The Decision Moose Asset Allocation Framework

June 26, 2015 • Posted in Economic Indicators, Momentum Investing

A reader suggested a review of the Decision Moose asset allocation framework of William Dirlam. “Decision Moose is an automated framework for making intermediate-term investment decisions.” Decision Moose focuses on asset class momentum, as augmented by monetary policy, exchange rate and interest rate indicators. Its signals tell followers when to switch from one index fund to another among nine encompassing a broad range of asset classes, including equity indexes for several regions of the globe. The trading system is a long-only approach that allocates 100% of funds to the index “having the highest probability of price appreciation.” The site includes a history of switch recommendations since the end of August 1996, with gross performance. To evaluate Decision Moose, we assume that switches and associated trading returns are as described (out of sample, not backtested) and compare the returns to those for the dividend-adjusted S&P 500 Depository Receipts (SPY) over the same intervals. Using Decision Moose signals and performance data during 8/30/96 through 6/5/15 (nearly 19 years), we find that:

In calculating SPY total returns by Decision Moose switching interval, we assume fund switches occur at the close on Decision Moose signal dates, or at the close on the next trading day if signal dates are not trading days. The last (6/5/15) Decision Moose signal is a marked-to-market hold, while all other signals indicate fund switches.

The following table summarizes high-level gross Decision Moose results over the entire sample period (87 fund switches) and over the approximately ten and five years ending 6/5/15 (58 and 28 fund switches, respectively). Over the entire sample period (last five years), Decision Moose signals an average of about 4.6 (5.5) fund switches per year.

Performance relative to SPY is weaker over the the last five and ten years than over the entire sample period.

Including trading frictions would shave the outperformance of Decision Moose by a small percentage depending on broker fees, fund bid-ask spread and trader account size. For small accounts, this friction may be important.

For another perspective, we look at gross value of a $1 initial investment.


The following chart compares on a logarithmic scale the trade-by-trade gross cumulative values of a $1 initial investments in Decision Moose and SPY during 8/30/96 through 6/5/15. Results show that:

  • Over the entire sample period, the gross compound annual growth rate for Decision Moose (SPY) is about 20% (8.2%).
  • Decision Moose outperformance concentrates during U.S. stock market crashes. During 8/18/06 to 6/6/15, gross CAGR for Decision Moose (SPY) is about 7.2% (7.7%).
  • Decision Moose has two very large positive trades in 2001-2002 (gold) and late 2003 (Asian stocks).

Measured on switch dates, maximum drawdown for Decision Moose (SPY) is -16.4% during March 2014 to October 2014 (-42.9% during 10/5/07 through 4/10/09), but there may be larger drawdowns between signal dates.

For a more detailed trend analysis, we look at signal-to-signal outperformance relative to SPY.


The next chart summarizes the differences between Decision Moose gross signal-to-signal returns and contemporaneous SPY returns over the entire sample period, with a dashed best-fit linear trend line. The trend line indicates that Decision Moose outperformance has dissipated over the sample period.

Might the signal 18 outlier (gold during 11/24/01-6/1/02) be decisive in determining the trend?


The next chart summarizes the differences between Decision Moose gross signal-to-signal returns and contemporaneous SPY returns over the entire sample period, excluding the signal 18 outlier. The best-fit linear trend line still indicates dissipation of Decision Moose outperformance over time.

The trend in outperformance by trade could be misleading because Decision Moose trading frequency is higher for the recent subperiod than for the overall sample period. What happens to the trend if we normalize results based on outperformance per calendar day?


The following chart summarizes the differences between Decision Moose signal-to-signal gross returns and contemporaneous SPY returns per calendar day over the entire sample period. After this normalization, The best-fit linear trend line indicates marked dissipation and reversal of Decision Moose normalized outperformance over time.

A plausible interpretation of these dissipation tests is that financial markets are adapting to increasing use of momentum-based tactical asset class allocation strategies. However, sample size is not large in terms of number of SPY bull and bear markets, and a long bull market for SPY begins at about signal 56.

What does a regression show about the relationship between Decision Moose gross returns and SPY returns?


The following scatter plot presents a regression-based perspective on normalized gross Decision Moose performance over the entire sample period. Results indicate that:

  • Decision Moose generates a gross daily alpha of about 0.003% relative to buying and holding SPY. (However, over the past five years, this alpha is about -0.064%.)
  • Decision Moose has a beta of about 0.46 relative to buying and holding SPY.
  • SPY returns explain about 19% of Decision Moose returns (the R-squared statistic is about 0.19).

The latter two points indicate that Decision Moose returns are only moderately linked to U.S. stock market performance.


In his FAQs, William Dirlam suggests that Decision Moose trading is best suited to reasonably large tax-deferred accounts to minimize the impacts of trading friction and taxes. He also offers guidance on the type of investor for whom Decision Moose is suitable.

In summary, the Decision Moose asset allocation framework may offer investors a way to beat buying and holding the broad U.S. stock market over the long term by occasionally trading to the “hottest hand” (in economic context) from a set of nine asset class proxies, but its outperformance dissipates over time.

Cautions regarding findings include:

  • As noted, the sample of trades is not large for trend analysis, especially in terms of number of bull and bear equity markets.
  • Results from analysis by calendar interval would likely differ from that by trade interval.
  • As noted, Decision Moose may not work well with small accounts for which taxes are not deferred.
  • SPY may not be the most appropriate benchmark for Decision Moose, which employs nine distinct asset classes.
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