Risk Premia Harvesting Through Dual Momentum
Risk Premia Harvesting Through Dual Momentum

Risk Premia Harvesting Through Dual Momentum

Momentum is the premier market anomaly. It is nearly universal in its applicability. This paper examines multi-asset momentum with respect to what can make it most effective for momentum investors. We consider price volatility as a value-adding factor. We show that both absolute and relative momentum can enhance returns, but that absolute momentum does far more to lessen volatility and drawdown. We see that combining absolute and relative momentum gives the best results. (Page 1)

Momentum is the tendency of investments to persist in their performance. Assets that perform well over a 3 to 12 month period tend to continue to perform well into the future. The momentum effect of Jegadeesh and Titman (1993) is one of the strongest and most pervasive financial phenomena. (Page 1)

In addition to cross-sectional or relative strength momentum, in which an asset's performance relative to other assets predicts its future relative performance, momentum also works well on an absolute, or time series, basis, in which an asset's own past return indicates its future performance (Moskowitz, Ooi and Pedersen (2012)). (Page 2)

It holds up well across multiple asset classes and back in time to the turn of the century (Hurst, Ooi, and Pedersen (2012)). Absolute momentum may also benefit relative strength momentum, since there is evidence that relative strength profits depend on the state of the market (Cooper, Guiterrez, and Hameed (2004)). (Page 2)

When we consider two assets, momentum is positive on a relative basis if one asset has appreciated more than the other has. It is possible for an asset to have positive relative and negative absolute momentum. Positive absolute momentum exists only when the excess return of an asset is positive over the look-back period, regardless of its performance relative to other assets. (Page 3)

Cross-sectional momentum researchers use long and short positions applied to both the long and short side of a market simultaneously. They are therefore only concerned with relative momentum. It makes little difference whether the studied markets go up or down, since short momentum positions hedge long ones, and vice versa. When looking only at long side momentum, however, it is desirable to be long only when both absolute and relative momentum are positive, since long-only momentum results are highly regime dependent. The goal of this paper is to show what happens when we combine relative strength price momentum with trend following absolute momentum. One way to determine absolute momentum is to see if an asset has had a positive excess return by outperforming Treasury bills over the past year. Since Treasury bill returns should remain positive over time, if our chosen asset has outperformed Treasury bills, then it too is likely to continue showing a positive future return by virtue of the transitive property. In absolute momentum, there is significant positive auto-covariance between an asset's excess return next month and its lagged one-year return (Moskowitz, Ooi, and Pedersen (2012)). (Page 4)

First, we choose between our module's non-Treasury bill assets using relative strength momentum. If our selected asset does not also show positive momentum with respect to Treasury bills (meaning it does not have positive absolute momentum), we select Treasury bills as an alternative proxy investment until our selected asset is stronger than Treasury bills. (Page 4)

Besides incorporating a safe alternative investment when market conditions are not favorable, our module approach has another important benefit. It imposes diversification on our momentum portfolio. With only absolute momentum one could construct a well-diversified permanent portfolio of multiple assets. (Page 5)

If one were to toss all assets into one large pot, as is often the case with momentum investing, and then select the top momentum candidates, even with covariance-based position sizing, all or most of the positions could be highly correlated with one another. Modules help ensure that diversified asset classes receive portfolio representation under a dual momentum framework, without having to use covariances that may be unstable or variances that may be non-stationary (Tsay (2010)) (Page 5)

Most momentum studies use either a six or a twelve-month formation (look back) period. Since twelve months is more common and has lower transaction costs, we will use that timeframe. 3 With equity returns, one often skips the most recent month of the formation period in order to disentangle the momentum effect from the short-term reversal effect related to liquidity or microstructure issues. Non-equity assets suffer less from liquidity issues. Because we are dealing with gold, fixed income and real estate, as well as equities, for consistency reasons, we rebalance all our positions monthly without skipping a month. Maximum drawdown here is the greatest peak-to-valley equity erosion on a month-end basis. (Page 6)

We first apply relative and absolute momentum to the MSCI U.S. and EAFE+ stock market indices in order to create our equities momentum module. (Page 7)

Real estate has the highest volatility over the past five years looking at the eleven U.S. equity market sectors tracked by Morningstar. Real Estate Investment Trusts (REITs) make up most of this sector. The Morningstar real estate sector index has both mortgage and equity-based REITs. We similarly use both to create our REIT module. Our final risk factor focuses on economic stress and uncertainty. For this, we use the Barclays Capital U.S. Long Treasury Bond Index and physical gold. Investors may hold these as safe haven alternatives to equities and non-government, fixed-income securities. (Page 7)

We might find additional assets by further segmenting a market or asset class. For example, we could split equities into individual countries or regions. However, greater segmentation would reduce the diversification benefits we get from using broader asset classes. Our module approach imposes a framework of portfolio diversification which reduces portfolio volatility. Our trend following, absolute momentum overlay further reduces potential downside volatility and substantially reduces maximum drawdown. (Page 22)

The first column is all nine assets without any momentum. The second column shows the same assets with an absolute momentum overlay applied to each asset. The third column shows our four modules with relative momentum, but not absolute momentum. The final column is our dual momentum module-based portfolio. (Page 26)

Our results have important practical implications for momentum investors. Using thirty-eight years of past performance data, dual momentum modules show significant performance improvement in all four areas we have examined - equities, credit risk, real estate, and economic stress, as well as with an equally-weighted composite portfolio of all the modules. The ancillary conclusions we reach are as follows: Long side momentum works best when one uses a combination of absolute momentum and relative strength momentum. Trend determination with absolute momentum can help mitigate downside risk and take advantage of regime persistence, while both relative strength and absolute momentum can enhance expected returns. (Page 33)

Investors wish to avoid high volatility yet still enjoy decent returns. There is now a propensity toward low volatility investment portfolios. However, what is undesirable is downside variability, rather than total volatility. Absolute momentum can help investors harness upside volatility and convert it into extraordinary returns (Page 34)

Focused modules can isolate and target specific risk factors. They facilitate the effective use of a hurdle rate/safe harbor alternative asset. Modules provide flexibility and diversification on a non-parametric basis, making it simple and easy to implement dual momentum-based portfolios. (Page 34)