The Decision Moose Asset Allocation Framework

July 1, 2014 • 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 the 77 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 data for the 81 trades spanning 8/30/96 through 4/11/14 (about 18.5 years), we find that:

In calculating SPY total returns by Decision Moose trading interval, we assume trades 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 following table summarizes gross Decision Moose trading results over the entire sample period (81 trades) and over the approximately ten and five years ending 4/11/14 (57 and 29 trades, respectively). Over the entire sample period (last five years), Decision Moose signals an average of about 4.3 (5.8) trades per year.

It appears that performance has weakened over time.

Including trading frictions would shave the outperformance of Decision Moose by a small percentage depending on specific 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 the trade date-by-trade date gross cumulative values of a $1 initial investments in Decision Moose and SPY during 8/30/96 through 4/11/14. Results show that:

  • As reasonably expected, Decision Moose outperformance concentrates during U.S. stock market crashes.
  • Decision Moose has two very large positive trades in 2001-2002 (gold) and late 2003 (Asian stocks).

Measured on trade dates, maximum drawdown is -13.3% during October 2009 to May 2010, but there may be larger drawdowns between trade dates.

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


The next chart summarizes the differences between Decision Moose (Moose) gross returns and SPY returns by trade 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.

The sample size of 81 trades is not large. Might the Trade 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 returns and SPY returns by trade over the entire sample period, excluding the Trade 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 recent Decision Moose trading frequency is higher for the recent subperiod than for the overall sample period. What happens to the trend if we normalize trading results based on outperformance per calendar day?


The following chart summarizes the differences between Decision Moose gross returns and SPY returns per calendar day by trade 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, and a long bull market for SPY begins at about trade 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 0.01% relative to buying and holding SPY. (However, over the past five years, this alpha is about -0.07%.)
  • Decision Moose has a beta of about 0.42 relative to buying and holding SPY.
  • SPY returns explain about 18% of Decision Moose returns (the R-squared statistic is about 0.18).

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.
  • 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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