Swedroe: The Downside Of Momentum

March 01, 2017

Momentum has been found to be a persistent and pervasive factor in the returns not only of equities, but in other asset classes (including bonds, commodities and currencies). With equities (compared to the market, value, size, profitability and quality factors), during the period 1927 through 2015, momentum has earned both the highest premium (9.6%) and the highest Sharpe ratio (0.61). However, momentum has also had the worst crashes, making the strategy unappealing to investors with strong risk aversion.

Crash And Burn
Kent Daniel and Tobias Moskowitz contribute to the literature on momentum with their paper, “Momentum Crashes,” which covers the period 1927 through 2013 and appears in the November 2016 issue of the Journal of Financial Economics.

They found that “despite their strong positive average returns across numerous asset classes, momentum strategies can experience infrequent and persistent strings of negative returns.” Thus, such strategies exhibit negative skewness, a feature generally disliked by investors (and which should thus require a large premium).

For example, they found that “the two worst months for a momentum strategy that buys the top decile of past 12-month winners and shorts the bottom decile of losers are consecutive: July and August of 1932. Over this short period, the past-loser decile portfolio returned 232%, while the past-winner decile portfolio had a gain of only 32%. In a more recent crash, over the three-month period from March to May of 2009, the past-loser decile rose by 163%, while the decile portfolio of past winners gained only 8%.”

The good news was that the authors also found that these momentum crashes are partly forecastable. They occur in “panic” states (which follow market declines and occur when market volatility is high) and are contemporaneous with market rebounds. Daniel and Moskowitz show that “the low ex-ante expected returns in panic states are consistent with a conditionally high premium attached to the option-like payoffs of past losers.”

They also show that “an implementable dynamic momentum strategy based on forecasts of momentum’s mean and variance approximately doubles the alpha and Sharpe Ratio of a static momentum strategy, and is not explained by other factors. These results are robust across multiple time periods, international equity markets, and other asset classes.”


Beta And Momentum

Importantly, their research found “a good chance that the firms that fell in tandem with the market were and are high beta firms, and those that performed the best were low beta firms. Thus, following market declines, the momentum portfolio is likely to be long low-beta stocks (the past winners), and short high-beta stocks (the past losers).”

Daniel and Moskowitz verified empirically that there is dramatic time variation in the betas of momentum portfolios: “We find that, following major market declines, betas for the past-loser decile can rise above 3, and fall below 0.5 for past winners. Hence, when the market rebounds quickly, momentum strategies will crash because they have a conditionally large negative beta.” They also found that “that most of the up- versus down-beta asymmetry in bear markets is driven by the past losers.” Thus, long-only momentum strategies are not subject to such deep crashes.

Using insights from the relationship between momentum payoffs and volatility, and the fact that momentum strategy volatility is itself predictable and distinct from the predictability in its mean return, Daniel and Moskowitz designed an optimal dynamic momentum strategy that is levered up or down over time so as to maximize the unconditional Sharpe ratio of the portfolio. The strategy is based on the finding that the return of winner-minus-loser portfolios is negatively related to the forecasted return volatility of momentum.

Further Findings
The authors first showed that, to maximize the unconditional Sharpe ratio, a dynamic strategy should scale its weights, at each point in time, so that the strategy’s conditional volatility is proportional to its conditional Sharpe ratio. Then, using the insights from their analysis on the forecastability of both the momentum premium and momentum volatility, they estimated these conditional moments to generate their dynamic weights.

Daniel and Moskowitz then found “that the optimal dynamic strategy significantly outperforms the standard static momentum strategy, more than doubling its Sharpe ratio and delivering significant positive alpha relative to the market, Fama and French factors, [and] the static momentum portfolio.”

Additionally, they found that their dynamic momentum strategy significantly outperforms the constant volatility momentum strategies suggested by Pedro Barroso and Pedro Santa-Clara in their paper, which I’ll discuss.

Importantly, Daniel and Moskowitz’s findings are robust, as their study examined four different equity markets, as well as bond, commodity and currency asset classes. Across different time periods, markets and asset classes, they found remarkably consistent results. Their findings on momentum crashes are consistent with those of Barroso and Santa-Clara, authors of the study “Momentum Has its Moments,” which was published in the April 2015 issue of the Journal of Financial Economics.


They, too, found that the risk of momentum is highly variable over time and is quite predictable. Barroso and Santa-Clara found that the major source of predictability doesn’t come from a systematic risk. Instead, it’s a specific, time-varying risk.

They found management of the risk virtually eliminates crashes and nearly doubles the Sharpe ratio of the momentum strategy. They proposed a strategy of constant volatility targeting to address the issue. Daniel and Moskowitz improve on that with their dynamic weighting strategy.


Daniel and Moskowitz concluded that in “normal” markets there is consistent price momentum that is statistically and economically strong, and manifests itself across numerous equity markets and a wide range of diverse asset classes.

However, in extreme market environments following a long market downturn, when poor market conditions ameliorate and the market starts to rebound, the losers experience strong gains, resulting in a “momentum crash” as momentum strategies short these assets.

The authors found that “in bear market states, and in particular when market volatility is high, the down-market betas of the past losers are low, but the up-market betas are very large. This optionality does not appear to generally be reflected in the prices of the past losers.

Consequently, the expected returns of the past losers are very high, and the momentum effect is reversed during these times. This feature does not apply equally to winners during good times, however, resulting in an asymmetry in the winner and loser exposure to market returns during extreme times.” So they developed a dynamic weighting strategy to address this negative feature of long/short momentum strategies, one that reduces exposures when volatility increases and vice versa.

AQR Capital is an example of a mutual fund family that incorporates momentum into its strategies. It’s interesting to note that, for its long-only funds, the firm does not scale momentum exposure. However, for its long/short funds (such as the Style Premium Funds), it does. Recall that it’s the short side of momentum that is subject to crashes. (Full disclosure: My firm, Buckingham, recommends AQR funds in constructing client portfolios.)

Larry Swedroe is the director of research for The BAM Alliance, a community of more than 140 independent registered investment advisors throughout the country.


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