Strategic Allocation

As summarized in “Twisting Buffett’s Preferred Stocks-bonds Allocation”: (1) Warren Buffett’s preferred fixed asset allocation of 90% stocks and 10% short‐term government bonds (90-10), rebalanced annually, is sensible for U.S. markets; and, (2) investors may be able to beat this allocation modestly by adding simple annual dynamics. Are findings similar internationally? In his July 2016 paper entitled “Global Asset Allocation in Retirement: Buffett’s Advice and a Simple Twist”, Javier Estrada extends his analysis of U.S. markets to 20 other countries. He assumes a 1,000 (local currency unit) nest egg to start a 30‐year retirement. Annual withdrawals (either 4% or 3% of the initial amount, adjusted annually for inflation) and rebalancing to the target allocation occur at the beginning of each year. The first 30‐year retirement interval is 1900‐1929 and the last 1985‐2014, for a total of 86 rolling intervals. He first compares performances of eight fixed stocks-bonds allocations, rebalanced annually, ranging from 100-0 to 30-70. He then compares a fixed 90-10 allocation to one with a dynamic twist that, at the end of each year, compares the stock market’s annualized total return over the last five years to its annualized total return since the beginning of the sample. If 5-year performance exceeds long-term performance, the annual withdrawal comes from stocks with rebalancing to 90-10. If long-term performance exceeds 5-year performance, the annual withdrawal comes from bonds with no portfolio rebalancing (giving stocks time to recover). He focuses on average portfolio failure rate (running out of money within 30 years) and average terminal wealth across countries as key performance metrics. Using annual stock and short-term government bond real total returns (adjusted by local inflation rate) in local currencies for 21 countries as compiled by Dimson‐Marsh‐Staunton for 1900 through 2014, he finds that: Keep Reading

What is the best way to combine styles (smart betas) in one portfolio? In their June 2016 paper entitled “Long-Only Style Investing: Don’t Just Mix, Integrate”, Shaun Fitzgibbons, Jacques Friedman, Lukasz Pomorski and Laura Serban compare two approaches to long-only combined equity style investing:

1. Mixed portfolio – simply picks stocks from single-style portfolios.
2. Integrated portfolio – first combines single-style rankings into an overall score for each stock and then builds a portfolio based on top overall scores.

They focus on combining momentum stocks (highest return from 12 months ago to one month ago) and value stocks (high book-to-market ratio). They first employ simulated data to illustrate differences in stock selection between the two approaches. They then compare net performances for equally weighted, monthly rebalanced mixed and integrated combinations of liquid global stocks. Using monthly data for large-capitalization stocks from developed markets (roughly the MSCI World Index components) during February 1993 through December 2015, they find that: Keep Reading

Matt Hall, cofounder and president of Hill Investment Group, introduces his 2016 book, Odds On: The Making of an Evidence-Based Investor, by stating that: “…the evidence-based movement has been studying market data and academic research to identify the groups of stocks and other investments that provide better odds of long-term success. …I’m inviting you to learn how evidence-based investing could change your life…” Based on his experience, he concludes that: Keep Reading

How hard is it to beat equal weighting in constructing a portfolio of attractive common stocks? In his May 2016 paper entitled “Naive Diversification Isn’t so Naive after All”, Mike Dickson compares performances of 15 portfolio construction methods applied to eight portfolios of stocks with high expected returns. Construction methods include equal weighting, two versions of minimum volatility, three versions of mean-variance optimization, eight versions of reward-to-risk timing (six of which involve factor models) and a characteristic-based scheme that each year estimates stock weights based on market capitalization, book-to-market ratio, gross profitability, investment, short-term reversal and momentum. The eight portfolios consist of stocks with the top 10% or top 20% of expected returns based on rolling averages of multivariate cross-sectional regression coefficients for these same characteristics, formed with or without momentum and with or without microcaps (capitalizations less than the 20% percentile for NYSE stocks). He estimates trading frictions as 1% of the value traded each month in rebalancing to specified portfolio weights. Using monthly data for a broad sample of U.S. common stocks during July 1963 through December 2013 (with evaluated returns commencing July 1973), he finds that: Keep Reading

Can individual investors practically implement mean-variance optimization in a multi-asset class context? In their April 2016 paper entitled “Asset Allocation: A Recommendation for Resolving the Collision between Theory and Practice”, Larry Prather, James McCown and Ron Shaw describe how individual investors can construct and maintain a low-cost optimal (maximum Sharpe ratio) multi-class portfolio via the Excel Solver function. They consider four criteria in selecting asset class proxies: (1) market capitalization-weighted coverage of a wide variety of investable assets; (2) small initial investment; (3) low annual expenses; and, (4) versions that investors can short. Based on these criteria, they select five Vanguard index mutual funds and three precious metals:

• Vanguard Total Stock Market Index Fund Investor Shares (VTSMX), capturing the U.S. equity market.
• Vanguard Total International Stock Index Fund Investor Shares (VGTSX), representing 98% of the capitalization of non-U.S. equity markets.
• Vanguard Emerging Markets Stock Index Fund Investor Shares (VEIEX), supplementing VGTSX to better capture emerging market equities.
• Vanguard Total Bond Market Index Fund Investor Shares (VBMFX), providing broad exposure to U.S. investment grade bonds.
• Vanguard REIT Index Fund Investor Shares (VGSIX), providing broad exposure to U.S. Real Estate Investment Trusts (REIT).
• Spot gold, platinum and palladium, offering safe haven and currency exchange rate protection.

These mutual funds and metals have exchange-traded fund (ETF) analogs, supporting optimization with short selling. They assume a constant risk-free rate of 3%. Using daily mutual fund returns and spot metals prices during September 1998 through June 2015, they find that: Keep Reading

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