Common asset allocation strategies (like the TAA strategy) are based on the so-called "momentum anomaly", which is known for centuries. The gist of the momentum anomaly is that assets often continue their price momentum, defined as the change in price over a given lookback period. Therefore one should buy assets with the highest momentum and sell assets with the lowest momentum.
FAA incorporates new momentum factors into risk regime determination. Next to the traditional momentum factor (R) based on the Relative returns among assets, Keller and Van Putten introduced Generalized Momentum by adding these new factors: Absolute momentum (A), Volatility momentum (V) and Correlation momentum (C). In their paper Keller and Van Putten demonstrated that by expanding the traditional momentum approach, portfolio performance increases compared to the buy and hold benchmark, both in terms of return as well as risk.
Summarizing FAA, Keller and Van Putten present their strategy with an example universe of 7 index funds. Applying a 4 month lookback, from this universe at the end of each month the top 3 assets are selected through a nested ranking process of these 7 assets based on relative momentum (higher is better), volatility (lower is better) and correlations (lower is better). Last, each of the top 3 assets chosen, has to pass the absolute momentum test: if their absolute momentum is negative, just go into cash. Capital is equally allocated over the top 3 assets or if applicable into cash.
FAA was scrutinized by Empiritrage. In their full report following findings are reached:
FAA has significantly higher risk-adjusted return than an equal weight portfolio. FAA decreases maximum drawdown dramatically. FAA is robust when adjusting look-back periods. Absolute momentum can directly add value on identifying down side risk regimes and decrease maximum drawdown.
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| Source: Empiritrage |
The FAA paper example 7 index fund universe consists of 3 assets representing global stocks (VTSMX, FDIVX, VEIEX) covering US, EAFE and EM regions, 2 assets for US bonds (VFISX, VBMFX) and a commodity and REIT index fund (QRAAX, VGSIX). In this basket the Vanguard Short-Term Treasury Fund VFISX is also used as cash proxy.
The core element of FAA is the nested generalized ranking function:
First for each index fund separate ranks for momentum, volatility and correlation are determined using a 4 month (84 trading days in the thinkscript port) lookback horizon. Next those ranks are combined in a generalized ranking function where ranks for momentum, volatility and correlation are weighted and then again sorted. Last, the top 3 ranked assets are tested against their absolute momentum. For weighting the authors arbitrarily set wR* = 1, wV = 0.5, and wC = 0.5. Portfolio rebalancing happens at the first trading day of every new month.
MVC_i = (wM * MR_i) + (wV * VR_i) + (wC * CR_i)
where
wM, wV and wC are weight factors for momentum, volatility and correlation
MR_i: momentum rank, with highest = 1 and lowest =7
VR_i: volatility rank, with highest = 7 and lowest = 1
CR_i: correlation rank,with highest = 7 and lowest = 1
Since FAA provides these relatively simple linear functions to combine relative and absolute momentum with volatility- and correlation-momentum, porting the strategy into thinkscript became ... inevitable.
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| Combining ranks for momentum, volatility and correlation in a generalized ranking function |
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| Historical overview of FAA ranking |
(* In order to prevent ex aequo's in the final ranking, the thinkscript logic increases the weight for momentum by 0.001 in the generalized ranking function.)
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| Steady equity growth with limited volatility |
NB! Load the FAA suite on the price chart of the universe's asset with the shortest available price history in TOS otherwise the equity curve will not paint properly! Check price history on a separate weekly chart beforehand.
ashProtection.png) |
| C(r)ash Protection during the 2008 market meltdown |
As proposed by the authors instead of index funds, FAA is suitable for a universe of ETF's too.
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| FAA with universe of ETF's (with longer backtest horizon in TOS) |
For interested readers (and coders) Mike Guan generously shared his FAA source code for "R" at SystemeticEdge. Using Yahoo! price quotes overcomes the limited backtest horizon in TOS. Mike's "R" code also generates a very nice performance summary:
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| Not a single year with negative YoY results |
To conclude the discussion on FAA, all used thinkscript code is available. The code in the comment section is only intended for review. For your convenience download the studies from Google Dive:
Starting right now studies are published on TrendXplorer's Google Drive.
Please share insights and comments through the Disqus forum below.
[Edit November 24, 2013: ranking range corrected in text and comments from 12 to 7. "R" stats added.]
[Edit November 29, 2013: FAA_Equity studies updated with Sharpe/Sortino and on/off switch.]
