said.
