Enhancing Returns with Momentum

One of the most well-researched and widely documented phenomena observed across global markets through vast periods of time is the effect of price or return momentum.  Much like trend following, momentum investing seeks to profit from the autocorrelated nature of asset price returns over intermediate time intervals - i.e. positive returns tend to precede more positive returns, and muted or negative returns tend to beget further underperformance over the following weeks and months.  As this is not a newly discovered market phenomenon, there are many asset managers that successfully use momentum tools to seek out higher absolute returns and improved risk reduction across their portfolios, and like these managers, we at RQA view momentum as a valuable component in certain areas of our strategy arsenal, as well. 

In what follows, we will demonstrate the fundamentals and underlying assumptions behind momentum-based strategies, how and why they tend to work, and the benefits they can provide to investor portfolios.  Additionally, we will walk through the historical application of a simple cross-sectional momentum methodology on the assets in the Traditional Portfolio, resulting in a “Momentum Portfolio”, and then analyze the results as compared to a traditional buy-and-hold approach.  As a spoiler alert, the historical performance would have been materially improved, particularly in terms of incremental absolute returns.

Basic Concepts of Momentum

As noted above, the thesis behind momentum is that asset price returns have been observed to be autocorrelated over time, particularly over short-to-intermediate term time horizons (i.e. a few weeks to several months).  The fundamental reasons behind this phenomenon are largely debated, but it is our view that a combination of psychological and behavioral biases paired with positive reinforcement in market fundamentals are typically at play.  Moreover, in our experience, sometimes strengthening asset fundamentals like revenues, earnings, market positioning, etc. act as the initial catalysts for producing positive returns, while other times, the simple fact that prices are rising (no matter how bad the fundamentals) is what drives investors to take further interest, which surprisingly in turn can benefit the asset’s underlying fundamentals, driving further return momentum and more robust investor conviction.  George Soros in his book The Alchemy of Finance refers to this dynamic as “reflexivity”, where essentially two separate and seemingly unrelated return drivers create a positive feedback loop, driving returns sequentially higher or lower depending on the directional bias of the self-reinforcing drivers.  These dynamics or reflexive processes have been observed countless times throughout history across multiple markets, asset classes, and even investment strategies.  

Using Momentum to Enhance Performance

The dynamics of price and return momentum can often have the ability to position portfolio components prior to the change in underlying fundamentals.  Throughout financial market history, it is arguably one of the most reliable and persistent phenomena that can help boost returns within a portfolio, and if executed correctly and conservatively, may even be able to help reduce risk throughout market cycles.  So how can an investor execute a momentum strategy?  Well, from our perspective, there are two main types of momentum in investing: time series momentum and cross-sectional momentum. 

Time Series Momentum

Time series momentum is essentially another method of trend following, as the historical prices or returns of a single asset over a certain period of time (or multiple periods of time) are analyzed to determine if the asset is exhibiting positive momentum (moving higher) or negative momentum (moving lower).  Trade positions are then established in the direction of each asset’s recent momentum bias in hopes that the trend will continue. 

As an example, if an investor wanted to trade a U.S. stock index using time series momentum, they could use a momentum metric such as a rolling historical return over a certain period (like the last 6 months) to tell them when to be invested in the stock index and when to be in cash. In this instance, if the return over the prior 6 months was positive, they would remain invested in the index for the month to come, while if it was negative, they would sell and return to cash. To illustrate the return behavior of this specific methodology over time, we can look to Figures 1 and 2 below, which compare the equity curves and historical performance of the time series momentum strategy outlined above and a simple buy & hold strategy on the Vanguard Total Stock Market Index (“U.S. Stocks”).

Figure 1: Buy & Hold vs. Time Series Momentum on U.S. Stocks (1995-2017)


Figure 2: Buy & Hold v. Time Series Momentum Performance on U.S. Stocks (1995-2017)


As can be observed in Figures 1 and 2 above, applying a momentum filter to U.S. Stocks between 1995 and 2017 would have provided investors with slightly higher annual returns than buy & hold, while also meaningfully protecting portfolio capital throughout some of the more substantial market corrections and recessions. More specifically, annual returns would have increased from 10% to 11%, resulting in a respective total return difference over the period of 789% vs. 1,014% (not an insignificant amount!).   More importantly from our perspective, annual volatility would have been reduced by a third, falling from roughly 15% to nearly 10%.  Lastly and most surprisingly, maximum drawdown over the period (i.e. maximum peak-to-valley decline) would have been cut by over two thirds, declining from 51% to 16%.  While momentum strategies can struggle in sideways or range-bound markets, they have historically had the tendency of rotating to cash or bonds in prolonged bear markets, as evidenced by the drastic change in the maximum drawdown statistics.  The reduction in risk in terms of annual volatility and maximum drawdown has a substantial smoothing affect, which we have found can helps investors maintain their long-term investment strategy.  Next, we will look at cross-sectional momentum and its application to the Traditional Portfolio.

Cross-Sectional Momentum

Cross-sectional momentum analyzes recent momentum metrics across an array of assets and then ranks these assets based on their relative momentum to each other.  In other words, the assets exhibiting the highest returns or positive momentum relative to the others are ranked highest, while those with the lowest returns or negative momentum are ranked lowest.  The highest ranking assets are then chosen for investment, while lower ranking assets are either not chosen for investment or even sold short.

In order to analyze this cross-sectional approach further, we can walk through a simple example using the seven asset classes that make up the Traditional Portfolio, and as a result, create a basic Momentum Portfolio.  As a reminder and baseline for comparison, the historical performance profiles for an equal-weight Traditional Portfolio and its component asset classes are depicted in Figures 3 and 4 below.

Figure 3:  Traditional Portfolio and Component Asset Class Equity Curves (1995-2017)


Figure 4:  Traditional Portfolio and Component Asset Class Performance (1995-2017)


In relation to each asset class individually, the Traditional Portfolio outperforms on a risk-adjusted basis, as can be observed in the Return/Risk Ratio of 0.79 in Figure 4.  However, a modest amount of return is foregone in order to diversify away a large amount of downside risk and volatility, particularly in relation to U.S. Stocks and Real Estate.  It is this trade-off that raises the question: “Are there any proven ways to decrease risk without giving up returns?”  This is where cross-sectional momentum seems to provide some assistance.

In order to build our Momentum Portfolio, we would take the following steps:

1)      Calculate the rolling 6-month returns for each asset class listed above at the end of every month;

2)      Rank the asset classes from highest return to lowest return; 

3)      Choose the top four ranked asset classes and place 25% of the portfolio in each for the duration of the following month.

(Rinse and repeat at the end of every month.)

Applying this methodology from 1995-2017 would have produced the following results:

Figure 5:  Traditional Portfolio v. Momentum Portfolio Equity Curves (1995-2017)


Figure 6:  Traditional Portfolio v. Momentum Portfolio Performance (1995-2017)


We can note the Momentum Portfolio has a compounded annual return of 10.4% (vs. the Traditional Portfolio’s 7.6%) between 1995 and 2017.  We can also observe the incremental improvements achieved in terms of portfolio risk, as maximum drawdown (i.e. maximum peak-to-valley decline) improved even further, coming in from roughly 31% to nearly 26%, making this portfolio potentially an easier investment approach to adhere to during weaker economic times and market stress.  Furthermore, as returns materially improved and portfolio volatility remained largely unchanged, the overall Return/Risk Ratio increased from 0.78 to 1.02, marking a substantial improvement over the Traditional Portfolio.  Lastly, we can see that the Momentum Portfolio’s correlation to U.S. Stocks of 0.59 is lower than the Traditional Portfolio’s 0.75, and therefore, it is likely that the Momentum Portfolio would act as a more robust diversification tool over time.  (It’s important to note that this historical exercise assumes 0% interest earned on positions that rotate into cash and does not account for any individual investor's tax implications, which can, as with any active strategy, affect overall returns.)

In conclusion, it is RQA’s view that many investor portfolios can achieve greater returns and potentially decrease downside risk through the conservative addition of alternative, lowly-correlated strategies such as the Momentum Portfolio illustrated above.  Also, as we’ve noted in a number of prior examples, by reforming a Traditional Portfolio and those of similar construction with multi-dimensional diversification in mind, investors have significant potential to improve the risk and return profiles of their portfolios.  Lastly, as we commonly point out following many of our basic strategy examples, the Momentum Portfolio illustrated above is a relatively simple illustration, and there are many more high-quality, low-correlation strategies and sources of return that can be incorporated into investor portfolios to improve expected results even further.

Disclaimer: These materials have been prepared solely for informational purposes and do not constitute a recommendation to make or dispose of any investment or engage in any particular investment strategy.  These materials include general information and have not been tailored for any specific recipient or recipients.  Information or data shown or used in these materials were obtained from sources believed to be reliable, but accuracy is not guaranteed.  Furthermore, past results are not necessarily indicative of future results. The analyses presented are based on simulated or hypothetical performance that has certain inherent limitations.  Simulated or hypothetical trading programs in general are also subject to the fact that they are designed with the benefit of hindsight.