Strategic Allocation

Does simple asset price trend following based on 10-month simple moving average (SMA10) reliably boost the performance of retirement portfolios? In their July 2017 paper entitled “Can Sustainable Withdrawal Rates Be Enhanced by Trend Following?”, Andrew Clare, James Seaton, Peter Smith and Steve Thomas compare effects of asset class diversification and trend following on safe withdrawal rates from UK retirement portfolios. They consider 60-40 UK stocks-bonds, 30-70 UK stocks-bonds and equally weighted UK stocks, global stocks, bonds, commodities and UK real estate (EW Multi-asset). They further consider risk parity (RP) multi-asset (each class weighted by the inverse of its prior-year volatility) and 100% global stocks (equally weighted across five regions). They focus on a 20-year retirement period (but also consider 30-year), assume annual withdrawals the first day of each year and ignore taxes and rebalancing frictions. They use both in-sequence historical asset returns and Monte Carlo simulations (random draws with replacement from the historical annual returns of each portfolio). They apply trend following separately to each asset by holding the asset (cash) when asset price is above (below) its SMA10. Their key portfolio performance metric is Perfect Withdrawal Rate (PWR), the constant real (inflation-adjusted) withdrawal rate as a percentage of initial portfolio value that exactly exhausts the portfolio at the end of the retirement period. Using monthly total returns in pounds sterling for the selected asset classes and values of the UK consumer price index during 1970 through 2015, they find that: Keep Reading

A subscriber asked about extending “Simple Momentum Strategy Applied to TSP Funds” back in time to 1988. That test employs the following five funds, all available to U.S. federal government employees via the Thrift Savings Plan (TSP) as of January 2001:

G Fund: Government Securities Investment Fund (G)
F Fund: Fixed Income Index Investment Fund (F)
C Fund: Common Stock Index Investment Fund (C)
S Fund: Small Cap Stock Index Investment Fund (S)
I Fund: International Stock Index Investment Fund (I)

S Fund and I Fund data limit the combined sample period. To extend the test back to first availability of G Fund, F Fund and C Fund data in February 1988, we use Vanguard Small Cap Index Investors Fund (NAESX) as a proxy for the S Fund and Vanguard International Value Investors Fund (VTRIX) as a proxy for the I Fund prior to 2001. We first perform a sensitivity test of fund ranking (lookback) intervals ranging from one to 12 months on the following monthly reformed portfolios: the winner fund (Top 1); an equally weighting of the top two funds (EW top 2); an equally weighting of the Top 3 funds (EW Top 3); and, an equal weighting of all five funds (EW All). We then perform detailed tests using a representative lookback interval. Using monthly returns for the five TSP funds as available during February 1988 through June 2017 (351 months) and monthly returns for NAESX and VTRIX during February 1988 through December 2000, we find that:

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Is there a preferred frequency and are there preferred months for rebalancing conventional asset class portfolio holdings? To investigate we consider annual, semiannual and quarterly rebalancing of a simple portfolio targeting a 60-40 stocks-bonds mix. We consider all possible combinations of calendar month ends as rebalancing points. We ignore rebalancing (and dividend-reinvestment) frictions and tax implications, thereby giving an advantage to frequent rebalancing. We focus on compound annual growth rate (CAGR) as the critical portfolio performance metric. Using dividend-adjusted monthly closes for SPDR S&P 500 (SPY) to represent stocks and Vanguard Total Bond Market Index (VBMFX) to represent bonds over the period January 1993 (SPY inception) through June 2017 (about 24 years), we find that: Keep Reading

Does an asset class breadth rule work better than a class-by-class exclusion rule for momentum strategy crash protection? In their July 2017 paper entitled “Breadth Momentum and Vigilant Asset Allocation (VAA): Winning More by Losing Less”, Wouter Keller and Jan Keuning introduce VAA as a dual momentum asset class strategy aiming at returns above 10% with drawdowns less than -20% deep. They specify momentum as the average of annualized total returns over the past 1, 3, 6 and 12 months. This specification gives greater weight to short lookback intervals than a simple average of past returns over these intervals. Specifically, they:

1. Each month rank asset class proxies based on momentum.
2. Each month select a “cash” holding as the one of short-term U.S. Treasury, intermediate-term U.S. Treasury and investment grade corporate bond funds with the highest momentum. 
3. Set (via backtest) a breadth protection threshold (B). When the number of asset class proxies with negative momentum (b) is equal to or greater than B, the allocation to “cash” is 100%. When b is less than B, the base allocation to “cash” is b/B.
4. Set (via backtest) the number of top-performing asset class proxies to hold (T) in equal weights. When the base allocation to “cash” is less than 100% (so when b<B), allocate the balance to the top (1-b/B)T asset class proxies with highest momentum (irrespective of sign).
5. Mitigate portfolio rebalancing intensity (when B and T are different) by rounding fractions b/B to multiples of 1/T.

They construct four test universes from: a short sample of 17 (mostly simulated) exchange traded fund (ETF)-like global asset class proxies spanning December 1969 through December 2016; and, a long sample of 21 index-like U.S. asset classes spanning December 1925 through December 2016. After reserving the first year for initial momentum calculations, they segment each sample into halves for in-sample optimization of B and T and out-of-sample testing. For all cases, they apply 0.1% one-way trading frictions for portfolio changes. Their key portfolio performance metrics are compound annual growth rate (CAGR), maximum drawdown (MaxDD) and a composite of the two. Using monthly returns for the selected ETF-like and index-like assets over respective sample periods, they find that:

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How differently does the “Simple Asset Class ETF Value Strategy” (SACEVS) perform when the U.S. stock market rises and falls? This strategy seeks to exploit relative valuation of the term risk premium, the credit (default) risk premium and the equity risk premium via exchange-traded funds (ETF). To investigate, because the sample period available for mutual funds is much longer than that available for ETFs, we use instead data from “SACEVS Applied to Mutual Funds”. Specifically, each month we reform a Best Value portfolio (picking the asset associated with the most undervalued premium, or cash if no premiums are undervalued) and a Weighted portfolio (weighting assets associated with all undervalued premiums according to degree of undervaluation, or cash if no premiums are undervalued) using the following four assets:

• Monthly average 3-Month Treasury Bill (T-bill) Secondary Market Rate as an approximation of the return on Cash.
• Vanguard GNMA Investor Shares (VFIIX).
• Vanguard Long-Term Investment Grade Investor Shares (VWESX).
• Vanguard US Growth Investor Shares (VWUSX).

The benchmark is a monthly rebalanced portfolio of 60% stocks and 40% U.S. Treasuries (60-40 VWUSX-VFIIX). We say that stocks rise (fall) during a month when the return for VWUSX is positive (negative) during the SACEVS holding month. Using monthly risk premium estimates, SR and LR, and Best Value and Weighted returns during June 1980 through June 2017 (444 months), we find that:

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A subscriber asked how the “Simple Asset Class ETF Value Strategy” (SACEVS) performs when interest rates rise. This strategy seeks to exploit relative valuation of the term risk premium, the credit (default) risk premium and the equity risk premium via exchange-traded funds (ETF). To investigate, because the sample period available for mutual funds is much longer than that available for ETFs, we use instead data from “SACEVS Applied to Mutual Funds”. Specifically, each month we reform a Best Value portfolio (picking the asset associated with the most undervalued premium, or cash if no premiums are undervalued) and a Weighted portfolio (weighting assets associated with all undervalued premiums according to degree of undervaluation, or cash if no premiums are undervalued) using the following four assets:

• Monthly average 3-Month Treasury Bill (T-bill) Secondary Market Rate as an approximation of the return on Cash.
• Vanguard GNMA Investor Shares (VFIIX).
• Vanguard Long-Term Investment Grade Investor Shares (VWESX).
• Vanguard US Growth Investor Shares (VWUSX).

The benchmark is a monthly rebalanced portfolio of 60% stocks and 40% U.S. Treasuries (60-40 VWUSX-VFIIX). We use the T-bill yield as the short-term interest rate (SR) and the 10-year Constant Maturity U.S. Treasury note (T-note) yield as the long-term interest rate (LR). We say that each rate rises or falls when the associated average monthly yield increases or decreases during the SACEVS holding month. Using monthly risk premium estimates, SR and LR, and Best Value and Weighted returns during June 1980 through June 2017 (444 months), we find that:

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A subscriber asked for augmentation of “SACEMS at Weekly and Biweekly Frequencies” to determine whether bimonthly (every two months) measurement of asset class momentum works better than monthly measurement as used in “Simple Asset Class ETF Momentum Strategy” (SACEMS). To investigate, we apply a bimonthly strategy to the following eight asset class exchange-traded funds (ETF), plus cash:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 2000 Index (IWM)
SPDR S&P 500 (SPY)
iShares Barclays 20+ Year Treasury Bond (TLT)
Vanguard REIT ETF (VNQ)
3-month Treasury bills (Cash)

We use the same lookback interval as for basic SACEMS and consider portfolios of past ETF winners based on Top 1 and on equally weighted (EW) Top 2 and Top 3. Since a bimonthly lookback interval uses every other set of monthly signals, we consider two variations: (1) start at the end of July 2006, when signals are first available for the entire set of ETFs, and end with May 2017; and, (2) start at the end of August 2006 and end with June 2017. We consider as benchmarks an equally weighted portfolio of all ETFs, rebalanced monthly (EW All) and basic monthly SACEMS. We focus on gross compound annual growth rates (CAGR), annual returns and maximum drawdowns (MaxDD) as performance metrics. Using monthly dividend-adjusted closing prices for the asset class proxies and the yield for Cash during February 2006 (when all ETFs are first available) through June 2017 (137 months), we find that: Keep Reading

Michael Covel prefaces the 2017 Fifth Edition of his book, Trend Following: How to Make a Fortune in Bull, Bear, and Black Swan Markets, by stating that: “The 233,092 words in this book are the result of my near 20-year hazardous journey for the truth about this trading called trend following. …Trend following…aims to capture the majority of a connected market trend up or down for outsize profit. It is designed for potential gain in all major asset classes–stocks, bonds, metals, currencies, and hundreds of other commodities. …if you want outside-the-the-box different, the truth of how out-sized returns are made without any fundamental predictions or forecasts, this is it. And if you want the honest data-driven proof, I expect my digging will give everyone the necessary confidence to break their comfort addiction to the box they already know and go take a swing at making a fortune…” Based on his experience as a trader/portfolio manager and the body of trend following research, he concludes that: Keep Reading

What is the most effective way to hedge against equity market crashes? In their June 2017 paper entitled “The Best Strategies for the Worst Crises”, Michael Cook, Edward Hoyle, Matthew Sargaison, Dan Taylor and Otto Van Hemert examine active and passive strategies with potential to generate positive returns during the worst crises. They test these strategies across the seven S&P 500 Index drawdowns of more than 15% during 1985 through 2016. They focus on two active strategies:

1. Time-series (intrinsic or absolute) momentum long-short portfolio comprised of 50 liquid futures and forwards series spanning currencies, equity indexes, bonds, agricultural products, energy and metals. They consider return lookback intervals of 1, 3 and 12 months. They apply risk adjustments, risk allocations by class and finally a scale factor targeting 10% annualized portfolio volatility. They consider three extensions of the strategy that preclude or restrict positive exposure to equity market beta.
2. Quality factor long-short portfolios comprised of intermediate and large capitalization U.S. stocks. These portfolios ares long (short) the highest-ranked (lowest-ranked) stocks, as selected based on one of 18 metrics representing profitability, growth in profitability, safety and payout. Rankings are risk-adjusted and portfolios are equity market beta-neutral. They again apply a scale factor targeting 10% annualized portfolio volatility. They also consider several composite factor portfolios by averaging individual factor rankings and weighting for dollar neutrality, beta neutrality, sector neutrality and/or volatility balancing.

Using daily data for all indicated assets during 1985 through 2016, they find that: Keep Reading

Is there a way to enhance the ability of the cyclically-adjusted price-to-earnings ratio (P/E10 or CAPE) to predict U.S. stock market returns by incorporating real interest rates? In their June 2017 paper entitled “Improving U.S. Stock Return Forecasts: A ‘Fair-Value’ Cape Approach”, Joseph Davis, Roger Aliaga-Diaz, Harshdeep Ahluwalia and Ravi Tolani introduce “fair-value” CAPE that accounts for a dynamic, positive relationship between real 10-year U.S. Treasury note (T-note) yield (cost of capital) and real earnings yield (return on equity). They hypothesize that a lower real T-note yield should imply a lower earnings yield and thus a higher fair-value CAPE. Their use of fair-value CAPE to forecast stock market return involves:

• Each month, execute a multiple vector autoregression of the logarithms of the following five variables separately for each of the last 12 months: (1) inverse of CAPE; (2) expected real T-note yield based on a 10-year U.S. inflation forecast; (3) U.S. inflation; (4) realized S&P 500 Index price volatility over the last 12 months; and, (5) realized volatility of changes in real T-note yield over the last 12 months. Their 10-year inflation forecast is the average of 120 monthly forecasts generated via autoregression of the U.S. consumer price index over a 30-year rolling window.
• Each month, forecast 10-year stock market return (see the chart below) by summing: (1) percentage change in CAPE from the preceding vector autoregression; (2) constant earnings growth equal to its long-term average; and, (3) dividend yield calculated as earnings yield times the historical payout ratio.

They then compare out-of-sample forecasts of 10-year U.S. stock market returns for 1960 through 2016 and 1985 through 2016 generated by fair-value CAPE and two conventional CAPEs: Shiller CAPE based on Generally Accepted Accounting Principles (GAAP); and, Siegel CAPE based on National Income and Product Accounts (NIPA) earnings. Using Shiller’s data and NIPA earnings during 1950 through 2016, they find that: Keep Reading