April 9, 2018

Common Misconceptions About Momentum

Momentum is one of the most researched topic in financial market literature. A search of the SSRN database on momentum will turn up around 1000 papers written over the past three years and 3000 papers in total.

With so much information available, it is not be surprising that many analysts have missed seeing some of the research. Based on the way momentum is generally used, it is clear to me that there are some serious misconceptions about it. Here is my discussion of some of the more serious ones.

1) "Momentum is best used with stocks."

Initial academic research on momentum by Jegadeesh and Titman (1993), Asness (1994), and others focused on U.S. stocks. This explains why momentum was at first associated with stock investing.

But to see if momentum was robust, researchers soon applied it to other markets. Momentum was found to be effective not only with U.S. stocks. It also outperformed with international stocks, industry groups, stock indices, bonds, real estate, commodities, and currencies.

When I started to do my own momentum research in 2010, I looked at it from a practical point of view. I tried to determine how one might best use it. I applied momentum to U.S. stocks, industry and style groups, and world regional stock indices. In 2011, I wrote a paper called “Global Momentum: A Global Cross Asset Approach.” It showed that momentum worked best with regional stock indices.

In 2015, Geczy and Samonov did a more comprehensive study called “Two Centuries of Multi-Asset Momentum (Equities, Bonds, Currencies, Commodities, Sectors, and Stocks).” They looked at momentum with country indices, government bonds, currencies, commodities, sectors, and U.S. stocks back to 1801. Momentum gave significantly positive results in all areas. Like my results, they found momentum worked best with geographically diversified stock indices.
Below is an example showing geographic stock index momentum. About half the capitalization of global equities is in U.S. stocks. The other half is, of course, in non-U.S. stocks. We will compare the performance of the S&P 500, representing U.S. stocks, to the MSCI ACWI ex-US, representing the rest of the world. 

Each month we invest in whichever of the two has had better performance during the past 12 months. We use a 12-month look back because that was found to work by Cowles and Jones in 1937. It has been used in research ever since then.  We need not worry about selection bias since we are using all areas of the world.   Data mining is not an issue, since we are using a long-established model parameter. There averages less than one trade per year. So trading impact is not an issue. Here are the results from when non-U.S. stock index data became available.

1/1971 to 3/2018
S&P 500
Annual Std Dev
Sharpe Ratio
Worst Drawdown
Results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Positions are rebalanced monthly. Please see our Disclaimer page for more information.

This simple momentum model is always invested in stocks, so there is minor tracking error. There are no trading impact issues. 

This strategy shows an increase in annual return of 160 basis points versus both the S&P 500 and a 50/50 U.S./non-U.S. allocation. Most investment managers would be happy over the long-run to outperform the market by 160 bps annually with comparable risk. Yet no one, as far as I know, is using this simple strategy.

Neither my nor Geczy and Samonov’s research took into account the price impact of trading. There is considerable controversy in this area with respect to individual stocks. See here for details. 

The most recent paper on the subject is by Patton and Weller (2017).  In What You See Is Not What You Get: The Costs of Trading Market Anomalies,” they review previous studies. Their use two different methods to do their own research on the trading impact of momentum on stocks. They conclude: “Our estimates… imply that implementation costs erode almost the entirety of the return to value and momentum strategies... momentum profits, in particular, may be out of reach for the typical asset manager.”

Despite the issue of price impact and the documented superiority of momentum used with geographic indices, nearly all momentum funds use momentum with stocks. One cannot help but wonder why this is. It is likely for the very same reasons momentum works. These include the slow diffusion of information (research results), anchoring to prior beliefs, and underreaction to new information. My experience leads me to think there is also an irrational preference for stocks over indices.  

2)  "Momentum is best used on a relative strength, cross-sectional basis."

There is now plenty of research on cross-sectional, relative momentum that compares assets to one another. All momentum research between 1993 and 2010 was of this type. It was not until 2012 that Moskowitz, Oii and Pedersen released a paper called “Time Series Momentum.”  In early 2013, I came out with a paper called “Absolute Momentum: A Simple Rule-Based Strategy and Universal Trend Following Overlay.” These papers established absolute (time-series) momentum as another type of momentum.

Absolute momentum is a form of trend following based on autocorrelation. It assumes assets that have been strong over the recent past will continue to be strong.

Absolute momentum is as universal and consistent as relative momentum. Using at least 25 years of data applied to equity index, currency and commodity futures, Moskowitz et al. showed persistence in return for absolute momentum. They found it performed best in extreme markets and had little exposure to standard asset pricing factors. This gives it considerable value as a portfolio diversifier.

My paper applied absolute momentum to stock index, bond index, real asset, stock/bond balanced, and risk parity portfolios.  It showed that absolute momentum can identify regime change and add value as both a stand-alone strategy and a portfolio overlay.

In their 2012 paper “A Century of Evidence on Trend-Following Investing,” Hurst, Oii, and Pedersen applied absolute momentum to equity indices, bond indices, commodity futures, and currencies. They found it was consistently profitable across decades ever since 1903.

The amount of research done on absolute momentum has been catching up with relative momentum. If you do a search on “time series momentum” (the preferred academic term) on SSRN, you will find over 200 papers. You might wonder now after all this research whether relative or absolute momentum gives better results.

Bird, Gao, and Yeung (2017) in their “Time Series and Cross-Sectional Momentum Strategies Under Alternative Implementation Strategies” applied relative and absolute long/short momentum to stocks in 24 developed markets from 1990 to 2012. They found positive returns from both forms of momentum under alternative implementations. But they concluded that “time-series momentum is clearly superior,” and “momentum is best implemented using time-series momentum.”

D’Souza, Srichanachaichok, Wang, and Yao (2017) in their “The Enduring Effect of Time-Series Momentum on Stock Returns Over Nearly 100-Years”, studied long/short absolute momentum in U.S. stocks from 1927 to 2014 and in international stocks since 1975. They found absolute momentum could entirely account for relative momentum. Absolute momentum performed well in both up and down markets. Unlike relative momentum, absolute momentum did not suffer from January losses and market crashes.

Despite the evidence in favor of absolute momentum, almost every momentum fund uses just relative momentum. The reasons for this may be the same as to why investors prefer momentum with stocks instead of indices – the slow diffusion of research results and anchoring that overweights prior information. But there may also be other reasons. First, there is a long-standing bias against tactical approaches such as trend following. What many don’t realize is all momentum is a form of trend following. Relative momentum looks at trends between assets. Absolute momentum looks at the trend of an asset itself over time

Another reason absolute momentum has not been well received may be its tracking error, especially during bull markets. Absolute momentum is known to outperform in bear markets. But in bull markets whipsaw losses and trading lags can constrain the performance of absolute momentum portfolios. That is why some advisors use trend following only for satellite positions within diversified portfolios. 

There is no way to eliminate all tracking error. But I show in “Why Does Dual Momentum Outperform” how dual momentum can lead to superior long-run performance in both bull and bear markets. Relative momentum can boost returns during bull markets. This can compensate for the whipsaw losses and performance lags of absolute momentum. Absolute momentum can reduce the downside exposure of relative momentum during bear markets. In my 2012 paper, I introduced the concept I called dual momentum. 

Based on the evidence above, if I had to choose between relative and absolute momentum, you would most likely choose absolute momentum. But there is no reason you cannot use both. D’Souza et al. looked at both forms of momentum, as well as dual momentum. They found that dual momentum applied to long-short stock portfolios generated striking returns of 1.88% per month.

3)  "Momentum (trend following) is not as reliable as diversification in reducing risk."

The first thing to understand is that momentum is all about diversification. Momentum diversifies by time as well as by asset class. This makes it adaptive to changing market conditions. Traditional fixed diversification creates a drag on performance from poorer performing assets. Momentum reduces performance drag by being only in assets that are performing well.

Here is an example comparing the performance of momentum versus a diversified fixed portfolio. For the fixed portfolio we will use Ivy 5 developed by Meb Faber. Ivy 5 holds equal size positions in indices of U.S. stocks, foreign developed stocks, intermediate bonds, REITs, and commodities.

For momentum, we will use the Global Equities Momentum (GEM) model featured in my book. GEM uses 3 assets and holds only one at a time. It decides whether to be in stocks or bonds based on absolute momentum. When stocks are selected, it chooses either the S&P 500 or the MSCI ACWI ex-U.S. based on relative momentum.

Stocks have the highest risk premium of any asset class. That is why we want to be in them as much as possible, providing their trend is positive. As you can see, dual momentum has done a much better job in reducing tail risk and improving risk-adjusted returns.

1/1973 to 4/2018
Annual Std Dev
Sharpe Ratio
Worst Drawdown
Results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Please see our Disclaimer and Performance pages for more information.

There are some who criticize trend following and systematic trading approaches because of what they call "timing luck". With some strategies, results vary a lot depending on what day of the month you rebalance positions. This uncertainty causes them to trade portions of their portfolios at different times during the month (tranching). It also leads them to doubt the efficacy of systematic trading and put more emphasis on traditional diversification

Tranching may have merit in some situations. But that is not always the case.

Swinkels and van Vliet (2010) showed that stocks exhibit a statistically significant turn-of-the-month effect. The last trading day of the month and first few trading days of the next month outperform other days. This may be due to institutional portfolio rebalancing at or just before month-end. This rebalancing replaces poor performing stocks with better performing ones. It is sometimes called "window dressing". Momentum means persistence in performance. We want to be in portfolios after better performing stocks have replaced the laggards. This lets momentum work in our favor.

Our dual momentum models, which are most of the time in stock indices, perform better by rebalancing on the first or second trading day of the month. This lets us exploit the turn-of-the-month momentum effect to our advantage.

AllocateSmartly is a service that tracks systematic trading models after modifying some of them. Their reporting of our GEM model understates its performance by 90 basis points annually. This is because they substitute the MSCI EAFE index for the broader MSCI ACWI ex-US index that we use. We avoid selection bias by using a non-US index of developed and emerging markets, not just developed ones.

AllocateSmartly's analysis of what they refer to as GEM does have some value. They evaluated performance over the past 363 months based on which normalized trading day of the month one uses to rebalance positions.  

Source: www.allocatesmartly.com

We see that the first and second trading days of the month have the highest Sharpe and Sortino ratios. These days are consistent with the turn-of-the month effect. We use them for our portfolio rebalancing.

If investors and advisors studied more of the literature on momentum and trend following, they would surely be impressed. There is nothing else that comes close in terms of favorable results and longevity.

Besides the research mentioned above, here are a few other studies reinforcing these points. Clare et al. (2014) in “Size Matters: Tail Risk, Momentum, and Trend Following in International Equity Portfolios” look at 20 developed and 12 developing countries from 1995 through 2013. They find limited evidence for the outperformance of relative momentum stock portfolios. Trend following though is observed to be a very effective strategy delivering superior risk-adjusted returns.  

Geczy and Samonov (2015) show the effectiveness of absolute as well as relative momentum on 200 years of data. Lemperiere et al. (2014) in “Two Centuries of Trend Following,” look at trend following across commodities, currencies, stock indices, and bonds since 1800. They find “the existence of trends one of the most significant anomalies in financial markets.” Their results were very stable across time and asset classes.

For those wanting even more history, Greyserman and Kaminsky (2014) in Trend Following with Managed Futures take trend following back 800 years! When applied to 84 different markets, absolute momentum showed a Sharpe ratio of 1.16 versus 0.47 for buy-and-hold. The worst drawdown of trend following was 25% less than buy-and-hold. The duration of its longest drawdown and the average duration of the longest five drawdowns were 90% and 80% shorter. They found trend following to have a low correlation with traditional asset classes, interest rate regimes, and inflation. It has also provided consistently positive performance during crisis periods.


The above are serious misconceptions about momentum that I hope I have cleared up. Feel free to pass along this information to all who might benefit from it. Those who neglect momentum and trend following are missing out on opportunities that one could only dream about in days past.