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This article was published in the October issue of Morningstar ETFInvestor. Download a complimentary copy of Morningstar ETFInvestor by visiting the website.
Market-timing is notoriously difficult. Even defining “market-timing” is tough. To some investors, market-timing is pulling out of the stock market before a crash. To others, it’s staying fully invested, but shrewdly shifting money to asset classes or sectors projected to outperform, and avoiding or underweighting those expected to underperform.
Dynamic multifactor funds try to time factor exposures to outperform static factor funds and the broader market. Timing equity factor exposures is as difficult as predicting asset class or sector outperformance. However, these three large-blend exchange-traded funds are up for the challenge:
1) Oppenheimer Russell 1000 Dynamic Multifactor ETF (OMFL)
2) Global X Adaptive US Factor ETF (AUSF)
3) PIMCO RAFI Dynamic Multi-Factor US Equity ETF (MFUS)
These funds levy low fees compared with actively managed peers and target well-vetted equity factors, but have short live track records. Each uses different factor-timing signals and tracks indexes with strong back-tested performance. This suggests that there’s more than one way to successfully time factor bets.
OMFL times its factor bets based on the current economic regime. AUSF and MFUS rely on the mean reversion of factor performance as a signal to over- or underweight factor exposure. MFUS also incorporates momentum into its timing signal. Exhibit 1 summarizes the key stats of these three funds.
History Rhymes
OMFL uses economic and market pricing data to identify whether the economy is in one of four regimes: recovery, expansion, slowdown, or contraction. The fund overweights factors that have historically outperformed during the identified economic regime. Using economic regime as a timing signal has more moving parts than a signal that uses return data like mean-reversion or momentum. It also relies on assumptions that may not always hold:
1) The economic model will continue to correctly identify the current economic regime despite the changing nature of the economy and how to appropriately measure it.
2) The historical relationship between factor performance and economic regime will continue.
According to Oppenheimer, the length of the average economic regime identified from December 2006 through November 2017 measured 6.5 months. Oppenheimer’s economic regime model changes quickly because it pairs fast-moving market sentiment indicators (rather than traditional measures of expansion and contraction) with traditional economic indicators to identify economic regime. Given that factors can underperform for long periods of time, and the model assumptions may not hold, relying on economic regime to time factors may not be effective.
Doubling Down on Value and Momentum?
AUSF and MFUS use mean reversion to time factor allocation. AUSF adjusts its portfolio quarterly using a two-year lookback period. The fund splits its allocation between the two worst-performing factors of the three that it targets, if the returns of the top-performing factor exceed the other two factors by 2.0%. If the performance difference among factor sleeves is less than 2.0%, it allocates to all three factors, but puts less weight on the best-performing factor.
This fund takes larger factor bets than its peers because it only allocates across three factors instead of five. It also doesn’t constrain its weightings but goes all in on its bets for each quarterly rebalance. This will likely increase its tracking error against the broader market. Another drawback of this fund’s signal is its short two-year look back period. It is unclear whether this is a long enough signal to pick-up mean reversion and there is a risk that it could pick up some negative momentum. Research from the the paper “Does the Stock Market Overreact?” [1] suggests that a three- to five-year lookback period is more effective at capturing performance reversals.
MFUS lengthens its mean reversion lookback period to five years and incorporates momentum into its factor-timing signal. I prefer this lengthier lookback period and am a fan of incorporating momentum into the factor-timing signal. Using more than one signal to time factor exposure is akin to spreading factor bets. It helps diversify the signal without diluting its efficacy.
Mean reversion is a form of value investing, and the risk of using mean reversion or momentum to time factor exposures is that it may double down on these factor exposures, which could reduce the diversification of the portfolio across factors. That said, the resulting return benefit may offset this risk.
To test whether these two dynamic strategies are actually doubling down on value and momentum factor exposure, I regressed the monthly excess returns of the fund’s back-filled indexes and their static counterparts with market, value, size, momentum, and quality factor returns. I also included the excess return regression for the index that OMFL tracks in this analysis.
This analysis suggests that these dynamic funds do not double down on factor exposures, as the low R-squared values and coefficients on the value and momentum variables attest. So, the timing signals appear to be providing a differentiated source of return.
Wait and See
Timing any investment strategy is difficult. There’s little evidence to determine which timing signals will work well over the long haul. But the case for multifactor funds is sound. Spreading bets across factor exposures offers diversification benefits. I’d recommend investors interested in multifactor funds start with a well-constructed static fund until dynamic multifactor funds post longer live track records.
These static multifactor funds earn Morningstar Medalist ratings:
1) DFA US Core Equity 1 (DFEOX) (0.19% fee) — Morningstar Analyst Rating of Silver
2) Goldman Sachs ActiveBeta U.S. Large Cap Equity ETF (GSLC) (0.09% fee) — Bronze
3) iShares Edge MSCI Multifactor USA ETF (LRGF) (0.20% fee) — Bronze
4) AQR Large Cap Multi-Style (QCELX) (0.45% fee) — Bronze
Disclosure: Morningstar, Inc. licenses indexes to financial institutions as the tracking indexes for investable products, such as exchange-traded funds, sponsored by the financial institution. The license fee for such use is paid by the sponsoring financial institution based mainly on the total assets of the investable product. Please click here for a list of investable products that track or have tracked a Morningstar index. Neither Morningstar, Inc. nor its investment management division markets, sells, or makes any representations regarding the advisability of investing in any investable product that tracks a Morningstar index.
Adam McCullough, CFA does not own shares in any of the securities mentioned above. Find out about Morningstar’s editorial policies.
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