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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.

Effects of Execution Delay on SACEMS

“Optimal Monthly Cycle for SACEMS?” investigates whether using a monthly cycle other than end-of-month (EOM) to pick winning assets improves performance of the Simple Asset Class ETF Momentum Strategy (SACEMS). This strategy each month picks winners from the following set of exchange-traded funds (ETF) based on total returns 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)

In response, a subscriber asked whether sticking with an EOM cycle for determining the winner, but delaying signal execution, affects strategy performance. To investigate, we compare 23 variations of SACEMS portfolios that all use EOM to pick winners but shift execution from the contemporaneous EOM to the next open or to closes over the next 21 trading days (about one month). For example, EOM+5 uses an EOM cycle to determine winners but delays execution until the close five trading days after EOM. We focus on gross compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using daily dividend-adjusted opens and closes for the asset class proxies and the yield for Cash during February 2006 (limited by DBC) through January 2019, we find that: Keep Reading

Testing the All Weather Portfolio

A subscriber requested a test of Ray Dalio‘s All Weather (AW) portfolio with different rebalancing frequencies, allocated to exchange-traded funds (ETF) as asset class proxies as follows:

30% – Vanguard Total Stock Market (VTI)
40% – iShares 20+ Year Treasury (TLT)
15% – iShares 7-10 Year Treasury (IEF)
7.5% – SPDR Gold Shares (GLD)
7.5% – Invesco DB Commodity Tracking (DBC)

To investigate, we test:

We consider the following gross performance metrics, all based on monthly measurements: average monthly return, standard deviation of monthly returns, compound annual growth rate (CAGR), maximum drawdown (MaxDD) and Sharpe ratio (with the 3-month Treasury bill yield as the risk-free rate). We also compare number of rebalance actions for each portfolio. Using monthly dividend-adjusted returns for the specified assets during February 2006 (limited by DBC) through January 2019), we find that: Keep Reading

Add Position Stop-gain to SACEMS?

Does adding a position take-profit (stop-gain) rule improve the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) by harvesting some upside volatility? SACEMS each month picks winners from the following set of exchange-traded funds (ETF) based on total returns 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 the value of stop-gains, we augment SACEMS with a simple rule that: (1) exits to Cash from any current winner ETF when its intra-month return rises above a specified threshold; and, (2) re-sets positions per winners at the end of the month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using monthly total (dividend-adjusted) returns and intra-month maximum returns for the specified assets during February 2006 (limited by DBC) through January 2019, we find that: Keep Reading

Add Position Stop-loss to SACEMS?

Does adding a position stop-loss rule improve the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) by avoiding some downside volatility? SACEMS each month picks winners from the following set of exchange-traded funds (ETF) based on total returns 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 the value of stop-losses, we augment SACEMS with a simple rule that: (1) exits to Cash from any current winner ETF when its intra-month return falls below a specified threshold; and, (2) re-sets positions per winners at the end of the month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using monthly total (dividend-adjusted) returns and intra-month drawdowns for the specified assets during February 2006 (limited by DBC) through January 2019, we find that: Keep Reading

Simple Asset Class Leveraged ETF Momentum Strategy

Subscribers have asked whether substituting leveraged exchange-traded funds (ETF) in the “Simple Asset Class ETF Momentum Strategy” (SACEMS) might enhance performance. To investigate, we execute the strategy with the following eight 2X leveraged ETFs, plus cash:

DB Commodity Double Long (DYY)
ProShares Ultra MSCI Emerging Markets (EET)
ProShares Ultra MSCI EAFE (EFO)
ProShares Ultra Gold (UGL)
ProShares Ultra S&P500 (SSO)
ProShares Ultra Russell 2000 (UWM)
ProShares Ultra Real Estate (URE)
ProShares Ultra 20+ Year Treasury (UBT)
3-month Treasury bills (Cash)

We consider portfolios of Top 1, equally weighted (EW) Top 2 and EW Top 3 past winners. We include as benchmarks: an equally weighted portfolio of all ETFs, rebalanced monthly (EW All); buying and holding SSO (SSO); and, holding SSO when the S&P 500 Index is above its 10-month simple moving average (SMA10) and Cash when the index is below its SMA10 (SSO:SMA10). Using monthly adjusted closing prices for the specified ETFs and the yield for Cash over the period January 2010 (the earliest month prices for all eight ETFs are available) through January 2019, we find that: Keep Reading

SACEMS with Risk Parity?

Subscribers asked whether risk parity might work better than equal weighting of winners within the Simple Asset Class ETF Momentum Strategy (SACEMS), which each month selects the best performers over a specified lookback interval from among the following eight asset class exchange-traded funds (ETF), plus cash:

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 focus on the SACEMS Top 3 portfolio and compare equal weighting to risk parity weights. We calculate risk parity weights at the end of each month by:

  • Calculating daily asset return volatilities over the last 63 trading days (about three months, as suggested). This step includes Cash, which has very low volatility.
  • Picking the volatilities of the Top 3 momentum winners.
  • Weighting each winner by the inverse of its volatility.
  • Scaling winner weights such that the total of the three allocations is 100%. This step essentially puts the entire portfolio into Cash when any of the Top 3 is Cash.

We use gross compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) to compare strategies. We check robustness by trying lookback intervals of one to 12 months for both momentum ranking and volatility estimation (increments of 21 trading days for the latter). Using monthly dividend-adjusted closing prices for asset class proxies and the yield for Cash during February 2006 (when all ETFs are first available) through December 2018, we find that: Keep Reading

SACEMS Based on Martin Ratio?

In response to “Robustness of SACEMS Based on Sharpe Ratio”, a subscriber asked whether Martin ratio might work better than raw returns and Sharpe ratio for ranking assets within the Simple Asset Class ETF Momentum Strategy (SACEMS), which each month selects the best performers over a specified lookback interval from among the following eight asset class exchange-traded funds (ETF), plus cash:

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 focus on the SACEMS equally weighted (EW) Top 3 portfolio and compare outcomes across lookback intervals ranging from one to 12 months for the following three asset ranking metrics:

  1. Raw return – cumulative total return over the lookback interval.
  2. Sharpe ratio (SR) – average daily excess return (asset return minus T-bill return) divided by standard deviation of daily excess returns over the lookback interval, with months approximated as 21 trading days. We set SR for Cash at zero (though it is actually zero divided by zero).
  3. Martin ratio (MR) – average daily excess return divided by the Ulcer Index calculated from daily returns over the lookback interval, with months again approximated as 21 trading days.

We employ gross compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) to compare ranking metrics. Using monthly dividend-adjusted closing prices for asset class proxies and the yield for Cash during February 2006 (when all ETFs are first available) through December 2018, we find that: Keep Reading

A Few Notes on Your Complete Guide to a Successful and Secure Retirement

Larry Swedroe and Kevin Grogan introduce their 2019 book, Your Complete Guide to a Successful and Secure Retirement, as follows: “…failure to plan is to plan to fail. While so many of us have carefully planned our education, career choices, and family responsibilities, we tend to fail to prepare a written retirement life plan that considers, among other things, our passions, financial security, charitable endeavors, relationships, intellectual stimulation, and having fun. …Having a well-thought-out plan is important. However, planning is not a one-and-done event. To be effective, plans must be living things that must be revisited whenever any of the assumptions upon which the plan was based have changed.” Based on their experience in wealth management, mortgage lending and investment banking, they conclude that: Keep Reading

SACEMS with Momentum Breadth Crash Protection

In response to “SACEMS with SMA Filter”, a subscriber suggested instead crash protection via momentum breadth (proportion of assets with positive momentum) by:

  1. Switching to 100% cash when fewer than four of eight Simple Asset Class ETF Momentum Strategy (SACEMS) non-cash assets have positive past returns.
  2. Scaling from cash into winners when four to eight risk assets have positive past returns (no cash for eight).
  3. Replacing U.S. Treasury bills (T-bills), a proxy for broker money market rates, with iShares Barclays 7-10 Year Treasury Bond (IEF) as “Cash.”

To investigate, we each month rank assets from the following SACEMS universe based on total returns over a specified lookback interval. We also each month measure momentum breadth for the eight non-cash assets using the same 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)

While emphasizing the suggested momentum breadth crash protection threshold, we look at all possible thresholds. While emphasizing a baseline lookback interval, we consider lookback intervals ranging from one to 12 months for the suggested momentum breadth threshold. We focus on compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) for the equal-weighted (EW) Top 3 SACEMS portfolio, but also look at Top 1 and EW Top 2. We also look at EW Top 3 portfolio turnover. Using monthly dividend-adjusted closing prices for SACEMS assets and IEF and the T-bill yield during February 2006 (the earliest all ETFs are available) through December 2018, we find that: Keep Reading

Coverage Ratio and Asymmetric Utility for Retirement Portfolio Evaluation

Failure rate, the conventional metric for evaluating retirement portfolios, does not distinguish between: (1) failures early versus late in retirement; or, (2) small and large surpluses (bequests). Is there a better way to evaluate retirement portfolios? In their December 2018 paper entitled “Toward Determining the Optimal Investment Strategy for Retirement”, Javier Estrada and Mark Kritzman propose coverage ratio, plus an asymmetric utility function that penalizes shortfalls more than it rewards surpluses, to evaluate retirement portfolios. They test this approach in 21 countries and the world overall. Coverage ratio is number of years of withdrawals supported by a portfolio during and after retirement, divided by retirement period. The utility function increases at decreasing rate (essentially logarithmic) as coverage ratio rises above one and decreases sharply (linearly with slope 10) as it falls below one. They focus on a 30-year retirement with 4% initial withdrawal rate and annual inflation-adjusted future withdrawals. The portfolio rebalances annually to target stocks and bonds allocations. They consider 11 target stocks-bonds allocations ranging from 100%-0% to 0%-100% in increments of 10%. When analyzing historical returns, the first (last) 30-year period is 1900-1929 (1985-2014), for a total of 86 (overlapping) periods. When using simulations, they draw 25,000 annual real returns for stocks and bonds from two uncorrelated normal distributions. For bonds, all simulation runs assume 2% average real annual return with 3% standard deviation. For stocks, simulation runs vary average real annual return and standard deviation for sensitivity analysis. Using historical annual real returns for stocks and bonds for 21 countries and the world overall during 1900 through 2014 from the Dimson-Marsh-Staunton database, they find that: Keep Reading

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