Strategic Allocation

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

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

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

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

Can simple moving average (SMA) rules tell investors when it is prudent to leverage the U.S. stock market? In their March 2016 paper entitled “Leverage for the Long Run – A Systematic Approach to Managing Risk and Magnifying Returns in Stocks”, Michael Gayed and Charles Bilello augment conventional U.S. stock market SMA timing rules by adding leverage while in equities. Specifically, they test a Leverage Rotation Strategy (LRS) comprised of the following rules:

• When the S&P 500 Total Return Index closes above its SMA, hold the index and apply 1.25X, 2X or 3X leverage to magnify returns.
• When the S&P 500 Total Return Index closes below its SMA, switch to U.S. Treasury bills (T-bills) to manage risk.

They focus on a conventional 200-day SMA (SMA200), but include some tests with shorter measurement intervals to gauge robustness. They ignore costs of switching between stocks and T-bills. They apply targeted leverage daily with an assumed 1% annual cost of leverage, approximating current expense ratios for the largest leveraged exchange-traded funds (ETF) that track the S&P 500 Index. Using daily closes of the S&P 500 Total Return Index and T-bill yields during October 1928 through October 2015, they find that: Keep Reading

Subscribers have requested extension of the momentum ranking interval robustness test in “Simple Asset Class ETF Momentum Strategy Robustness/Sensitivity Tests” 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 momentum ranking intervals ranging from one month (1-1) to 12 months (12-1), all with one-month holding intervals (monthly portfolio reformation). The sample starts with the first month for which all ETFs are available (February 2006) and portfolio formation starts with the first month allowed by the longest momentum ranking interval (March 2007). We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key portfolio performance statistics, ignoring monthly reformation costs. Using monthly total returns for the specified assets during February 2006 through February 2016, we find that: Keep Reading

Does a simple countercyclical (contrarian) asset class allocation rule work well, wherein an investor assumes that a relatively high aggregate allocation to an asset class signals relatively low/risky future returns for that class? In his February 2016 paper entitled “Understanding Modern Portfolio Construction”, Cullen Roche reviews the principles of investing and portfolio construction and examines a simple countercyclical approach for adaptively balancing the risk of losing purchasing power versus the risk of permanent loss via a stocks-bonds portfolio. He hypothesizes that such an approach reduces behavioral risks. Using investment cycle indicators and world/U.S. stock and bond class returns as available during 1952 through 2015, he concludes that: Keep Reading

In the introductory text for Part I of their 2016 book, Adaptive Asset Allocation: Dynamic Global Porfolios to Profit in Good Times – and Bad, Adam Butler, Michael Philbrick and Rodrigo Gordillo state: “…we have come to stand for something square and real, a true Iron Law of Wealth Management: We would rather lose half our clients during a raging bull market than half of our clients’ money during a vicious bear market. …some of you might already be on the verge of change, carrying with you the emotional scars of a turbulent and ongoing battle with the markets. If so, there’s a decent chance that you lost faith in the traditional investment process some time ago and have struggled to find an alternative. We wrote this book for you.” Based on their experience and research, they conclude that: Keep Reading

Should investors trust retirement planning tools that are publicly available on financial websites? In their February 2016 paper entitled “The Efficacy of Publicly-Available Retirement Planning Tools”, Taft Dorman, Barry Mulholland, Qianwen Bi and Harold Evensky:

1. Identify via theoretical analysis and a survey of financial professionals the demographic, financial and economic variables important as inputs to retirement planning.
2. Assess effectiveness in using these inputs of 36 retirement planning tools available at no/modest cost without the intervention of a financial professional.

The second step is based on the following retirement scenario:

• Couple (male age 59 and female age 57), each with annual income $50,000.
• Total current investment assets $700,000.
• Expected retirement ages 65 and 63, respectively.
• Expected annual real retirement expenses after income taxes $70,000.
• Social Security income to begin at age 66.
• Life expectancies 90 and 92, respectively.

They establish a benchmark by using these inputs in MoneyGuidePro (used by the plurality of professionals responding to the survey) with estimates for expected investment return, inflation rate and tax rate, generating an unacceptably low 53% probability of successful retirement. If a retirement planning tool using these same estimates (to the extent it can) indicates that the couple can retire as expected qualitatively with a simple statement or quantitatively with 70%+ confidence, they classify the tool as failed. Using survey responses from 297 financial professionals, MoneyGuidePro and the 36 retirement planning tools, they find that: Keep Reading

Can individual investors easily mimic the asset allocation strategies, and thereby the returns, of university endowments? In his March 2016 paper entitled “Invest Like an Endowment”, Drew Knowles reviews the asset allocation policies and resultant investment returns of those college and university endowments who volunteer such data to the National Association of College and University Business Officers (NACUBO). He groups endowments into four size categories. He separately reviews Yale University endowment annual reports on allocations and performance as a best practices benchmark. He then analyzes returns for simple asset class allocation clones of endowment categories and the Yale endowment in particular. He builds clones using exchange-traded funds (ETF), augmented by associated indexes before the ETFs are available. He rebalances clones annually in January as NACUBO releases new endowment annual performance reports (with a lag of about six months). For most clones, he groups alternative assets into a broad hedge fund basket. Using nominal category returns during 1988 through 2014, category asset allocations during 2002 through 2014, Yale endowment returns and allocations during 1997 through 2014 and ETF/index total returns over matched periods, he finds that: Keep Reading