Dynamic Asset Allocation for Practitioners, Part 2: Multi-Asset Momentum
In our last post, we covered the importance of a well-designed investment universe as a precondition for thoughtful diversification. In this second article on Dynamic Asset Allocation for Practitioners, we will explore several methods for measuring price momentum to compare and contrast their utility under different portfolio concentration and asset universe specifications.
What is momentum?
Momentum is the tendency for an asset’s price to continue in its current direction. There have been countless studies of this effect in virtually every market around the ranging from vanilla stocks and bonds to real estate and fine art (and everything in between). Furthermore, research shows the momentum effect has existed since at least the thirteenth century.
Academics have presented myriad explanations for why this phenomenon is so universal, but our preferred explanation involves human behavior. Specifically, in uncertain situations, humans quite logically take cues from each other about how they should act. Agent models of this behavior always manifest in informational cascades that, when applied to capital markets, may create momentum effects.
The identification of human behavior as the driving force behind momentum is strengthened by prospect theory, which states that investors feel the pain of realized losses far more acutely than joy of realized gains. As a corollary, investors tend to hold depreciating assets to avoid the pain of realized loss, while quickly selling appreciating assets to lock in small wins. This dissonant trading behavior creates downward pressure on appreciating assets, causing them to take longer to achieve their fair value. Of course, it also creates an opportunity for momentum investors to harvest the gains left unclaimed by the herd.
These deeply-engrained behavioral shortcomings make momentum a “premiere anomaly” that expresses useful and actionable signals.
The balance of this series will present ways to harness the momentum effect across global asset classes. The strategies we present will be long-only because such approaches harness two major sources of return: the long-term premia derived from exposure to risky assets (as opposed to cash), and the multi-asset momentum factor itself. In later articles, we will demonstrate that about half the performance of Adaptive Asset Allocation strategies are derived from consistent exposure to long-only global risk premia, with the balance attributable to active momentum bets.
Overfitting via Investment Universe, Concentration, and Rebalancing Frequency
To revisit the main themes from Part 1, recall that our investment universe is composed of the following 12 global asset classes representing all major asset categories and economic regions of the world. Our goal is to capture the major muscle movements of the global economy, and monetary dynamics.
- Commodities (DB Liquid Commodities Index)
- Gold Bullion
- U.S. Stocks (S&P 500)
- European Stocks (FTSE Europe Index)
- Asia Pacific Stocks (MSCI Asia Pacific)
- Emerging Market Stocks (FTSE EM)
- Global REITs (Dow Jones Global REITs Index)
- Intermediate Treasuries (Barclays 7-10 Year Treasury Index)
- Long Treasuries (Barclays 20+ Year Treasury Index)
- Intermediate International Government Bonds (Unhedged)
- USD Denominated Emerging Market Bonds
- Long-Term TIPs
The investment universe can itself serve as a source of “curve fitting,” as it is easy (not to mention tempting) to alter the universe of potential investments for the purpose of improving simulation results. For example, if one or two assets happen to have done particularly well over our test horizon (U.S. equities, anyone?), or happen to have been particular “trendy.” a simulation’s success may be more attributable to a lucky investment universe than robust selection methods. And finally, holding periods may introduce both frequency and “trading day” biases.