Strategic Allocation

Is use of long-term stock index call options effective for those approaching retirement with desires of limiting exposure to crashes without sacrificing all benefit of equity exposure? In his January 2015 paper entitled “Individuals Approaching Retirement Have Options (Literally) to Secure a Comfortable Retirement”, Bryan Foltice proposes retirement strategies that employ stock index options during the five years before retirement (when prospective retirees tend to become very risk-averse) to limit equity risk while retaining some reward. These alternatives to conventional (100% stocks, 60%-40% stocks-bonds and 100% minus age in stocks) asset allocation strategies put core funds in Treasury Inflation-Protected Securities (TIPS) to secure retirement income at a real 75% of final working income and funds in excess of the core to buy long-term at-the-money stock index call options. He considers three option-based strategies:

1. Buy 5-year options at age 60.
2. Buy a 3-year option at age 60 and a 2-year option at age 63.
3. Buy 1-year call options each year using the final five annual contributions.

Base modeling assumptions use 1928-2013 historical return statistics, with robustness tests assuming (1) an increased equity risk premium and (2) expectations derived from 2014 data through October. Modeling includes expected costs/fees. Using simulations based on estimates for U.S. stock market capital gains/dividends and for the TIPS real yield, he finds that: Keep Reading

In reference to “Optimal Monthly Cycle for Simple Asset Class ETF Momentum Strategy?”, a subscriber asked about an optimal monthly cycle for the “Simple Debt Class Mutual Fund Momentum Strategy”. This latter strategy each month allocates the entire portfolio value to the one of the following 12 debt class mutual funds with the highest past total return (optimally over the last two months):

T. Rowe Price New Income (PRCIX)
Thrivent Income A (LUBIX)
Vanguard GNMA Securities (VFIIX)
T. Rowe Price High-Yield Bonds (PRHYX)
T. Rowe Price Tax-Free High Yield Bonds (PRFHX)
Vanguard Long-Term Treasury Bonds (VUSTX)
T. Rowe Price International Bonds (RPIBX)
Fidelity Convertible Securities (FCVSX)
PIMCO Short-Term A (PSHAX)
Fidelity New Markets Income (FNMIX)
Eaton Vance Government Obligations C (ECGOX)
Vanguard Long-Term Bond Index (VBLTX)

To investigate, we compare 21 variations of the strategy based on shifting the monthly return calculation cycle relative to trading days from the end of the month (EOM). For example, an EOM+5 cycle ranks funds based on closing prices five trading days after EOM each month. We use the historically optimal two-month fund momentum measurement interval. Using daily dividend-adjusted closes for the 12 funds during mid-December 1994 through mid-January 2015 (241 months), we find that: Keep Reading

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

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” (SACEMS) 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)
SPDR Dow Jones REIT (RWR)
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

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:

Keep Reading

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

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

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

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:

Keep Reading

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