Strategic Allocation

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

Does asset allocation based on both trend following via a simple moving average (SMA) and return momentum work well? In the July 2015 update of their paper entitled “The Trend is Our Friend: Risk Parity, Momentum and Trend Following in Global Asset Allocation”, Andrew Clare, James Seaton, Peter Smith and Stephen Thomas examine the effectiveness of trend following based on SMAs and momentum screens in forming portfolios across and within asset classes. They consider five asset classes: developed equity markets (24 component country indexes); emerging equity markets (16 component country indexes); bonds (19 component country indexes); commodities (23 component commodity indexes); and, real estate (13 country REIT indexes). They compare equal weight and risk parity (proportional to inverse 12-month volatility) strategic allocations. They define trend following as buying (selling) an asset when its price moves above (below) a moving average of 6, 8, 10 or 12 months. They consider both simple momentum (12-month lagged total return) and volatility-adjusted momentum (dividing by standard deviation of monthly returns over the same 12 months) for momentum screens. They ignore trading frictions, exclude shorting and assume monthly trend/momentum calculations and associated trade executions are coincident. Using monthly total returns in U.S. dollars for the five broad value-weighted asset class indexes and for the 95 components of these indexes during January 1993 through March 2015, along with contemporaneous 3-month Treasury bill yields as the return on cash, they find that: Keep Reading

Which is worse with respect to sustaining retirement income: sacrificing potential investment portfolio growth early, or exposing mortgage debt to interest rates later? In his November 2015 paper entitled “Incorporating Home Equity into a Retirement Income Strategy”, Wade Pfau simulates different strategies for incorporating home equity into a retirement plan (both income assurance and legacy) via a Home Equity Conversion Mortgage (reverse mortgage). A reverse mortgage is a non-recourse loan that enables many U.S. homeowners to tap (untaxed) up to $625,000 of home value. The different strategies are:

1. Ignore Home Equity: A baseline not comparable to the other strategies.
2. Home Equity as Last Resort: Delay opening a reverse mortgage line of credit until the investment portfolio is exhausted.
3. Use Home Equity First: Open a reverse mortgage line of credit at the start of retirement and draw upon it first, letting the investment portfolio grow.
4. Sacks and Sacks Coordination Strategy: Open a reverse mortgage line of credit at the start of retirement. Draw upon it (until exhausted, with no repayments) only after years when the investment portfolio loses money.
5. Texas Tech Coordination Strategy: Open a reverse mortgage line of credit at the start of retirement. Draws upon it (until exhausted) when investment portfolio balance falls below an estimated 80% of a required wealth glidepath. Pay it down when investment portfolio balance rises above an estimated 80% of required wealth glidepath.
6. Use Home Equity Last: Open a reverse mortgage line of credit at the start of retirement. Use it only after the investment portfolio is exhausted.
7. Use Tenure Payment: At the start of retirement, implement a reverse mortgage tenure payment (life annuity) option, with the balance of annual spending drawn from the investment portfolio.

For each strategy, he runs 10,000 Monte Carlo simulations of a 40-year retirement based on historical annual distributions of 10-year bond yield, equity premium, home appreciation, short-term interest rate and inflation rate. Annual withdrawals and investment portfolio rebalancings (to 50% stocks and 50% bonds) occur at the start of each year. Assuming initial home value $500,000, initial tax-deferred investment portfolio value $1 million, annual withdrawal 4% of initial investment portfolio value ($40,000, subsequently adjusted for inflation) and marginal tax rate 25% for investment portfolio withdrawals, he finds that: Keep Reading

What is Warren Buffett’s preferred fixed asset allocation, and how does it perform? In his October 2015 paper entitled “Buffett’s Asset Allocation Advice: Take It … With a Twist”, Javier Estrada examines Warren Buffett’s 2013 implied endorsement of a fixed allocation of 90% stocks and 10% short‐term bonds (90/10). Specifically, he tests the performance of eight fixed asset allocations ranging from 100/0 to 30/70. Testing assumes a $1,000 nest egg at retirement, a withdrawal rate of 4% of the initial amount adjusted annually for inflation and a 30‐year retirement. At the beginning of each year, the retiree makes the annual withdrawal and rebalances to the target allocation. The first 30‐year retirement interval is 1900‐1929 and the last 1985‐2014, for a total of 86 rolling intervals. He further explores two adjustments (twists) to the 90/10 allocation:

1. T1 – If stocks are up the past year, take the annual withdrawal from stocks and rebalance to 90/10. If stocks are down, take the annual withdrawal from bonds and do not rebalance.
2. T2 – If stocks outperform bonds the past year, take the annual withdrawal from stocks and rebalance to 90/10. If stocks underperform, take the annual withdrawal from bonds and do not rebalance.

Using U.S. stock market and U.S. Treasury bill (T-bill) annual real total returns as compiled by Dimson‐Marsh‐Staunton for 1900 through 2014, he finds that: Keep Reading

Do both asset-level and portfolio-level risk management techniques enhance portfolio performance? In the October 2015 version of his paper entitled “Optimal Dynamic Portfolio Risk Management”, Valeriy Zakamulin investigates risk management across assets (relative weighting of risky assets) and risk management over time (timing the market via positions in the risk-free rate/leverage). For risk management across risky assets, he consider equal weighting, risk parity (based on asset volatility forecasts) and minimum variance (based on asset volatility and correlation, or covariance, forecasts). He employs an Exponentially Weighted Moving Average (EWMA) for forecasting volatilities and covariances as needed. For risk management over time, he uses portfolio-level variance targeting, applying leverage to risky assets when expected variance is low and shifting capital to the risk-free asset when expected variance is high. He focuses on Sharpe ratio as a performance metric. He ignores costs of portfolio adjustments and leverage. Using daily returns for market capitalization-weighted groupings of U.S. common stocks formed via size-value, size-momentum, size-long reversal and industry sorts (as risky assets) and daily 90-day U.S. Treasury bill yields (as the risk-free rate) from the data library of Kenneth French during January 1972 through December 2014, he finds that: Keep Reading

Is there a simple way to improve the performance of conventional asset class target allocations by rotating to strength within classes based on Relative Strength Index (RSI)? In his September 2015 paper entitled “Momentum Investing and Asset Allocation”, Drew Knowles seeks to improve the performance of baseline asset class (equity, fixed income, hedge fund) allocations via dynamic intra-class rotation to strength based on RSI. His principal passive benchmark (50/30/20) allocates 50% to equities (S&P 500 Total Return Index), 30% to fixed income (Barclays U.S. Aggregate Index) and 20% to hedge funds (HFRI Fund Weighted Composite), apparently rebalanced annually. For dynamic rotation, he replaces the broad equity, fixed income and hedge fund indexes with, respectively, the apparently equally weighted Top 5 (of 10) S&P 500 sector indexes, Top 5 (of seven) fixed income style indexes and Top 5 (of eight) hedge fund style indexes based on 12-month RSI. He breaks ties in RSI by picking the index with higher return per unit of risk (compound annual growth rate divided by standard deviation of returns) over the same 12 months. Within each asset class, he tests four Top 5 reformation frequencies: annual, semi-annual, quarterly or monthly. He ignores rebalancing/reformation frictions and tax implications of trading. Using monthly data for the selected broad and sector/style indexes during 1991 through 2014, he finds that: Keep Reading

Is there a “trick” to good results for risk parity backtests? In their April 2014 brief research paper entitled “The Risks of Risk Parity”, the Brandes Institute examines the sustainability of a critical performance driver for the risk parity asset allocation approach. This approach weights asset classes such that their expected contributions to overall portfolio risk (volatility) are equal, generally by shifting conventional portfolio weights substantially from equities to bonds. Using hypothetical portfolio performance during 1994 through 2013 and bond yield data during 1871 through 2013, they find that: Keep Reading

Robert Carver introduces his 2015 book, Systematic Trading: A Unique New Method for Designing Trading and Investing Systems, by stating that: “I don’t believe there is any magic system that will automatically make you huge profits, and you should be wary of anyone who says otherwise, especially if they want to sell it to you. Instead, success in systematic trading is mostly down to avoiding common mistakes such as over complicating your system, being too optimistic about likely returns, taking excessive risks, and trading too often. I will help you avoid these errors. This won’t guarantee returns, but it will make failure less likely. My framework…can be adapted to meet your needs. …Each element of the framework has been carefully designed… I’ll explain the available options, which I prefer, and why.” Based on his experience as a trader/portfolio manager and specific research, he concludes that: Keep Reading

We have made three changes to the “Simple Asset Class ETF Value Strategy” (SACEVS) based on results of  robustness tests and subscriber comments:

1. To employ fresher data, we decrease the SACEVS S&P 500 Index level and bond/bill yield measurement interval from quarterly to monthly. S&P 500 Index operating earnings updates are still quarterly.
2. To employ fresher data, we use end-of-measurement interval (end-of-month) bond/bill yields rather than average yields during the measurement interval.
3. To account for a lag in availability of bill/bond yield data, we delay signal execution by one trading day.

These changes are logical, but introduce some additional noise. They result in somewhat higher risk-adjusted performance for SACEVS, at the expense of some additional trading. Effects on the Weighted version of the strategy are greater than those on the Best Value version.

We are updating “Value Strategy” and some related tests accordingly.