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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Value Strategy Update

We have updated the the monthly asset class ETF value strategy weights and associated performance data at Value Strategy.

Preliminary Value Strategy Update

The home page and “Value Strategy” now show preliminary asset class ETF value strategy positions for December 2015. There may be small shifts in allocations based on final data. The difference between the two best values is small, so the “Best Value” selection could change.

Momentum Strategy and Trading Calendar Updates

We have updated the the monthly asset class ETF momentum winners and associated performance data at Momentum Strategy.

We have updated the Trading Calendar to incorporate data for November 2015.

Preliminary Momentum Strategy Update

The home page and “Momentum Strategy” now show preliminary asset class ETF momentum strategy positions for December 2015. Differences in past returns among the top places are large enough that changes by the close are unlikely.

Reverse Mortgage as Retirement Strategy Component

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

Twisting Buffett’s Preferred Stocks-bonds Allocation

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

Risk Management Across Assets and Over Time

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

Multi-class RSI-based Dynamic Asset Allocation

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

Secular Headwind for Risk Parity?

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

A Few Notes on Systematic Trading

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

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Current Momentum Winners

ETF Momentum Signal
for December 2015 (Final)

Winner ETF

Second Place ETF

Third Place ETF

Gross Compound Annual Growth Rates
(Since August 2006)
Top 1 ETF Top 2 ETFs
12.0% 12.3%
Top 3 ETFs SPY
12.6% 7.4%
Strategy Overview
Current Value Allocations

ETF Value Signal
for December 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
12.8% 9.9% 8.0%
Strategy Overview
Recent Research
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