‘Does the future look enough like that past that you can count on the same models to continue to work?’
One of the investing success stories of the past decade has been “smart beta” funds, and one of the category’s standouts is Invesco’s Russell 1000 Dynamic Multifactor ETF
, often called by its ticker, OMFL.
The exchange-traded fund, which relies on a model to determine when to shift into stocks that represent broad factors such as growth and value, beat broader indexes in 2019’s upmarket — no small feat — and again in March, when everything fell apart.
But the old investing saw “past performance does not guarantee future results” may never have been so resonant as right now.
“Does the future look enough like the past that you can count on the same models to continue to work? I’m very skeptical of that,” said Dave Nadig, chief investment officer and research director for ETF Database. “I am of the opinion that we are undergoing a cataclysmic shift in the global marketplace. I’m not sure that low volatility and value investing are going to mean the same thing going forward.”
Factors are broad, but quantifiable, stock characteristics that can offer a performance roadmap. For example, quality and low-volatility tend to do better when the economy is slowing down, while momentum stocks often outperform during economic expansions.
Earlier coverage: What is factor investing?
“Smart-beta” is the category name into which funds relying on factor models usually get lumped. There are now over 900 such funds, according to ETF Database. The category hit its stride in the wake of the 2008 financial crisis, amid the crush of flows into passively-managed funds, as many investors continued to want a little extra edge.
OMFL has been around since late 2017, and charges a 29-basis point management fee.
In an interview, Invesco ETF equity strategist Nick Kalivas explained that OMFL relies on two inputs, one economic and the other a proprietary market risk appetite barometer, to determine whether markets are in one of four economic phases: recovery, expansion, slowdown, or contraction. Each phase has a corresponding factor exposure, and stocks that fit into the factors are selected from the Russell 1000 Index.
While that may sound like “active management,” Kalivas calls it “rules-based,” and the fund technically does track an index. The two data point inputs are checked once a month, and the fund and index are rebalanced “as frequently as monthly,” Invesco says.
OMFL’s secret sauce, Kalivas believes, is that its models build “bottom-up” portfolios made up of the three relevant factors in play at the time, rather than combining three separate factor groupings.
In 2019, when the S&P 500
returned 31.5%, OMFL gained 35.6%, according to FactSet data. In the year to date, it’s down a bit more, but has outperformed since the market top in mid-February, and also throughout the month of March. That’s probably because the model identified a “contraction” regime starting in February, and moved the portfolio accordingly.
In fact, inputs in February took OMFL from “recovery,” where it had been for most of the second half of 2019 through January, straight to “contraction,” bypassing “expansion” and “slowdown” altogether. That’s a jarring jump to consider in an academic sense — and even more uncomfortable in real life.
But even in the wreckage of the pandemic, with 33 million jobs lost in a matter of weeks, more than 80,000 American deaths, and deep uncertainty about what the future will hold for all kinds of sectors, Kalivas doesn’t think there’s any reason to expect factors won’t continue to hold up in the future.
“I don’t think that this event is outside the expectations of how factors would perform,” he said. “We have seen quality perform as we would expect. Late cycle momentum has performed as we would expect, based on other periods of market stress. This has been an extreme case, with a historically high volatility index
, but what we’ve seen confirms our expectations of how factors are supposed to work.”
“This is a part of the market that definitely has traction,” ETF Database’s Nadig said. “This is a time-honored strategy for quant managers and this fund did what it was supposed to do. Still, I believe it’s appropriate to be very skeptical of any quantitative model that’s relying on inputs prior to 2020.”