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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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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“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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How lucky would a asset class picker with no skill have to be to match the performance of the Simple Asset Class Momentum Strategy (SACEMS), which each month picks winners from a set of eight exchange-traded funds (ETF) plus cash based on total returns over a specified lookback interval. To investigate, we run 1,000 trials of a “strategy” that each month allocates funds to one, the equally weighted two or the equally weighted three of these nine assets picked at random. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics. Using monthly total (dividend-adjusted) returns and for the specified assets during February 2006 (limited by DBC) through June 2019, we find that:

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The Simple Asset Class ETF Momentum Strategy (SACEMS) each month picks winners based on total return over a specified ranking (lookback) interval from the following eight asset class exchange-traded funds (ETF), plus cash:

1. PowerShares DB Commodity Index Tracking (DBC)
2. iShares MSCI Emerging Markets Index (EEM)
3. iShares MSCI EAFE Index (EFA)
4. SPDR Gold Shares (GLD)
5. iShares Russell 2000 Index (IWM)
6. SPDR S&P 500 (SPY)
7. iShares Barclays 20+ Year Treasury Bond (TLT)
8. Vanguard REIT ETF (VNQ)
9. 3-month Treasury bills (Cash)

Based on findings in “SACEMS Portfolio-Asset Addition Testing”, a subscriber proposed adding iShares JPMorgan Emerging Market Bond Fund (EMB) to this set. To investigate, we revisit relevant analyses and conduct robustness tests, with focus on the equal-weighted (EW) Top 3 SACEMS portfolio. Using monthly dividend-adjusted closing prices for asset class proxies and the yield for Cash during February 2006 (when all ETFs in the baseline universe are first available) through June 2019, we find that: Keep Reading

Can investors beat a typical active U.S. equity mutual fund via a small portfolio of periodically re-weighted equity exchange-traded funds (ETF)? In their February 2019 paper entitled “Are Passive Funds Really Superior Investments: An Investor Perspective”, flagged by a subscriber, Edwin Elton, Martin Gruber and Andre de Souza:

1. Determine via cluster analysis a small set of ETFs that captures most of the variation in 69 broad U.S. equity indexes.
2. Explore use of this set to mimic past performances of many active U.S. equity mutual funds via 24-month linear regressions with ETF coefficients scaled to sum to one.
3. Compare next-year (close of first trading day of the year after coefficient calculation to close of first trading day next year) returns of mimicking ETF portfolios and active mutual fund counterparts.

Their target set of 883 active U.S. equity mutual funds are those with at least: three years of data as of January 2003; $15 million in assets; and, 90% of assets allocated broadly to stocks. Using monthly returns for 69 U.S. equity indexes, the small set of passive equity ETFs that capture variation in these indexes and 883 active U.S. equity mutual funds during January 2003 through December 2018, they find that:

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What steps should investors consider to mitigate impact of inevitable large U.S. stock market corrections? In their May 2019 paper entitled “The Best of Strategies for the Worst of Times: Can Portfolios be Crisis Proofed?”, Campbell Harvey, Edward Hoyle, Sandy Rattray, Matthew Sargaison, Dan Taylor and Otto Van Hemert compare performances of an array of defensive strategies with focus on the eight worst drawdowns (deeper than -15%) and three NBER recessions during 1985 through 2018, including:

1. Rolling near S&P 500 Index put options, measured via the CBOE S&P 500 PutWrite Index.
2. Credit protection portfolio that is each day long (short) beta-adjusted returns of duration-matched U.S. Treasury futures (BofAML US Corp Master Total Return Index), scaled retrospectively to 10% full-sample volatility.
3. 10-year U.S. Treasury notes (T-notes).
4. Gold futures.
5. Multi-class time-series (intrinsic or absolute) momentum portfolios applied to 50 futures contract series and reformed monthly, with:
   • Momentum measured for 1-month, 3-month and 12-month lookback intervals.
   • Risk adjustment by dividing momentum score by the standard deviation of security returns.
   • Risk allocations of 25% to currencies, 25% to equity indexes, 25% to bonds and 8.3% to each of agricultural products, energies and metals. Within each group, markets have equal risk allocations.
   • Overall scaling retrospectively to 10% full-sample volatility.
   • With or without long equity positions.
6. Beta-neutral factor portfolios that are each day long (short) stocks of the highest (lowest) quality large-capitalization and mid-capitalization U.S. firms, based on profitability, growth, balance sheet safety and/or payout ratios.

They further test crash protection of varying allocations to the S&P 500 Index and a daily reformed hedge consisting of equal weights to: (1) a 3-month time series momentum component with no long equity positions and 0.7% annual trading frictions; and, (2) a quality factor component with 1.5% annual trading frictions. For this test, they scale retrospectively to 15% full-sample volatility. Throughout the paper, they assume cost of leverage is the risk-free rate. Using daily returns for the S&P 500 Index and inputs for the specified defensive strategies during 1985 through 2018, they find that:

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Considering taxes, in what order should U.S. retirees consume different sources of retirement savings/income? In their August 2018 paper entitled “Constructing Tax Efficient Withdrawal Strategies for Retirees with Traditional 401(k)/IRAs, Roth 401(k)/IRAs, and Taxable Accounts”, James DiLellio and Daniel Ostrov describe and illustrate an algorithm that computes individualized tax-efficient consumption for U.S. retirees of:

• Tax-deferred retirement accounts [Traditional IRA/401(k)].
• Post-tax retirement accounts [Roth IRA/Roth 401(k)].
• Other taxable retirement accounts.
• Other sources of money subject to income tax, including: earned income, some pensions, annuities bought with pre-tax money, earnings from annuities bought with post-tax money and sometimes Social Security benefits.
• Other sources of money that do not affect tax rates of retirement accounts, such as: tax-free gifts, Health Savings Accounts, some pensions, principal from annuities bought with post-tax money and sometimes Social Security benefits.

Their model adapts to individual retiree circumstances and accommodates typical changes in tax policies (changes in marginal rates and number of brackets). For tractability, they make simplifying assumptions. The principal simplification is that  return on stocks, stock dividend yield, inflation rate, tax brackets and rates, other income sources and consumption rates are known each year (not random variables). When the goal is to optimize a bequest, inputs also include year of retiree death, marginal tax rate of the heir and rate the heir consumes inherited retirement accounts. They do not attempt to determine the optimal mix of  stocks and bonds/cash within retirement accounts (their deterministic model would prefer all stocks). Using illustrations of algorithm outputs based on varying input assumptions, they find that: Keep Reading

Are mean-variance (MV) strategies preferable for allocations to asset classes and equal-weight (EW) preferable for allocations to much noisier individual assets? In their May 2019 paper entitled “Horses for Courses: Mean-Variance for Asset Allocation and 1/N for Stock Selection”, Emmanouil Platanakis, Charles Sutcliffe and Xiaoxia Ye address this question. They focus on the Bayes-Stein shrinkage MV strategy, with 10 U.S. equity sector indexes as asset classes and the 10 stocks with the largest initial market capitalizations within each sector (except only three for telecommunications) as individual assets. The Bayes–Stein shrinkage approach dampens the typically large effects of return estimation errors on MV allocations. For estimation of MV return and return covariance inputs, they use an expanding (inception-to-date) 12-month historical window. They focus on one-month-ahead performances of portfolios formed in four ways via a 2-stage process:

1. MV-EW, which uses MV to determine sector allocations and EW to determine stock allocations within sectors.
2. EW-EW, which uses EW for both deteriminations.
3. EW-MV, which uses EW to determine sector allocations and MV to determine stock allocations within sectors.
4. MV-MV, which uses MV for both deteriminations.

They consider four net performance metrics: annualized certainty equivalent return (CER) gain for moderately risk-averse investors; annualized Sharpe ratio (reward for risk); Omega ratio (average gain to average loss); and, Dowd ratio (reward for value at risk). They assume constant trading frictions of 0.5% of value traded. They perform robustness tests for U.S. data by using alternative MV strategies, different parameter settings and simulations. They perform a global robustness test using value-weighted equity indexes for UK, U.S., Germany, Switzerland, France, Canada and Brazil as asset classes and the 10 stocks with the largest initial market capitalizations within each index as individual assets (all in U.S. dollars). Using monthly total returns for asset classes and individual assets as specified and 1-month U.S. Treasury bill yield as the risk-free rate during January 1994 through August 2017, they find that: Keep Reading

For investors embracing the concept of portfolios based on factor premiums (rather than asset classes), what is the best factor allocation approach? In their March 2019 paper entitled “Factor-Based Allocation: Is There a Superior Strategy?”, Hubert Dichtl, Wolfgang Drobetz and Viktoria-Sophie Wendt search for the best way of combining factors in a portfolio after accounting for bias introduced from snooping many alternative allocation strategies. They consider the following 10 factors (mostly long-short) suitable for a U.S. institutional investor constrained to global equity and fixed income securities: equity, value, size, momentum, quality, low-volatility, term, real rates, credit and high-yield. They construct factors using associated published indexes denominated in U.S. dollars, with 1-month U.S. Treasury bill (T-bill) yield as the risk-free rate. They consider 17 factor allocation strategies: equal weight, minimum variance, equal risk, maximum diversification, volatility timing, reward-to-risk timing, mean-variance optimization without and with shrinkage, Black-Litterman and eight combinations of these strategies. Their test portfolio holds a 100% position in cash and a fully hedged (long-short, or zero net investment) factor portfolio, subject to 0.5% trading frictions on portfolio turnover. Using monthly data required to construct factors and T-bill yield during January 2001 though December 2018, with the first 60 months set aside to estimate strategy inputs, they find that:

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