Swedroe: Risk-Managed Momentum Outperforms

May 27, 2015

Momentum has been found to be a persistent and pervasive factor in the returns not only of stocks, but of other asset classes (including bonds, commodities and currencies). Compared with the market, value and size risk factors, momentum in equities has earned both the highest premium and the highest Sharpe ratio.


However, momentum has also experienced the worst crashes, making the strategy unappealing to investors with a strong risk aversion.


Pedro Barroso and Pedro Santa-Clara—authors of the study “Momentum Has Its Moments,” which appears in the April 2015 issue of the Journal of Financial Economics —found that the risks associated with momentum are highly variable over time and are quite predictable.


They determined that the major source of predictability doesn’t originate from a systematic risk. Instead, it’s a specific, time-varying risk. Furthermore, they discovered management of the risk virtually eliminates crashes and nearly doubles the Sharpe ratio (a measure of risk-adjusted returns) of the momentum strategy. And that only serves to make what the authors termed “risk-managed momentum” an even greater puzzle than the original version.


The Dark Side Of Momentum
Barroso and Santa-Clara, whose study covered the period from 1927 through 2011, found that buying winners and shorting losers has provided large returns (14.5 percent per year), with a Sharpe ratio higher than the market. This impressive excess return to momentum, its high Sharpe ratio and negative relation to other risk factors (particularly the value premium) can make the strategy appear to be a free lunch for investors. But as we mentioned earlier, there’s a dark side to momentum.


In 1932, the winners-minus-losers strategy delivered a -91.6 percent return in just two months. In 2009, momentum experienced a crash of -73.4 percent in just three months. These are the types of losses that can take decades for investors to recover from.


That said, momentum crashes result from the strategy’s short side, and they occur during reversals (such as we experienced in March 2009) after periods of sharp decline. Thus, investors using long-only momentum strategies don’t have to worry about crashes. Long-short momentum strategies, however, are exposed to crashes.


The bottom line is that the benefits of employing momentum come with risks. In particular, a high excess kurtosis (fat tail) of 18.2 combined with a pronounced left skew (where values to the left of, or less than, the mean are fewer but farther from it than the values to the right of, or greater than, the mean) of -2.5.


Trimming The Fat (Tail)
These two features of the momentum strategy mean that it carries the risk of large losses. But it’s important to not think of an asset in isolation. Instead, think about how the asset’s addition impacts the portfolio’s return as a whole. With that in mind, the study’s authors provide an important insight that investors could use to minimize fat-tail risk in momentum strategies.


Barroso and Santa-Clara concluded that momentum risk isn’t constant; rather, as we mentioned earlier, that it varies over time. The authors found if they scaled exposure to momentum (using the realized variance of daily returns over the previous six months), the risk-managed momentum strategy achieves a higher cumulative return with less risk.


The weights of the scaled momentum strategy, over time, ranged between the values of 0.13 and 2.00, reaching their most significant lows in the early 1930s, in 2000 through 2002 and in 2008 through 2009. On average, the weight was 0.90, slightly less than full exposure to momentum.


The authors concluded: “As these weights depend only on ex ante information this strategy could actually be implemented in real time.” They go on to add: “The scaled strategy benefits from the large momentum returns when it performs well and effectively shuts it off in turbulent times, thus mitigating momentum crashes. Also, the risk-managed strategy no longer has variable and persistent risk, so risk management indeed works.”



Key Findings

The following is a brief summary of their findings:

  • The risk-managed momentum strategy has a higher average return with substantially less standard deviation.
  • The Sharpe ratio of risk-managed momentum portfolios almost doubles, from 0.53 to 0.97.
  • The most important gains of the risk management strategy show up in improvement to the higher-order moments. Managing the risk of momentum drops excess kurtosis from a very high value of 18.2 to just 2.7, and it reduces left skew from -2.5 to -0.4, practically eliminating the crash risk of momentum. The scaled momentum strategy, for example, manages to preserve levels of investment in the 1930s. This compares very favorably with the pure momentum strategy, which loses 90 percent during the same period. In the 2000s, simple momentum loses 28 percent of wealth, because of the crash in 2009. Risk-managed momentum ends the decade up 88 percent, as it not only avoids the crash, but captures part of the positive returns of 2007 through 2008.
  • The turnover of risk-managed momentum is virtually the same as it is for regular momentum.
  • Risk-managed momentum also worked well in four other countries studied: the U.K., France, Germany and even Japan (where evidence for momentum has been weakest). Not only were the Sharpe ratios much improved, but the skewness and kurtosis were reduced.


Volatility Targeting

The authors provide some interesting insights into some of the momentum strategies already available in the market. For example, AQR Capital is one fund family that incorporates momentum into its strategies. It’s interesting to note that for their long-only funds, the firm doesn’t scale momentum exposure. For their long-short funds, such as the firm’s Style Premium Funds like QSPIX, they do.


Recall that only the short side of momentum is subject to crashes. AQR scales momentum by targeting a specific level of volatility, investing fewer dollars when markets are more volatile and more dollars when markets are less volatile.


Volatility targeting requires two inputs: a volatility target and a volatility forecast. The target is the fund’s desired level of risk, and the forecast is based on market risk. By dividing the target by the forecast, you calculate the allocation needed to reach the chosen level of risk. For example, if the target level of volatility is 10 percent, and the forecast is 20 percent, the fund would invest only half its assets. Such an approach has historically dampened volatility without negatively impacting returns.


While many investors believe that highly volatile markets occur because of large losses (in which case, volatility-targeting would result in selling after a drop in prices has already occurred), AQR’s research has found that empirically this is not the case; in fact, they found that the contrary holds true more often than not. Increased volatility generally precedes large drawdowns.


In a study of more than 70 liquid investments in several asset classes between 2000 and 2011, AQR analyzed how constant volatility-targeting (using rolling 21-day volatilities) changed risk and performance statistics compared with constant nominal holdings. Kurtosis, a measure of “fat-tail-ness,” declined in about 80 percent of cases. In addition, the Sharpe ratios increased in about 70 percent of cases, with the average across all assets rising from 0.32 to 0.40.

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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