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.

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Add REITs to SACEVS?

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 four asset class exchange-traded funds (ETF), plus cash:

3-month Treasury bills (Cash)
iShares 7-10 Year Treasury Bond (IEF)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR Dow Jones REIT (RWR)
SPDR S&P 500 (SPY)

This set of ETFs relates to four factor risk premiums: (1) the difference in yields between Treasury bills and Treasury notes/bonds indicates the term risk premium; (2) the difference in yields between corporate bonds and Treasury notes/bonds indicates the credit (default) risk premium; (3) the difference in yields between equity REITs and Treasury notes/bonds indicates the real estate risk premium; and, (4) the difference in yields between equities and Treasury notes/bonds indicates the equity risk premium. We consider two alternative strategies 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% stocks and 40% U.S. Treasury notes (60-40 SPY-IEF). Using lagged quarterly S&P 500 earnings, end-of-month S&P 500 Index levels and end-of-month yields for the 3-month Constant Maturity 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 April 2016 (limited by availability of earnings data), and daily dividend-adjusted closing prices for the above four asset class ETFs during July 2002 through April 2016 (166 months, limited by availability of IEF and LQD), we find that: Keep Reading

Mean-Variance Asset Allocation for Individual Investors

Can individual investors practically implement mean-variance optimization in a multi-asset class context? In their April 2016 paper entitled “Asset Allocation: A Recommendation for Resolving the Collision between Theory and Practice”, Larry Prather, James McCown and Ron Shaw describe how individual investors can construct and maintain a low-cost optimal (maximum Sharpe ratio) multi-class portfolio via the Excel Solver function. They consider four criteria in selecting asset class proxies: (1) market capitalization-weighted coverage of a wide variety of investable assets; (2) small initial investment; (3) low annual expenses; and, (4) versions that investors can short. Based on these criteria, they select five Vanguard index mutual funds and three precious metals:

  • Vanguard Total Stock Market Index Fund Investor Shares (VTSMX), capturing the U.S. equity market.
  • Vanguard Total International Stock Index Fund Investor Shares (VGTSX), representing 98% of the capitalization of non-U.S. equity markets.
  • Vanguard Emerging Markets Stock Index Fund Investor Shares (VEIEX), supplementing VGTSX to better capture emerging market equities.
  • Vanguard Total Bond Market Index Fund Investor Shares (VBMFX), providing broad exposure to U.S. investment grade bonds.
  • Vanguard REIT Index Fund Investor Shares (VGSIX), providing broad exposure to U.S. Real Estate Investment Trusts (REIT).
  • Spot gold, platinum and palladium, offering safe haven and currency exchange rate protection.

These mutual funds and metals have exchange-traded fund (ETF) analogs, supporting optimization with short selling. They assume a constant risk-free rate of 3%. Using daily mutual fund returns and spot metals prices during September 1998 through June 2015, they find that: Keep Reading

Integrating Value and Momentum Stock Strategies, with Turnover Management

Is there a most practical way to make value and momentum work together across stocks? In the April 2016 version of their paper entitled “Combining Value and Momentum”, Gregg Fisher,  Ronnie Shah and Sheridan Titman examine long-only stock portfolios that seek exposure to both value and momentum while suppressing trading frictions. They define value as high book-to-market ratio based on book value lagged at least four months. They define momentum as return from 12 months ago to one month ago. They consider two strategies for integrating value and momentum:

  1. Each month, choose stocks with the highest simple average value and momentum percentile ranks. They suppress turnover with buy-sell ranges, either 90-70 or 95-65. For example, the 90-70 range adds stocks with ranks higher than 90 not already in the portfolio and sells stocks in the portfolio with ranks less than 70. 
  2. After initially forming a value portfolio, each month buy stocks only when both value and momentum are favorable, and sell stocks only when both are unfavorable. This strategy weights value more than momentum, because momentum signals change more quickly than value signals. For this strategy, they each month calculate value and momentum scores for each stock as percentages of aggregate market capitalizations of other stocks with lower or equal value and momentum. They suppress turnover with a 90-70 or 95-65 buy-sell range, but the range applies only to the value score. There is a separate 50 threshold for momentum score, meaning that stocks bought (sold) must have momentum score above (below) 50.

They consider large-capitalization stocks (top 1000) and small-capitalization stocks (the rest) separately, with all portfolios value-weighted. They calculate turnover as the total amount bought or sold each month relative to portfolio size. They consider two levels of round-trip trading frictions based on historical bid-ask spreads and broker fees: high levels (based on 1993-1999 data) are 2.94% for small stocks and 1.06% for large stocks; low levels (based on 2000-2013 data) are 0.82% for small stocks and 0.41% large stocks. They focus on net Sharpe ratio as a performance metric. Using monthly data for a broad sample of U.S. common stocks during January 1974 through December 2013, they find that: Keep Reading

Value Strategy Update

We have updated the the monthly asset class ETF value strategy weights and associated performance data at Value Strategy.

Preliminary Value Strategy Update

The home page and “Value Strategy” now show preliminary asset class ETF value strategy positions for May 2016. There may be small shifts in allocations based on final data.

Momentum Strategy and Trading Calendar Updates

We have updated monthly asset class ETF momentum winners and associated performance data at Momentum Strategy.

We have updated the Trading Calendar to incorporate data for April 2016.

Preliminary Momentum Strategy Update

The home page and “Momentum Strategy” now show preliminary asset class ETF momentum strategy positions for May 2016. Differences in past returns among the top places suggest that rankings are unlikely to change by the close.

SACEMS Portfolio-Asset Exclusion Testing

“Simple Asset Class ETF Momentum Strategy Universe Sensitivity” explores effects on basic strategy (Top 1) performance from excluding base set exchange-traded funds (ETF) one at a time. How do these exclusions affect the more diversified equally weighted top two (EW Top 2) and equally weighted top three (EW Top 3) portfolio variations? To investigate, we each month rank the following assets based on past return with one excluded (nine separate test sequences) and reform the Top 1, EW Top 2 and EW Top 3 portfolios:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

The sample for the test starts with the first month all base set ETFs are available (February 2006). We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics, ignoring monthly portfolio reformation costs. Using end-of-month total returns for the specified nine assets during February 2006 through March 2016, we find that: Keep Reading

SACEMS Portfolio-Asset Addition Testing

“Simple Asset Class ETF Momentum Strategy Universe Enhancers?” explores effects on basic strategy (Top 1) performance of adding 22 exchange-traded fund (ETF) or note (ETN) asset class proxies one at a time to the base set. How do these additions affect the more diversified equally weighted top two (EW Top 2) and equally weighted top three (EW Top 3) portfolio variations? To investigate, we again consider the following 22 ETFs/ETNs:

AlphaClone Alternative Alpha (ALFA)
JPMorgan Alerian MLP Index (AMJ)
Vanguard Total Bond Market (BND)
SPDR Barclays International Treasury Bond (BWX)
UBS ETRACS Wells Fargo Business Development Companies (BDCS)
iShares Core US Credit Bond (CRED)
First Trust US IPO Index (FPX)
PowerShares DB G10 Currency Harvest (DBV)
Guggenheim Frontier Markets (FRN)
iShares iBoxx High-Yield Corporate Bond (HYG)
iShares 7-10 Year Treasury Bond (IEF)
iPath DJ-UBS Copper Total Return Sub-Index (JJC)
PowerShares Closed-End Fund Income Composite (PCEF)
PowerShares Global Listed Private Equity (PSP)
IQ Hedge Multi-Strategy Tracker (QAI)
SPDR Dow Jones International Real Estate (RWX)
ProShares UltraShort S&P 500 (SDS)
iShares TIPS Bond (TIP)
United States Oil (USO)
VelocityShares Daily Inverse VIX Short-Term (XIV)
iPath S&P 500 VIX Short-Term Futures (VXX)
iPath S&P 500 VIX Medium-Term Futures (VXZ)

The base set consists of:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

Each month, we rank the base set plus one of the additional ETFs/ETNs based on past return and reform the Top 1, EW Top 2 and EW Top 3 portfolios. The sample starts with the first month all base set ETFs are available (February 2006), but inceptions for most of the additional ETFs/ETNs are after this month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics, ignoring monthly reformation costs. Using end-of-month total returns for the specified 31 assets as available during February 2006 through March 2016, we find that: Keep Reading

Dual Momentum with Multi-market Breadth Crash Protection

Does adding crash protection based on global market breadth enhance the reliability of dual momentum? In their April 2016 paper entitled “Protective Asset Allocation (PAA): A Simple Momentum-Based Alternative for Term Deposits”, Wouter Keller and Jan Willem Keuning examine a multi-class, dual-momentum portfolio allocation strategy with crash protection based on multi-market breadth. Their principal goal is consistently positive returns, at least 95% (99%) of 1-year rolling returns not below 0% (-5%). Their investment universe is 13 exchange-traded funds (ETF), 12 risky (SPY, QQQ, IWM, VGK, EWJ, EEM, IYR, GSG, GLD, HYG, LQD, TLT) and one safe (IEF). Each month, they:

  1. Measure the momentum of each risky ETF as ratio of current price to simple moving average (SMA) of monthly prices over the past 3, 6, 9 or 12 months, minus one.
  2. Specify the safe ETF allocation as number of risky assets with non-positive momentum divided by 12 (low crash protection), 9 (medium crash protection) or 6 (high crash protection). For example, if 3 of 12 risky assets have zero or negative momentum, the IEF allocation for high crash protection is 3/6 = 50%.
  3. Allocate the balance of the portfolio to the equally weighted 1, 2, 3, 4, 5 or 6 risky assets with the highest positive momentum (reducing the number of risky assets held if not enough have positive momentum).

The interactions of four SMA measurement intervals, three crash protection levels and six risky asset groupings yield 72 combinations. They first identify the optimal combination in-sample during 1971 through 1992 and then test this combination out-of-sample since 1992. Prior to ETF inception dates, they simulate ETF prices based on underlying indexes. They assume constant one-way trading frictions 0.1%, acknowledging that this level may be too low for early years and too high for recent years. They focus on a monthly rebalanced 60% allocation to SPY and 40% allocation to IEF (60/40) as a benchmark. Using monthly simulated/actual ETF total return series during December 1969 through December 2015, they find that: Keep Reading

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Current Momentum Winners

ETF Momentum Signal
for May 2016 (Final)

Winner ETF

Second Place ETF

Third Place ETF

Gross Compound Annual Growth Rates
(Since August 2006)
Top 1 ETF Top 2 ETFs
11.3% 11.5%
Top 3 ETFs SPY
12.4% 7.2%
Strategy Overview
Current Value Allocations

ETF Value Signal
for May 2016 (Final)

Cash

IEF

LQD

SPY

The asset with the highest allocation is the holding of the Best Value strategy.
Gross Compound Annual Growth Rates
(Since September 2002)
Best Value Weighted 60-40
12.7% 9.8% 7.8%
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