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Strategic Allocation

Is there a best way to select and weight asset classes for long-term diversification benefits? These blog entries address this strategic allocation question.

A Few Notes on Heads I Win, Tails You Lose

Patrick Donohoe introduces his 2018 book, Heads I Win, Tails You Lose: A Financial Strategy to Reignite the American Dream, by stating that the book: “…will teach you many of the principles and strategies to help discover your own path to financial freedom. Most importantly, it will show you the mindset required to carry out a successful plan. …almost everything you will gain from this book conflicts with what the typical financial planner, financial celebrity, and most financial publications tell you to do. …You will…discover how to pivot the foundation of your wealth to…the private mutual insurance company.” Based on his experience, market research and many examples, he concludes that: Keep Reading

Multi-class Momentum Portfolio with “Canary” Crash Protection

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: Keep Reading

A Few Notes on The Geometry of Wealth

Brian Portnoy introduces his 2018 book, The Geometry of Wealth: How To Shape A Life Of Money And Meaning, by stating that the book is: “…a story told in three parts,…from purpose to priorities to tactics. Each step has a primary action associated with it. The first is adaptation. The second is prioritization. The third is simplification. …The principle that motors us along the entire way is what I call ‘adaptive simplicity,’ a means of both rolling with the punches and and cutting through the noise.” Based on his two decades of experience in the mutual fund and hedge fund industries, including interactions with many investors, along with considerable cited research (much of it behavioral), he concludes that: Keep Reading

SACEVS with Quarterly Allocation Updates

Do quarterly allocation updates for the Best Value and Weighted versions of the “Simple Asset Class ETF Value Strategy” (SACEVS) work as well as monthly updates? These strategies allocate funds to the following asset class exchange-traded funds (ETF) according to valuations of term, credit and equity risk premiums, or to cash if no premiums are undervalued:

3-month Treasury bills (Cash)
iShares 20+ Year Treasury Bond (TLT)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR S&P 500 (SPY)

Changing from monthly to quarterly allocation updates does not sacrifice information about lagged quarterly S&P 500 Index earnings, but it does sacrifice currency of term and credit premiums. To assess alternatives, we compare cumulative performances and the following key metrics for quarterly and monthly allocation updates: gross compound annual growth rate (CAGR), gross maximum drawdown (MaxDD) and annual returns and volatilities. Using monthly dividend-adjusted closes for the above ETFs during September 2002 (earliest alignment of months and quarters) through June 2018, we find that:

Keep Reading

Alternative Momentum Metrics for SACEMS?

A subscriber asked whether some different momentum metric might improve performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS), which each month reforms a portfolio of winners from the following universe based on total return over a specified lookback interval:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 2000 Index (IWM)
SPDR S&P 500 (SPY)
iShares Barclays 20+ Year Treasury Bond (TLT)
Vanguard REIT ETF (VNQ)
3-month Treasury bills (Cash)

To investigate, we compare performances of the following alternative monthly momentum metrics to that of the original baseline metric:

  • Average monthly total returns over the lookback interval.
  • Slope of the dividend-adjusted price series over the lookback interval.
  • Sharpe ratio of the monthly total return series over the lookback interval (using Cash return as the risk-free rate, and setting the Sharpe ratio of Cash at zero).

We focus on the equally weighted (EW) Top 3 SACEMS portfolio. We consider all the performance metrics used for the baseline, with emphasis on compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly dividend adjusted closing prices for the asset class proxies and the yield for Cash over the period February 2006 (the earliest all ETFs are available) through May 2018 (148 months), we find that: Keep Reading

SACEVS and SACEMS from a European Perspective

A European subscriber asked about the effect of the dollar-euro exchange rate on the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS). To investigate, we each month adjust the gross returns for these strategies for the change in the dollar-euro exchange rate that month. We consider all strategy variations: Best Value and Weighted for SACEVS; and, Top 1, equally weighted (EW) Top 2 and EW Top 3 for SACEVS. We focus on SACEVS Best Value and SACEMS EW Top 3. We consider effects on four gross performance metrics: average monthly return; standard deviation of monthly returns; compound annual growth rate (CAGR); and, maximum drawdown (MaxDD). Using monthly returns for the strategies and monthly changes in the dollar-euro exchange rate since August 2002 for SACEVS and since August 2006 for SACEMS, both through April 2018, we find that: Keep Reading

Testing a Countercyclical Asset Allocation Strategy

“Countercyclical Asset Allocation Strategy” summarizes research on a simple countercyclical asset allocation strategy that systematically raises (lowers) the allocation to an asset class when its current aggregate allocation is relatively low (high). The underlying research is not specific on calculating portfolio allocations and returns. To corroborate findings, we use annual mutual fund and exchange-traded fund (ETF) allocations to stocks and bonds worldwide from the 2018 Investment Company Fact Book, Data Tables 3 and 11 to determine annual countercyclical allocations for stocks and bonds (ignoring allocations to money market funds). Specifically:

  • If actual aggregate mutual fund/ETF allocation to stocks in a given year is above (below) 60%, we set next-year portfolio allocation below (above) 60% by the same percentage.
  • If actual aggregate mutual fund/ETF allocation to bonds in a given year is above (below) 40%, we set next-year portfolio allocation below (above) 40% by the same percentage.

We then apply next-year allocations to stock (Fidelity Fund, FFIDX) and bond (Fidelity Investment Grade Bond Fund, FBNDX) mutual funds that have long histories. Based on Fact Book annual publication dates, we rebalance at the end of April each year. Using the specified actual fund allocations for 1984 through 2017 and FFIDX and FBNDX May through April total returns and April 1-year U.S. Treasury note (T-note) yields for 1985 through 2018, we find that: Keep Reading

SACEVS and SACEMS Performance by Calendar Month

A subscriber asked whether the Simple Asset Class ETF Momentum Strategy (SACEMS) exhibits monthly calendar effects. In investigating, we consider also the Simple Asset Class ETF Value Strategy (SACEVS)? We focus on: (1) the “Best Value” version of SACEVS, which each month picks one of three exchange-traded funds (ETF) corresponding to the most undervalued of U.S. term, credit and equity risk premiums (or cash if none of the three premiums are undervalued); and, (2) the “EW Top 3” version of SACEMS, which each month equally weights the top three of nine ETFs/cash with the highest total returns over a specified lookback interval. Using monthly total returns for SACEVS Best Value asset selections since August 2002 and for SACEMS EW Top 3 asset selections since August 2006, all through March 2018, we find that:

Keep Reading

Putting Strategic Edges and Tactical Views into Portfolios

What is the best way to put strategic edges and tactical views into investment portfolios? In their March 2018 paper entitled “Model Portfolios”, Debarshi Basu, Michael Gates, Vishal Karir and Andrew Ang describe and illustrate a three-step optimized asset allocation process incorporating investor preferences and beliefs that is rigorous, repeatable, transparent and scalable. The three steps are: 

  1. Select a benchmark portfolio matched to investor risk tolerance via simple combination of stocks and bonds. They represent stocks with a mix of 70% MSCI All World Country Index and 30% MSCI USA Index. They represent bonds with Barclays US Universal Bond Index. In their first illustration, they focus on 20-80, 60-40 and 80-20 stocks-bonds benchmarks, rebalanced quarterly.
  2. Construct a strategic portfolio with the same expected volatility as the selected benchmark but generates a higher long-term Sharpe ratio by including optimized exposure to styles/factors expected to outperform the market over the long run. Key inputs are long-run asset returns and covariances plus a risk aversion parameter. In their first illustration, they constrain the strategic model portfolio to have the same overall equity exposure and regional equity exposures as the selected benchmark.
  3. Add tactical modifications to the strategic portfolio by varying strategic positions based on short-term expected returns and risks. In their second illustration, they employ a 100-0 stocks-bonds benchmark consisting of 80% MSCI USA Net Total Return Index and 20% MSCI USA Minimum Volatility Net Total Return Index. The corresponding strategic portfolio reflecting long-term expectations is an equally weighted combination of value, momentum, quality, size and minimum volatility equity factor indexes. They specify short-term return and risk expectations based on four indicators involving: economic cycle variables; aggregate stock valuation metrics; factor momentum; and, dispersion of factor measures (such as difference in valuations between value stocks and growth stocks). They apply these indicators to underweight or overweight strategic positions using an optimizer. They rebalance these portfolios monthly. 

For their asset universe, they focus on indexes accessible via Exchanged Traded Funds (ETFs). Using monthly data for five broad capitalization-weighted equity indexes, six broad bond/credit indexes of varying durations and six style/factor (smart beta) equity indexes as available during January 2000 through June 2017, they find that: Keep Reading

“Pulling the Goalie” Metaphor for Investors

Can sacrificing little goals satisfy bigger ones? In the March 2018 draft of their paper entitled “Pulling the Goalie: Hockey and Investment Implications”, Clifford Asness and Aaron Brown ponder when a losing hockey coach should pull the goalie as a metaphor for focusing on portfolio-level return and portfolio-level risk management. Based on statistical analysis of hockey scenarios and broad examples from investing, they conclude that: Keep Reading

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