Strategic 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

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.

Which is better, sector-based or factor-based stock investing? In their June 2015 paper entitled “Factor-Based v. Industry-Based Asset Allocation: The Contest”, Marie Briere and Ariane Szafarz compare the attractiveness of sector-based and factor-based U.S. stock allocations. From Kenneth French’s data library, they extract return series for 10 sectors and five factors (size, value, profitability, investment and momentum). They expand the factor set to 10 by using long and short portfolios for each factor. They consider three trials:

1. Which group, sectors or factors, yields the dominantly more attractive efficient frontier?
2. Which group offers the clearly superior gross Jensen’s alphas across single-sector/factor portfolios and portfolios diversified across sectors or factors based on maximizing estimated Sharpe ratio, minimizing estimated volatility or equal weighting?
3. Do portfolios diversified across sectors or factors (based on maximizing estimated Sharpe ratio, minimizing estimated volatility or equal weighting) offer the best gross Sharpe ratios?

For each trial, they test long-only and long-short factor portfolios. Also for each trial, they test the overall sample, economic recession and expansion subsamples (per the National Bureau of Economic Research) and bull and bear market subsamples (per Forbes magazine). Using monthly U.S. stock market factor and sector returns from Kenneth French’s library spanning July 1963 through November 2014, they find that: Keep Reading

“Simple Asset Class ETF Value Strategy” (SACEVS) tests a simple relative value strategy that each quarter allocates funds to one or more of the following three asset class exchange-traded funds (ETF), plus cash, based on degree of undervaluation of measures of the term risk, credit risk and equity risk premiums:

3-month Treasury bills (Cash)
iShares 7-10 Year Treasury Bond (IEF)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR S&P 500 (SPY)

One version of SACEVS (Best Value) picks the most undervalued premium. Another (Weighted) weights all undervalued premiums according to degree of undervaluation. Premium calculations and SACEVS portfolio allocations derive from quarterly average yields for 3-month Constant Maturity U.S. Treasury bills (T-bills), 10-year Constant Maturity U.S. Treasury notes (T-notes) and Moody’s Seasoned Baa Corporate Bonds (Baa). A subscriber asked whether fresh end-of-quarter yields might work better than quarterly average yields. Using monthly S&P 500 Index levels, quarterly S&P 500 earnings and daily T-note, T-bill and Baa yields during March 1989 through March 2015 (limited by availability of earnings data), and quarterly dividend-adjusted closing prices for the above three asset class ETFs during September 2002 through March 2015 (154 months, limited by availability of IEF and LQD), we find that: Keep Reading

“Simple Asset Class ETF Value Strategy” (SACEVS) tests a simple relative value strategy that each quarter allocates funds to one or more of the following three asset class exchange-traded funds (ETF), plus cash, based on degree of undervaluation of measures of the term risk, credit risk and equity risk premiums:

3-month Treasury bills (Cash)
iShares 7-10 Year Treasury Bond (IEF)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR S&P 500 (SPY)

One version of SACEVS (Best Value) picks the most undervalued premium. Another (Weighted) weights all undervalued premiums according to degree of undervaluation. Premium calculations and SACEVS portfolio allocations are quarterly per the arrival rate of new corporate earnings information. The principal benchmark is a quarterly rebalanced portfolio of 60% SPY and 40% IEF. A subscriber asked whether monthly SACEVS updates outperform quarterly updates. Using monthly S&P 500 Index levels, quarterly S&P 500 earnings and monthly average yields for 3-month Constant Maturity U.S. Treasury bills (T-bills), 10-year Constant Maturity U.S. Treasury notes (T-notes) and Moody’s Seasoned Baa Corporate Bonds during March 1989 through March 2015 (limited by availability of earnings data), and monthly dividend-adjusted closing prices for the above three asset class ETFs during September 2002 through March 2015 (154 months, limited by availability of IEF and LQD), we find that: Keep Reading

Is intermediate-term asset class momentum a useful way to generate inputs (return, volatility and correlation forecasts) for a multi-class mean-variance optimization strategy? In their May 2015 paper entitled “Momentum and Markowitz: a Golden Combination”, Wouter Keller, Adam Butler and Ilya Kipnis test the effectiveness of using intermediate-term lookback intervals (1 to 12 months) to generate monthly long-only mean-variance optimized portfolios. They argue that such lookback intervals are more likely than conventional long (multi-year) intervals to provide forecasts that persist during one-month portfolio holding intervals. They name their approach Classical Asset Allocation (CAA). To test CAA, in addition to adopting the practical long-only constraint, they further:

1. Select from the efficient frontier a target annualized portfolio volatility of either 10% (aggressive) or 5% (conservative).
2. Forecast asset returns by averaging results from lookback intervals of 1, 3, 6 and 12 months.
3. Forecast covariances (volatility-correlation relationships) from a 12-month lookback interval.
4. Cap portfolio weights for risky assets at 25%, but do not cap weights for 3-month U.S. Treasury bills (T-bills) and 10-year U.S. Treasury notes (T-notes).
5. Consider three universes of 8, 16 and 39 asset class proxies.
6. Use equal weighting (EW) of all assets in a universe as a benchmark.

They introduce an optimizer program to streamline calculation of optimal portfolio weights. Using monthly total returns for 39 indexes spanning multiple asset classes as available during January 1914 through December 2014, they find that: Keep Reading