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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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

SACEMS Portfolio-Monthly Cycle Robustness Testing

Subscribers have requested extension of the monthly return calculation cycle robustness test in “Optimal Monthly Cycle for Simple Asset Class ETF Momentum Strategy?” to portfolios other than the momentum winner (Top 1), which each month ranks the following eight asset class exchange-traded funds (ETF), plus cash, on past return and rotates to the strongest class:

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)

We consider the following additional five portfolios: equally weighted top two (EW Top 2); equally weighted top three (EW Top 3); loser (Bottom 1); equally weighted bottom two (EW Bottom 2); and, equally weighted bottom three (EW Bottom 3). We consider monthly return calculation cycles ranging from 10 days before end of month (EOM-10) to 10 days after end of month (EOM+10). The sample starts with the first month for which all ETFs are available (February 2006). We focus on gross compound annual growth rate (CAGR) for all portfolios and gross maximum drawdown (MaxDD) for “Top” portfolios as key performance statistics, ignoring monthly reformation costs. Using daily total returns for the specified assets during early February 2006 through early April 2016, we find that: Keep Reading

Balancing Short-term and Long-term Portfolio Risks

How should investors (particularly retirees) think about balancing short-term crash risk and long-term portfolio sustainability? In their March 2016 paper entitled “Asset Allocation with Short and Long Term Risk Objectives”, Peng Wang and Jon Spinney present a way to balance short-term and long-term portfolio performance risks. They consider portfolios that each month allocate all funds in fixed weights to a mix of stocks (MSCI ACWI Index), bonds (Barclays U.S. Aggregate Index) and real estate investment trusts (MSCI Global REIT Index). They measure short term risk as the average of the worst 1% of annual returns from 10,000 bootstrapping simulations that randomly draw three months of returns at a time from 20-year historical pool of returns for these indexes, thereby preserving some monthly return autocorrelations and cross-correlations. They measure long-term risk as the probability that portfolio value is below its initial value after ten years from 10,000 Monte‐Carlo simulations based on expected asset class returns, pairwise asset return correlations, inflation, investment alpha (baseline constant 1% annually) and withdrawals (baseline approximately 5% annual real rate). Using monthly returns for the asset class proxies during January 1995 through October 2015 and longer samples to estimate ten-year returns and return correlations, they find that: Keep Reading

Economic/Market Factor Investing Heat Map

Can an approach that describes each asset class as a bundle of sensitivities to economic/market conditions improve investment decision-making? In their March 2016 paper entitled “Factor-Based Investing”, Pim Lausberg, Alfred Slager and Philip Stork develop a “heat map” to summarize how returns for seven asset classes relate to six economic/market factors. The seven asset classes are: (1) government bonds; (2) investment grade corporate bonds; (3) high-yield corporate bonds; (4) global equity; (5) real estate; (6) commodities; and, (7) hedge funds. The six economic/market factors are: (1) change in consensus forecast of next-year economic growth; (2) change in consensus forecast for next-year inflation; (3) illiquidity (Bloomberg market liquidity indexes); (4) volatility of stock market indexes; (5) credit spread (return on investment grade corporate bonds minus return on duration-matched U.S. Treasuries); and, (6) term spread (return on government bonds of duration 7-10 years minus return on government bills of duration three months). They also provide suggestions on how to use the heat map in the investment process. Using monthly asset class returns and factor estimation inputs during 1996 through 2013, 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%
Strategy Overview
Recent Research
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