Strategic Allocation

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

Does seasonality usefully combine with trend following for timing asset markets? In his January 2016 paper entitled “Multi-Asset Seasonality and Trend-Following Strategies”, Nick Baltas examines seasonal patterns (based on same calendar month over the past ten years) for four asset classes: commodities, government bonds, currency exchange rates and country equity markets. He then tests whether identified seasonal patterns enhance a simple trend-following strategy that is long (short) the inverse volatility-weighted assets within a class that have positive (negative) excess returns over the past 12 months. Specifically, he closes any long (short) trend positions in the bottom (top) fifth of seasonality rankings. To assess net performance, he considers trading frictions ranging from 0.05% to 0.25%. Using spot and front futures return data for 19 commodity price indexes and spot return data for 16 10-year government bonds, 10 currency exchange rates and 18 country equity total return indexes as available through December 2014, he finds that: Keep Reading

Wesley Gray, Jack Vogel and David Foulke preface their 2015 book, DIY Financial Advisor: A Simple Solution to Build and Protect Your Wealth, by stating that: “This book is a synopsis of our research findings developed while serving as a consultant and asset manager for large family offices. …Our book is meant to be an educational journey that slowly builds confidence in one’s own ability to manage a portfolio. In our book, we explore a potential solution that can be applicable to a wide variety of investors, from the ultra-high-net-worth to middle-class individual, all of whom are focused on similar goals  of preserving and growing their capital over time.” Based on their research, they conclude that: Keep Reading