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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Simple Asset Class Momentum Strategy Applied to Mutual Funds

A subscriber inquired whether a longer test of the “Simple Asset Class ETF Momentum Strategy” is feasible using mutual funds rather than exchange-traded funds (ETF) as asset class proxies. To investigate, we consider the following set of mutual funds (partly adapted from the paper summarized in “Asset Allocation Combining Momentum, Volatility, Correlation and Crash Protection”):

Oppenheimer Commodity Strategy Total Return A (QRAAX)
Vanguard Emerging Markets Stock Index Investor Shares (VEIEX)
Fidelity Diversified International (FDIVX)
First Eagle Gold A (SGGDX)
Vanguard Total Stock Market Index Investor Shares (VTSMX)
Vanguard Small Capitalization Index Investor Shares  (NAESX)
Vanguard REIT Index Investor Shares (VGSIX)
Vanguard Long-Term Treasury Investor Shares (VUSTX)
3-month Treasury bills (Cash)

The investigation includes basic tests performed in “Simple Asset Class ETF Momentum Strategy”, robustness tests performed in “Simple Asset Class ETF Momentum Strategy Robustness/Sensitivity Tests” and some of the extensions explored in “Alternative Asset Class ETF Momentum Allocations”. The selected mutual funds all have monthly prices available as of the end of March 1997. Monthly strategy returns, as limited by the kinds of tests performed, commence in April 1998. Using monthly dividend-adjusted closing prices for the above mutual funds and the yield for Cash during March 1997 through September 2014 (212 months), we find that: Keep Reading

Survey of Recent Research on Constructing and Monitoring Portfolios

What’s the latest research on portfolio construction and risk management? In the the introduction to the July 2014 version of his (book-length) paper entitled “Many Risks, One (Optimal) Portfolio”, Cristian Homescu states: “The main focus of this paper is to analyze how to obtain a portfolio which provides above average returns while remaining robust to most risk exposures. We place emphasis on risk management for both stages of asset allocation: a) portfolio construction and b) monitoring, given our belief that obtaining above average portfolio performance strongly depends on having an effective risk management process.” Based on a comprehensive review of recent research on portfolio construction and risk management, he reports on:

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A Few Notes on Dual Momentum Investing

In the preface to his 2015 book entitled Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk, author Gary Antonacci states: “We need a way to earn long-term above-market returns while limiting our downside exposure. This book shows how momentum investing can make that desirable outcome a reality. …the academic community now accepts momentum as the ‘premier anomaly’ for achieving consistently high risk-adjusted returns. Yet momentum is still largely undiscovered by most mainstream investors. I wrote this book to help bridge the gap between the academic research on momentum, which is extensive, and its real-world application… I finally show how dual momentum—a combination of relative strength and trend-following…is the ideal way to invest.” Based on a survey of related research and his own analyses, he concludes that: Keep Reading

Comparing Ivy 5 Allocation Strategy Variations

A subscriber requested comparison of four variations of an “Ivy 5″ asset class allocation strategy, as follows:

  1. Ivy 5 EW: Assign equal weight (EW), meaning 20%, to each of the five positions and rebalance annually.
  2. Ivy 5 EW + SMA10: Same as Ivy 5 EW, but take to cash any position for which the asset is below its 10-month simple moving average (SMA10).
  3. Ivy 5 Volatility Cap: Allocate to each position a percentage up to 20% such that the position has an expected annualized volatility of no more than 10% based on daily volatility over the past month, recalculated monthly. If under 20%, allocate the balance of the position to cash.
  4. Ivy 5 Volatility Cap + SMA10: Same as Ivy 5 Volatility Cap, but take completely to cash any position for which the asset is below its SMA10.

The subscriber proposed the following five asset class proxies for testing:

iShares 7-10 Year Treasury Bond (IEF)
SPDR S&P 500 (SPY)
SPDR Dow Jones REIT (RWR)
iShares MSCI EAFE Index (EFA)
PowerShares DB Commodity Index Tracking (DBC)

The DBC series in combination with the SMA10 rule are limiting with respect to sample start date and the first return calculations. Using daily and monthly dividend-adjusted closing prices for the five asset class proxies and the yield on 13-week U.S. Treasury bills (T-bills) as a proxy for return on cash during February 2006 through August 2014 (103 months), we find that: Keep Reading

Retirement Allocation Strategy Informed by P/E10

Does adjusting an asset allocation retirement glidepath according to a stock market valuation metric such as Shiller’s cyclically adjusted price-earnings ratio (CAPE ratio or P/E10) improve the outcome? In their September 2014 paper entitled “Retirement Risk, Rising Equity Glidepaths, and Valuation-Based Asset Allocation”, Michael Kitces and Wade Pfau investigate the interaction of pre-determined allocation glidepaths and P/E10 valuation based on long-run U.S. historical data. They consider the following strategy alternatives:

  • Fixed equity allocations of either 45% or 60%.
  • Declining (accelerated declining) equity glidepaths that start retirement at 60% stocks and reduce the allocation by 1% (2%) per year.
  • Rising (accelerated rising) equity glidepaths that start retirement at 30% stocks and increase the allocation by 1% (2%) per year.
  • A standalone dynamic valuation-based strategy with baseline 45% equity, raised (lowered) to 60% (30%) at the beginning of any year for which P/E10 is less than (greater than) 67% (133%) of its inception-to-date median. (See the chart below.)
  • Unbounded and bounded combinations of declining or rising glidepaths and the dynamic valuation-based strategy, adding (subtracting) 15% from the equity glidepath at the beginning of any year for which P/E10 indicates undervaluation (overvaluation). Bounded combinations constrain equity allocation to a minimum 30% and a maximum 60%.

They consider both short-term bills (six months to a year) and long-term bonds (10-year) as complements to equities. They use overlapping 30-year intervals to approximate retirement outcomes. They focus on worst-case maximum sustainable real (inflation-adjusted) withdrawal rate over the 30-year retirement interval as the main strategy performance metric. Withdrawals occur at the beginning of each year, with the residual portfolio then rebalanced to target allocations. They assume withdrawals pay the taxes. Using Robert Shiller’s monthly data for U.S. stock market returns, associated P/E10, short-term bill yields (six-month commercial paper/one-year U.S. Treasury notes) and long-term bond yields (10-year U.S. Treasury notes or equivalent) during 1871 through 2013, they find that: Keep Reading

Momentum as Moderator of Portfolio Rebalancing Risk

Does playing trends both ways via periodic rebalancing (betting on reversion) and momentum (betting on continuation) reliably produce attractive outcomes? In the August 2014 version of their paper entitled “Rebalancing Risk”, Nick Granger, Doug Greenig, Campbell Harvey, Sandy Rattray and David Zou investigate the effects of adding a momentum overlay to a conventionally rebalanced stocks-bonds portfolio. They note that periodic rebalancing to fixed asset class weights tends to perform well in trendless markets exhibiting mean reversion but suffers during extended trends. They consider simple examples using a 60% target allocation to the S&P 500 Index and a 40% allocation to 10-year U.S. Treasury notes (T-note), rebalanced monthly or quarterly. Their momentum strategy employs a complex daily moving average cross-over model with target volatility 10% that has an average annual turnover of 400%. Using both theoretical arguments and empirical analysis of daily and monthly asset class proxy returns during January 1990 through February 2014, they find that: Keep Reading

Optimal Rebalancing Method/Frequency?

How much performance improvement comes from rebalancing a stocks-bonds portfolio, and what specific rebalancing approach works best? In their August 2014 paper entitled “Testing Rebalancing Strategies for Stock-Bond Portfolios Across Different Asset Allocations”, Hubert Dichtl, Wolfgang Drobetz and Martin Wambach investigate the net performance implications of different rebalancing approaches and different rebalancing frequencies on portfolios of stocks and government bonds with different weights and in different markets. With buy-and-hold as a benchmark, they consider three types of rebalancing rules: (1) strict periodic rebalancing to target weights; (2) threshold rebalancing, meaning periodic rebalancing to target weights if out-of-balance by 3% or more; and, (3) range rebalancing, meaning periodic rebalancing to plus (minus) 3% of target weights if above (below) target weights by more than 3%. They consider annual, quarterly and monthly rebalancing frequencies. They use 30 years of broad U.S., UK and German stock market, bond market and risk-free returns to construct simulations with 10-year investment horizons. Their simulation approach preserves most of the asset class time series characteristics, including stocks-bonds correlations. They assume round-trip rebalancing frictions of 0.15% (0.10% for stocks and 0.05% for bonds). Using monthly returns for country stock and bonds markets and risk-free yields during January 1982 through December 2011 to generate 100,000 simulated 10-year return paths, they 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” might enhance performance. To investigate, we execute the strategy with the following eight 2X leveraged ETFs, plus cash:

ProShares Ultra DJ-UBS Commodity (UCD)
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 allocate all funds at the end of each month to the asset class leveraged ETF or cash with the highest total return over the past five months (5-1). 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 July 2014 (only 55 months), we find that: Keep Reading

Effects of Execution Delay on Simple Asset Class ETF Momentum Strategy

“Optimal Monthly Cycle for Simple Asset Class ETF Momentum Strategy?” investigates whether using a monthly cycle other than end-of-month (EOM) to determine the winning asset improves performance of the “Simple Asset Class ETF Momentum Strategy”. This strategy each month allocates all funds to the one of the following eight asset class exchange-traded funds (ETF), or cash, with the highest total return over the past five months:

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)

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 the strategy that all use EOM to determine the winning asset 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, an EOM+5 Close variation uses an EOM cycle to determine winners but delays executions until the close five trading days after EOM. Using daily dividend-adjusted opens and closes for the asset class proxies and the yield for Cash from the end of July 2002 (or inception if not available then) through the end of July 2014 (144 months), we find that: Keep Reading

Buffered Winner Asset Class ETF Momentum Strategy

“Sticky Winner Asset Class ETF Momentum Strategy” tests whether limiting the trading of the “Simple Asset Class ETF Momentum Strategy” by holding onto the winner until it drops out of the top three boosts performance of the latter by reducing trading and thereby suppressing trading frictions. A subscriber proposed a more precise approach to limit trading: continue holding a past winner until it loses to a new winner by a significant margin. To investigate whether this approach (Buffered Winner) works, we compare it to the original strategy (Winner), which allocates all funds at the end of each month to the asset class exchange-traded fund (ETF) or cash with the highest total return over the last five months, as applied to the following nine assets:

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)

Using monthly adjusted closing prices for the asset class proxies and the yield for Cash over the period July 2002 (or inception if not available then) through June 2014 (144 months), we find that: Keep Reading

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