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A Momentum and Low-Volatility Switching Strategy

Changes in market volatility may be helpful for timing exposure to momentum and low-volatility funds.

A version of this article was published in the February 2019 issue of Morningstar ETFInvestor. Download a complimentary copy of Morningstar ETFInvestor by visiting the website.

Momentum and low volatility have been remarkably effective investment strategies, despite their simplistic focus on past performance. They are also complementary. Momentum is built to deliver market-beating returns, while low volatility reduces risk. These are both good long-term strategies, but it is possible to further improve performance by tactically shifting between the two.

Low volatility tends to work the best during market downturns and in risk-off environments. In contrast, momentum should shine in trending markets with low volatility, but it has tended to struggle during market reversals and when volatility picks up. For example, at the end of the bull market, traditional momentum strategies tend to favor cyclical stocks, which get hit harder than most during market downturns. At the end of a market downturn, momentum usually favors more-defensive stocks, which tend to lag in the subsequent recovery.

Those periods are easy to recognize with the benefit of hindsight. The challenge is predicting them. There is no way to know what the future holds. But changes in market volatility can provide some insight into whether it might be a better time to favor momentum or low volatility.

Volatility Hurts Momentum Momentum's trouble with volatility isn't a secret, but it hasn't been enough to keep the strategy from succeeding over the long term. And it doesn't mean nothing can be done to address this shortcoming. For example, to mitigate this issue and reduce risk, the MSCI USA Momentum Index (which iShares Edge MSCI USA Momentum Factor ETF MTUM tracks) targets stocks with the best returns relative to their volatility. This penalizes stocks with choppy returns, as their relative performance is less likely to persist. It also reduces the index's exposure to cyclical stocks in market rallies and defensive stocks in market downturns, which should reduce the pain during market reversals. The index also has a provision that triggers a special rebalance if market volatility spikes, as the performance data that put the stocks in the portfolio at the last scheduled rebalance may be a less reliable indicator of future performance in those periods.

These adjustments help, but they don't completely solve the problem. A regression analysis revealed that a 1-percentage-point increase in the standard deviation of the market's returns over the past year was associated with a 0.14% decline in the MSCI USA Momentum Index's monthly return after controlling for its exposure to market risk (beta), based on data from December 1990 through November 2018. That figure wasn't as bad as the corresponding value for the AQR Large Cap Momentum Index (0.21%), which doesn't make any risk adjustments. But it still suggests MTUM will likely struggle in periods of high market volatility.

While an increase in market volatility hurts momentum, it doesn't appear to have a significant impact (positive or negative) on the S&P 500 Low Volatility and MSCI USA Minimum Volatility indexes, after controlling for their exposure to market risk. That said, market volatility may still indicate when it's time to get defensive, as it is a proxy for uncertainty and risk.

Volatility Persists Market volatility tends to persist in the short term, so past volatility says something about the future. Periods of high volatility are likely to be followed by more high volatility, while stable periods tend to be followed by more stability. However, this relationship is far from perfect, and it decays over time. To illustrate, I ran a regression analysis using U.S. stock market volatility over rolling 12-month periods from June 1964 through November 2018 to predict market volatility over the next one, three, six, and 12 months. The results are shown in Exhibit 1.

The regression shows that a 1-percentage-point increase in the standard deviation of the market's returns over the past 12 months was associated with a 0.41-percentage-point increase in standard deviation in the next month, all else equal. However, past volatility could explain only 27% of the variation in market volatility over the next month. So, this is not a foolproof signal; there are a lot of other things that affect market volatility. It's also notable that the explanatory power of the regressions weakens as past volatility is used to predict volatility further out. But this relationship is significant. Past volatility is a useful indicator of (near-term) future volatility.

The Strategy Given the short-term persistence of market volatility and momentum's trouble with choppy markets, I decided to test a tactical strategy that switches between momentum and low-volatility portfolios based on year-over-year changes in market volatility. Focusing on changes in volatility is useful because it shows how risk in the market is trending, making it a more sensitive indicator than the level of market volatility that should pick up on changes in risk earlier.

The strategy works like this: Each month, compare the standard deviation of the market's returns over the past 12 months (using a benchmark like the MSCI USA Index) to the corresponding figure 12 months prior. If that change is positive (volatility has increased), hold Invesco S&P 500 Low Volatility ETF SPLV, otherwise hold MTUM.

I chose to start with SPLV because it offers cleaner exposure to low-volatility stocks than optimized portfolios like iShares Edge MSCI Minimum Volatility USA ETF USMV. However, either exchange-traded fund could work as the low-volatility component of this strategy. MTUM is a good candidate to represent the momentum side because it is cheap and its focus on risk-adjusted performance should help if market volatility unexpectedly picks up.

I back-tested this strategy using data from December 1990 through December 2018 for the indexes these funds track. On paper, it worked well. The switching strategy earned higher returns and had a lower maximum drawdown than a static 50%/50% allocation to SPLV and MTUM (rebalanced monthly), as well as the market. It also exhibited lower volatility than the market (though not as low as the static 50%/50% allocation). This data is shown in Exhibit 2.

Despite the monthly refresh, this was a low-turnover strategy that spent about an equal amount of time holding the momentum and low-volatility funds. On average, it only required 1.3 switches per year. That's because volatility tends to trend. If year-over-year volatility increased in one month, chances are it will continue to increase in the next. However, it isn't necessary to check up on this strategy every month, given the short-term persistence of volatility.

I tested this strategy using quarterly, semiannual, and annual rebalancing periods, leaving everything else the same. Rebalancing quarterly worked just as well as rebalancing monthly, though the efficacy started to taper off with longer holding periods.

It is possible to make this strategy more aggressive (target higher returns with more risk) by raising the volatility threshold that would trigger a move into the low-volatility ETF. For example, rather than switching to SPLV when the year-over-year change in market volatility is positive, a more-aggressive rule might require volatility to increase by 25%. That would effectively force the strategy to own MTUM a larger portion of the time, which helps boost returns because momentum stocks tend to have higher expected returns than low-volatility stocks. Not surprisingly, that adjustment increases volatility and slightly reduces turnover.

Robustness Check Before trusting the results of any back-tested strategy, it's important to stress-test it. That means changing things that shouldn't significantly affect the results, like the specific indexes or market chosen, and verifying that the strategy still works. To do that, I swapped out SPLV's index for USMV's and replicated the test. I also paired SPLV's index with the AQR Large Cap Momentum Index in place of MTUM's benchmark. In both cases, the volatility-switching strategy, rebalanced monthly and quarterly, still produced better returns than the static 50%/50% allocation to momentum and low volatility. The results are shown in Exhibits 3 and 4.

As a further check on the strategy's robustness, I tested it in foreign developed-markets stocks using data from the MSCI World ex USA Momentum and MSCI EAFE Minimum Volatility indexes from June 1995 through December 2018. (IShares Edge MSCI International Momentum Factor ETF IMTM and iShares Edge MSCI Minimum Volatility EAFE ETF EFAV track these indexes.) Here again, the switching strategy beat the static allocation when it was updated monthly or quarterly. Exhibit 5 shows this data.

It is a little surprising that the international switching strategy and the U.S. version with the AQR index didn't beat the static allocation when rebalanced semiannually or annually. That suggests that the strategy isn't foolproof and won't always work. But it also reinforces the idea that this strategy works the best with frequent rebalancing.

Putting the Strategy Into Practice While factor-timing is difficult, using changes in market volatility to determine when to switch between momentum and low volatility appears to be a good strategy, supported by both theory and empirical evidence. However, it requires the discipline to do what the signal says, regardless of how it feels, and frequent updates (at least once per quarter), which could result in a large tax bill. If that's not your cup of tea, there's nothing wrong with maintaining a static allocation to both momentum and low volatility. As the data in this article suggests, that's likely to be a winning combination. But if you're looking for a little more, the dynamic switching strategy could be a good way to go. Right now, it says it's time to own low volatility.

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