Strategic Allocation

When a retirement portfolio veers from its planned path, is it better to count on reversion-to-path or adjust the plan? In his March 2019 paper entitled “Managing to Target: Dynamic Adjustments for Accumulation Strategies”, Javier Estrada employs a simple retirement portfolio model to compare outcomes for sticking to the plan (S2P) with 13 dynamic strategies of three types:

1. Five effective but impractical (EBI) dynamic contribution strategies. EBI1 at the end of each year contributes to or withdraws from the portfolio so that it stays exactly on track. EBI2, EBI3, EBI4 and EBI5 are similar but limit annual adjustments to no more than 5%, 10%, 15% and 20% above or below the prior-year contribution, respectively.
2. Five feasible but limited (FBL) dynamic contribution strategies. FBL1, FBL2, FBL3, FBL4 and FBL5 also at end of each year contribute to or withdraw from the portfolio to help keep it on track, but limit changes to no more than 5%, 10%, 15%, 20% and 50% (FBL5) above or below the initial plan contribution, respectively.
3. Three dynamic asset allocation (AA) strategies that every five years make portfolio asset allocations more aggressive (conservative) when the portfolio is below (above) plan. AA1, AA2 and AA3 limit changes in asset class allocations to 10%, 20% and 30%, respectively, compared to allocations five years ago.

His model portfolio consists of 39 annual contributions over 40 years, with 5% annualized real return (the historical average for 60% stocks and 40% bonds) and target value $1 million at retirement. He evaluates portfolio performance over 80 possible 40-year periods over 118 years. Using annual real (based on the U.S. Consumer Price Index) total returns for the S&P 500 Index and 10-year U.S. Treasury notes during 1900 through 2017, he finds that:

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We have updated monthly allocations and performance data for the Simple Asset Class ETF Momentum Strategy (SACEMS) and the Simple Asset Class ETF Value Strategy (SACEVS). We have also updated performance data for the Combined Value-Momentum Strategy.

We have updated the Trading Calendar to incorporate data for November 2019.

The home page, Simple Asset Class ETF Momentum Strategy (SACEMS) and Simple Asset Class ETF Value Strategy (SACEVS) now show preliminary positions for December 2019. For SACEMS, the top four positions are in close competition, so the top three may change by the early close. For SACEVS, allocations are unlikely to change.

What happens if we extend the “Simple Asset Class ETF Value Strategy” (SACEVS) with a real estate risk premium, derived from the yield on equity Real Estate Investment Trusts (REIT), represented by the FTSE NAREIT Equity REITs Index? To investigate, we apply the SACEVS methodology to the following asset class exchange-traded funds (ETF), plus cash:

3-month Treasury bills (Cash)
iShares 20+ Year Treasury Bond (TLT)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR Dow Jones REIT (RWR) through September 2004 dovetailed with Vanguard REIT ETF (VNQ) thereafter
SPDR S&P 500 (SPY)

This set of ETFs relates to four risk premiums, as specified below: (1) term; (2) credit (default); (3) real estate; and, (4) equity. We focus on the effects of adding the real estate risk premium on Compound annual growth rates (CAGR) and Maximum drawdowns (MaxDD) of the Best Value (picking the most undervalued premium) and Weighted (weighting all undervalued premiums according to degree of undervaluation) versions of SACEVS. Using lagged quarterly S&P 500 earnings, monthly S&P 500 Index levels and monthly yields for 3-month U.S. Treasury bill (T-bill), the 10-year Constant Maturity U.S. Treasury note (T-note), Moody’s Seasoned Baa Corporate Bonds and FTSE NAREIT Equity REITs Index during March 1989 through August 2018 (limited by availability of earnings data), and monthly dividend-adjusted closing prices for the above asset class ETFs during July 2002 through September 2019, we find that: Keep Reading

The Simple Asset Class ETF Momentum Strategy (SACEMS) each month picks the one, two or three of nine asset class proxies with the highest cumulative total returns over a specified lookback interval. A subscriber proposed instead using the optimal intrinsic (time series or absolute) momentum lookback interval for each asset rather than a common lookback interval for all assets. SACEMS and the proposed approach represent different beliefs (which could both be somewhat true), as follows:

• Many investors adjust asset class allocations with some regularity, such that behaviors of classes are important and coordinated.
• Many investors switch between specific asset classes and cash with some regularity, such that each class may exhibit distinct times series behavior. 

To investigate, we consider two ways to measure intrinsic momentum for each asset class proxy:

1. Correlation between next-month return and average monthly return over the past one to 12 months. The lookback interval with the highest correlation has the strongest (linear) relationship between past and future returns and is optimal.
2. Intrinsic momentum, measured as compound annual growth rate (CAGR) for a strategy that is in the asset (cash) when its total return over the past one to 12 months is positive (zero or negative). The lookback interval with the highest CAGR is optimal.

We use the two sets of optimal lookback intervals (optimization-in-depth) to calculate momentum for each asset class proxy as its average monthly return over its optimal lookback interval. We then compare performance statistics for these two alternatives to those for base SACEMS, focusing on: gross CAGR for several intervals; average gross annual return; standard deviation of annual returns; gross annual Sharpe ratio; and, gross maximum drawdown (MaxDD). Using monthly dividend-adjusted prices for SACEMS asset class proxies during February 2006 through September 2019, we find that:

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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), annual gross returns and volatilities and annual gross Sharpe ratios. Using monthly dividend-adjusted closes for the above ETFs during September 2002 (earliest alignment of months and quarters) through September 2019, we find that:

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“SACEMS-SACEVS for Value-Momentum Diversification” finds that the “Simple Asset Class ETF Value Strategy” (SACEVS) and the “Simple Asset Class ETF Momentum Strategy” (SACEMS) are mutually diversifying. Do longer samples available from “SACEVS Applied to Mutual Funds” and “SACEMS Applied to Mutual Funds” confirm this finding? To check, we look at the following three equal-weighted (50-50) combinations of the two strategies, rebalanced monthly:

1. SACEVS Best Value paired with SACEMS Top 1 (aggressive value and aggressive momentum).
2. SACEVS Best Value paired with SACEMS Equally Weighted (EW) Top 3 (aggressive value and diversified momentum).
3. SACEVS Weighted paired with SACEMS EW Top 3 (diversified value and diversified momentum).

Using monthly gross returns for SACEVS and SACEMS mutual fund portfolios during September 1997 through July 2019, we find that:

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Are the “Simple Asset Class ETF Value Strategy” (SACEVS) and the “Simple Asset Class ETF Momentum Strategy” (SACEMS) mutually diversifying. To check, we look at three equal-weighted (50-50) combinations of the two strategies, rebalanced monthly:

1. SACEVS Best Value paired with SACEMS Top 1 (aggressive value and aggressive momentum).
2. SACEVS Best Value paired with SACEMS Equally Weighted (EW) Top 3 (aggressive value and diversified momentum).
3. SACEVS Weighted paired with SACEMS EW Top 3 (diversified value and diversified momentum).

We also test sensitivity of results to deviating from equal SACEVS-SACEMS weights. Using monthly gross returns for SACEVS and SACEMS portfolios since January 2003 for the first strategy and since June 2006 for the latter two, all through July 2019, we find that: Keep Reading

“Simple Asset Class ETF Value Strategy” (SACEVS) finds that investors may be able to exploit relative valuation of the term risk premium, the credit (default) risk premium and the equity risk premium via exchange-traded funds (ETF). However, the backtesting period is limited by available histories for ETFs and for series used to estimate risk premiums. To construct a longer test, we make the following substitutions for potential holdings (selected for length of available samples):

• Monthly average 3-Month Treasury Bill (T-bill) Secondary Market Rate instead of monthly average 3-Month T-bill Constant Maturity Rate as the risk-free rate and return on Cash.
• Vanguard GNMA Investor Shares (VFIIX) instead of  iShares 20+ Year Treasury Bond (TLT).
• Vanguard Long-Term Investment Grade Investor Shares (VWESX) instead of iShares iBoxx $ Investment Grade Corporate Bond (LQD).
• Vanguard 500 Index Fund Investor Shares (VFINX) instead of SPDR S&P 500 (SPY).

To enable estimation of risk premiums over a longer history, we also substitute:

• Monthly average Moody’s Seasoned Baa Corporate Bond Yields rather than day before end-of-month Baa yields for calculation of the credit risk premium. This substitution ignores a one-day delay in release of daily data.
• Robert Shiller’s S&P Composite Index monthly average levels instead of S&P 500 Index monthly closes. This substitution ignores any delay in posting of Shiller data (but new data are available elsewhere in real time).
• Robert Shiller’s monthly S&P Composite Index (GAAP) earnings instead of S&P Dow Jones S&P 500 operating earnings. We lag the Shiller earnings by four months to ensure real-time availability. In other words, the earnings yield for a month is the S&P Composite Index level for that month divided by index annual GAAP earnings as of four months ago. GAAP earnings are generally lower than operating earnings (see “Stock Market Valuation Ratio Trends”).
• Robert Shiller’s monthly average Long Interest Rates instead of monthly average yields on 10-year Constant Maturity U.S. Treasury notes. This substitution ignores any delay in posting of Shiller data (but new data are again available elsewhere with little delay).

As with ETFs, we consider two alternatives for exploiting premium undervaluation: Best Value, which picks the most undervalued premium; and, Weighted, which weights all undervalued premiums according to degree of undervaluation. Based on the assets considered, the principal benchmark is a monthly rebalanced portfolio of 60% VFINX and 40% VFIIX. Using monthly risk premium calculation data during March 1934 through July 2019 (limited by availability of T-bill data), and monthly dividend-adjusted closing prices for the three asset class mutual funds during June 1980 through July 2019 (39 years, limited by VFIIX), we find that:

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The “Simple Asset Class ETF Value Strategy” (SACEVS) seeks diversification across a small set of asset class exchanged-traded funds (ETF), plus a monthly tactical edge from potential undervaluation of three risk premiums:

1. Term – monthly difference between the 10-year Constant Maturity U.S. Treasury note (T-note) yield and the 3-month Constant Maturity U.S. Treasury bill (T-bill) yield.
2. Credit – monthly difference between the Moody’s Seasoned Baa Corporate Bonds yield and the T-note yield.
3. Equity – monthly difference between S&P 500 operating earnings yield and the T-note yield.

Premium valuations are relative to historical averages. How might this strategy react to changes in the Effective Federal Funds Rate (EFFR)? Using end-of-month values of the three risk premiums, EFFR, total 12-month U.S. inflation and core 12-month U.S. inflation during March 1989 (limited by availability of operating earnings data) through June 2019, we find that: Keep Reading