One of the investment success stories of the past decade has been smart beta funds, and one of the highlights in this category is the Invesco Russell 1000 Dynamic Dynamic Multifactor ETF
OMFL
, often called by its symbol, OMFL.
The publicly traded fund, which is based on a model for determining when to switch to stocks representing general factors such as growth and value, has outperformed broader indices in the 2019 luxury market, which is not a small matter, and again in March, when everything collapsed.
But the old investment saw that “past performance does not guarantee future results” may never have been more resonant than today.
“Does the future look sufficiently like the past to be able to count on the same models to continue to function?” I am very skeptical about this, “said Dave Nadig, director of investments and director of research at ETF Database.” I am of the opinion that we are experiencing catastrophic change in the global market. I am not sure that low volatility and a valuable investment will mean the same thing in the future. “
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Factors are broad but quantifiable market characteristics that can provide a performance roadmap. For example, quality and low volatility tend to improve when the economy slows, while momentum values often outperform during economic expansions.
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“Smart-beta” is the name of the category in which funds that depend on factor models are generally grouped. There are now more than 900 of these funds, according to the ETF database. The category grew in the wake of the 2008 financial crisis, amid an avalanche of flows to passively managed funds, with many investors continuing to want a few more benefits.
The OMFL has existed since the end of 2017 and invoices a management fee of 29 basis points.
In an interview, equity strategist Invesco ETF Nick Kalivas explained that OMFL uses two data, one economic and the other a market risk appetite barometer, to determine whether the markets are in one of the four economic phases: recovery, expansion, deceleration or contraction. Each phase has a corresponding factor exposure and the stocks corresponding to the factors are selected from the Russell 1000 index.
Although it may sound like “active management,” Kalivas calls it “rules-based,” and the fund technically tracks an index. The two data point entries are checked once a month, and the fund and index are rebalanced “on a monthly basis”. Invesco says
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Kalivas believes that OMFL’s secret sauce is that its models create bottom-up portfolios composed of the three relevant factors at play at the time, rather than combining three distinct groups of factors.
In 2019, when the S&P 500
SPX
returned 31.5%, OMFL gained 35.6%, according to FactSet data. Since the beginning of the year, it has dropped a little more, but it has outperformed since the market peak in mid-February, as well as throughout March. This is likely due to the fact that the model identified a “shrinking” regime from February and moved the portfolio accordingly.
In fact, the February inflows drove OMFL from the “recovery”, where it had been for most of the second half of 2019 until January, directly to “contraction”, completely avoiding “expansion” and slowdown”. ” It is a discordant leap to be considered in an academic sense, and even more clumsy in real life.
But even in the rubble of the pandemic, with 33 million jobs lost in a few weeks, more than 80,000 deaths of Americans and a deep uncertainty about what the future holds for all kinds of sectors, Kalivas does not believe that ‘there is a reason the expected factors will not continue in the future.
“I don’t think this event is outside expectations on how the mail carriers would work,” he said. “We have seen that quality works as expected. The dynamics at the end of the cycle unfolded as expected, depending on other periods of market stress. This was an extreme case, with a historically high volatility index
VIX
But what we have seen confirms our expectations of how the factors are supposed to work. “
“It’s part of the market that definitely has traction,” said Nadig of the ETF database. “This is a traditional strategy for quantitative managers and this fund has done what it was supposed to do. However, I think you should be very skeptical of any quantitative model based on inputs before 2020. “
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