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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A Few Notes on A Random Walk Down Wall Street

In the preface to the eleventh (2015) edition of his book entitled A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing, author Burton Malkiel states: “The message of the original edition was a very simple one: Investors would be far better off buying and holding an index fund than attempting to buy and sell individual securities or actively managed mutual funds. …Now, over forty years later, I believe even more strongly in that original thesis… Why, then, an eleventh edition of this book? …The answer is that there have been enormous changes in the financial instruments available to the public… In addition, investors can benefit from a critical analysis of the wealth of new information provided by academic researchers and market professionals… There have been so many bewildering claims about the stock market that it’s important to have a book that sets the record straight.” Based on a survey of financial markets research and his own analyses, he concludes that: Keep Reading

Simple Asset Class ETF Maximum Momentum Strategy

In an effort to generate more responsive exchange-traded fund (ETF) momentum switching, a subscriber proposed a version of the “Simple Asset Class ETF Momentum Strategy” that measures ETF returns from the lowest daily close within the momentum measurement interval rather than the monthly close at the beginning of the momentum measurement interval. To investigate, we run a competition between these alternative ways of measuring momentum as applied to the following eight asset class exchange-traded funds (ETF), plus cash:

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)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

Specifically, the baseline strategy allocates all funds at the end of each month to the ETF or cash with the highest total return over the past five months (5-1). The alternative strategy allocates all funds at the end of each month to the ETF or cash with the highest return measured from its low during the last 105 trading days (about five months) to the end of the current month (Max 5-1). Using daily dividend-adjusted closing prices for the asset class proxies and the monthly yield for Cash during July 2002 (or inception if not available then) through December 2014 (150 months), we find that: Keep Reading

Long-run Test of a Tactical, Tractable MPT

Does a cross-asset class, momentum-driven, simplified version of Modern Portfolio Theory (MPT) offer reliably strong performance over the long run? In their December 2014 paper entitled “A Century of Generalized Momentum; From Flexible Asset Allocations (FAA) to Elastic Asset Allocation (EAA)”, Wouter Keller and Adam Butler present an asset allocation strategy based on five concepts:

  1. MPT is a sound framework for portfolio construction.
  2. Momentum, a form of trend measurement, is a generally effective way to estimate key inputs to MPT: asset returns (R), return volatilities (V) and return correlations (C).
  3. Crash protection based on excluding assets with negative past returns is a reasonable corollary of reliance on trends.
  4. Tractability requires compromise to strict MPT, such as calculating return correlations relative to a single index (the equally weighted average returns of all assets).
  5. Recognition of differences in import among inputs means weighting R, V and C inputs differently according to their elasticities (how much small changes in R, V and C affect the optimal portfolio weight for the asset).

The fifth concept is the innovation relative to the Flexible Asset Allocation (FAA) predecessor (see “Asset Allocation Combining Momentum, Volatility, Correlation and Crash Protection”), which weights expected R, V and C inputs based on a simple scoring system. The new Elastic Asset Allocation (EAA) strategy each month scores all assets in a universe by: (1) calculating expected R, V and C for each asset as geometrically weighted averages of past values; and, (2) weighting the expected values of R, V and C by their respective elasticities. For R, they use average total monthly excess (relative to the 13-week U.S. Treasury bill yield) returns over the last 1, 3, 6 and 12 months. For V and C, they use the last 12 monthly returns. To test the EAA strategy, they each month reform a long-only portfolio of the top-ranked assets weighted by their respective scores. They replace a fraction of the portfolio with 10-year U.S. Treasury notes (selected empirically as the best “cash” asset) according to the fraction of assets in the universe with non-positive excess returns. They apply a nominal one-way index switching friction of 0.1%. They consider three universes of 7, 15 and 38 asset classes. They emphasize Calmar ratio (focusing on drawdown) as a key optimization metric, but also consider Sharpe ratio. To mitigate data snooping, they optimize elasticity parameters during April 1914 through March 1964 and test it out-of-sample during April 1964 through August 2014. Using monthly returns for the three sets of financial asset indexes as available during April 1914 through August 2014, they find that:

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Net Benefits of Diversification

Does diversification into alternative asset class investments, which may carry high management fees, help on a net basis? In the December 2014 version of their paper entitled “Fees Eat Diversification’s Lunch”, William Jennings and Brian Payne examine the diversification benefits of different asset classes after accounting for associated investment management fees. They focus on fees relative to allocation alpha, the expected return after accounting for market risk (volatility). Allocation alpha is a passive return derived from strategic allocation. They consider 45 asset classes with long-term (10-15 years) expected returns, risks and correlations per J.P. Morgan’s “Long-term Capital Market Return Assumptions.” They apply asset class investment management fees from a biennial fee survey performed by a major institutional investment consulting firm, segmented into three investor types: small endowment, state pension, and high-quality (fee-advantaged) foundation. Using the specified asset class performance estimates and associated investment management fees, they find that: Keep Reading

Simple Momentum Strategy Applied to TSP Funds

A subscriber asked about applying the “Simple Asset Class ETF Momentum Strategy” to the funds available to U.S. federal government employees via the Thrift Savings Plan (TSP). To investigate, we test the strategy on the following five funds:

G Fund: Government Securities Investment Fund (G)
F Fund: Fixed Income Index Investment Fund (F)
C Fund: Common Stock Index Investment Fund (C)
S Fund: Small Cap Stock Index Investment Fund (S)
I Fund: International Stock Index Investment Fund (I)

For baseline tests, we allocate at the end of each month to the fund with the highest total return over the past five months (5-1), an equally weighted portfolio of the top two funds (EW top 2) or an equally weighted portfolio of the Top 3 funds (EW Top 3). We also conduct a robustness test to assess ranking intervals other than five months. Using monthly returns for the five funds from initial availability of all five (January 2001) through November 2014 (167 months), we find that: Keep Reading

Simple Asset Class ETF Momentum Strategy Universe Enhancers?

Would adding a systematically chosen exchange-traded fund (ETF) or note (ETN) asset class proxy to the base set used in the “Simple Asset Class ETF Momentum Strategy” improve performance? To investigate, we consider adding each of the following 22 ETFs/ETNs (suggested over time by subscribers) one at a time to the strategy:

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

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)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

We evaluate adding an asset to the base set via its effect on monthly net return-risk ratio (average monthly net return divided by standard deviation of monthly returns, a rough Sharpe ratio). Since the added assets have different sample periods, we rationalize by focusing on the difference in return-risk ratio (the ratio of the base set with the asset minus the ratio of the base set only) over the period the added asset is available. We then relate the resulting 22 differences in return-risk ratio to four characteristics of the respective added assets: (1) average monthly return; (2) standard deviation of monthly returns; (3) average (pairwise) cross-correlation of monthly returns with the base set assets; and, (4) serial correlation of monthly returns. The objective is to determine whether any of these four characteristics explain asset contribution to the momentum strategy. Using dividend/split-adjusted monthly prices for the above 31 asset class proxies as available during July 2002 through November 2014 (a maximum of 149 months), we find that:

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Equal Weighting vs. All Feasible Long-only Mean-variance Optimals

Is equal weighting (1/n) of portfolio components a good choice? In their November 2014 paper entitled “Is 1/n Really Better Than Optimal Mean-Variance Portfolio?”, Woo Chang Kim, Yongjae Lee and William Ziemba assess 1/n weighting by comparing its performance to the performances of all feasible mean-variance optimal portfolios for different asset universes. By “all feasible,” they mean many long-only mean-variance optimal portfolios generated by randomly picking the estimated future return-to-variance ratios for assets within a universe. They use Sharpe ratio to measure portfolio performance. They consider 10 asset universes: 10 U.S. equity sectors; 10 U.S. equity industries; eight country equity indexes; three U.S. equity factor portfolios; six U.S. equity styles; 25 U.S. equity styles; 100 U.S. equity styles; 250 large-capitalization U.S. stocks; 250 medium-capitalization U.S. stocks; and, 250 small-capitalization U.S. stocks.They apply mostly annual rebalancing but also consider semiannual and quarterly rebalancing for the three stock universes. They also test 1/n versus capitalization weighting for seven of the 10 universes. Using returns for specified assets at the tested rebalancing frequencies with sample start dates as early as July 1963 and end dates as late as June 2014, they find that: Keep Reading

Overview of Master Limited Partnerships

Are publicly traded Master Limited Partnerships attractive investments? In their June 2014 paper entitled “Master Limited Partnerships (MLPs)”, Frank Benham, Steven Hartt, Chris Tehranian and Edmund Walsh describe and summarize the aggregate performance and characteristics of publicly traded MLPs. These partnerships are predominantly owners of “toll road” energy infrastructure, U.S. oil and natural gas pipelines and resource shipping. Like real estate investment trusts (REIT), MLPs are pass-through entities for tax purposes. Their distributions to partners are not subject to double-taxation as are corporate dividends. Unlike REITs, MLPs may retain income to fund growth. The general (managing) partner of an MLP typically earns an incentive-based share of distributions larger than that of limited (passive) partners. MLPs involve tax, accounting and administrative complications associated with partnerships. Using monthly returns for the capitalization-weighted Alerian MLP Index and for other asset class indexes during January 2000 through April 2014, they conclude that: Keep Reading

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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Current Momentum Winners

ETF Momentum Signal
for April 2015 (Final)

Winner ETF

Second Place ETF

Third Place ETF

Gross Compound Annual Growth Rates
(Since August 2006)
Top 1 ETF Top 2 ETFs
15.1% 15.8%
Top 3 ETFs SPY
15.3% 7.7%
Strategy Overview
Current Value Allocations

ETF Value Signal
for 2nd Quarter 2015 (Final)





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
13.7% 9.6% 8.6%
Strategy Overview
Recent Research
Popular Posts
Popular Subscriber-Only Posts