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Multi-class Momentum Portfolio with “Canary” Crash Protection

| | Posted in: Strategic Allocation

Is it suboptimal to employ the same asset class proxy universe both to exploit momentum and to avoid crashes? In their July 2018 paper entitled "Breadth Momentum and the Canary Universe: Defensive Asset Allocation (DAA)", Wouter Keller and Jan Willem Keuning modify their Vigilant Asset Allocation (VAA) by substituting a separate "canary" asset class universe for crash protection based on breadth momentum (percentage of assets advancing). VAA is a dual momentum asset class strategy specifying momentum as the average of annualized total returns over the past 1, 3, 6 and 12 months, implemented as follows:

  1. Each month rank asset class proxies based on momentum.
  2. Each month select a "cash" holding as the one of short-term U.S. Treasury, intermediate-term U.S. Treasury and investment grade corporate bond funds with the highest momentum. 
  3. Set (via backtest) a breadth protection threshold (B). When the number of asset class proxies with negative momentum (b) is equal to or greater than B, the allocation to “cash” is 100%. When b is less than B, the base allocation to “cash” is b/B.
  4. Set (via backtest) the number of top-performing asset class proxies to hold (T) in equal weights. When the base allocation to “cash” is less than 100% (so when b<B), allocate the balance to the top (1-b/B)T asset class proxies with highest momentum (irrespective of sign).
  5. Mitigate portfolio rebalancing intensity (when B and T are different) by rounding fractions b/B to multiples of 1/T.

DAA replaces step 3 with breadth protection calculated the same way but based on a separate, simpler asset universe, selected experimentally from pre-1971 data based on a unique indicator that that combines compound annual growth rate (R) and maximum drawdown (D). The aim of DAA is to lower the average cash allocation fraction compared to VAA while preserving crash protection. They describe assets in terms of existing exchange-traded funds (ETF) but use best available matching indexes prior to ETF inceptions. Using monthly return data for alternative canary assets during 1926-1970, for backtest (in-sample) DAA universe parameter optimization during 1971-1993 and for out-of-sample DAA universe testing during 1994 through March 2018, they find that:

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