Adaptive Asset Allocation
The Adaptive Asset Allocation portfolio was developed by Adam Butler, Mike Philbrick, Rodrigo Gordillo, and David Varadi of ReSolve Asset Management, and introduced in their 2012 paper Adaptive Asset Allocation: A Primer, later expanded into a full book published by Wiley in 2016. The strategy combines cross-sectional momentum to identify what to own with minimum variance optimization to determine how much of each to hold -- a two-step process designed to outperform simple momentum strategies on a risk-adjusted basis.
Investment Philosophy
Each month, the strategy ranks a universe of ten global asset classes by trailing momentum and selects the top five performers. Rather than equal-weighting those five, it applies minimum variance optimization -- using recent correlations and volatility estimates -- to find the weighting that minimizes total portfolio volatility given the selected assets. The insight is that momentum identifies which assets are trending positively, while minimum variance optimization determines the portfolio weights in a way that accounts for how those assets interact with each other. The authors showed this combination delivers alpha beyond what either momentum or minimum variance alone would produce.
Who It's For
This portfolio suits quantitatively-minded investors who want a systematic, globally diversified approach that adapts to changing market conditions each month. It requires comfort with a mechanically rebalanced portfolio and a medium-to-long time horizon. The optimization step is more involved than a simple equal-weight approach, making it better suited for investors using a platform or tool that supports monthly rebalancing.
Pros
- Combines two well-researched return drivers -- momentum and minimum variance -- in a single systematic framework
- Minimum variance weighting can substantially reduce portfolio volatility compared to equal-weighted momentum strategies
- Broad ten-asset global universe covers equities, bonds, real estate, commodities, and gold
- Backed by peer-reviewed research with extensive out-of-sample testing
Cons
- More complex to implement than simple buy-and-hold or equal-weight strategies -- requires monthly covariance estimation and optimization
- Minimum variance optimization relies on historical correlations, which can shift significantly during market stress
- No cash or defensive position in the base model -- the strategy remains invested in the top five assets even during broad downturns
Technical Notes
The asset universe spans US, European, Japanese, and emerging market equities; US and international REITs; intermediate and long-term US Treasuries; commodities; and gold. The optimization uses a 126-day covariance matrix and 20-day volatility estimates. Rebalancing occurs monthly on the last trading day.
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Average Allocation
Based on historical average weights across all rebalance periods.
Performance Snapshot
Rolling Returns
| Period | Low | Average | High |
|---|---|---|---|
| 1 Year | -13.7% | +10.8% | +42.2% |
| 3 Year | -1.7% | +10.6% | +23.8% |
| 5 Year | +2.4% | +10.9% | +22.3% |
| 10 Year | +3.8% | +11.6% | +18.1% |
Growth of $10,000
Historical Drawdown
Percentage decline from the portfolio's peak value at each point in time.
Rolling Returns
Annualised return for each rolling period ending on that date.
Annualised return for each 1Y period ending on that date.