Strategic Allocation

Is there a best time of the month for measuring momentum within the Simple Asset Class ETF Momentum Strategy (SACEMS)? To investigate, we compare 21 variations of baseline SACEMS by shifting the monthly return calculation cycle from 10 trading days before the end of the month (EOM) to 10 trading days after EOM. For example, an EOM+5 cycle ranks assets based on closing prices five trading days after EOM each month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using daily dividend-adjusted prices for SACEMS assets during mid-February 2006 through mid-October 2022, we find that:

Keep Reading

Does a constant real annual withdrawal rate of 4% of household savings at retirement, derived from U.S. asset return experience, really protect against financial ruin? In their September 2022 paper entitled “The Safe Withdrawal Rate: Evidence from a Broad Sample of Developed Markets”, Aizhan Anarkulova, Scott Cederburg, Michael O’Doherty and Richard Sias consider data from 38 developed countries to assess safe withdrawal rates. This sample mitigates survivorship/easy data biases of the U.S. experience by including multiple left-tail instances of trading halts, wars, hyperinflation and other extreme events. They use this data to model retirement portfolio performance via stationary block bootstrap simulation, with longevity risk incorporated from U.S. Social Security Administration mortality tables. Their base case examines joint investment-longevity outcomes for a couple retiring in 2022 at age 65 using a 60% domestic stocks-40% bonds (60-40) portfolio strategy. They also look at other fixed stocks-bonds allocations and investment strategies pursued by target-date funds. Using monthly (local) real returns for domestic stocks, international stocks, bonds and bills as available for 38 developed countries during 1890 through 2019, they find that: Keep Reading

Subscribers have questioned selecting assets with negative past returns within the “Simple Asset Class ETF Momentum Strategy” (SACEMS). Inclusion of Cash as one of the assets in the SACEMS universe of exchange-traded funds (ETF) prevents the SACEMS Top 1 portfolio from holding an asset with negative past returns. To test full dual momentum versions of SACEMS equally weighted (EW) Top 2 and EW Top 3 SACEMS portfolios, we add two more copies of Cash to the universe, thereby preventing both of them from holding assets with negative past returns. We focus on the effects of adding two copies of Cash on the holdings, compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) of SACEMS EW Top 2 and EW Top 3 portfolios. Using monthly dividend adjusted closing prices for the asset class proxies and the yield for Cash during February 2006 through September 2022, we find that:

Keep Reading

Can investors beat a 60/40 stocks/bonds portfolio via a long-only momentum strategy applied to many asset class proxies? In their September 2022 paper entitled “Long-Only Multi-Asset Momentum: Searching for Absolute Returns”, Enrique Zambrano and Carlos Rizzolo explore variations of a long-only multi-asset momentum strategy. Strategy elements are:

• The asset universe is SPY, QQQ, IWM, VGK, EWJ, EEM, VNQ, DBC, DBA, GLD, LQD, HYG, TLT, SHV, IEF and Cash (or underlying indexes dovetailed with actual short fund histories). Cash, SHV and IEF are risk-off assets, and all others are risk-on assets.
• Measure momentum base on: (1) total return; (2) price relative to a simple moving average (SMA); and, (3) risk-adjusted returns that penalize assets with high return dispersion.
• Use signals for three lookback intervals for returns (3, 6 and 12 months) and three SMAs (50, 100 and 200 days). Aggregate signals from the three momentum measurements and three lookback intervals based on either an ensemble method or a scoring approach.
• For some tests, penalize (favor) assets with high (low) correlations with a portfolio of the rest of the assets using either a multiplier or a divisor.
• At the end of each month, rank the 13 risk-on assets according to aggregated-signal momentum and reform an equal-weighted portfolio of the top five assets. If any risk-on assets have negative momentum (with two ways to determine negative for the scoring approach), substitute for it the risk-off asset with highest momentum.

They focus on total strategy return over the full sample period, compound annual growth rate (CAGR), annualized standard deviation of returns (volatility), ratio of CAGR to volatility, maximum drawdown (MaxDD) and the probability of 1-year rolling returns being positive or higher than -5%. They also present performance data for the nine individual momentum measurement-lookback interval combinations. They further consider a strategy variation that limits exposure to risk-on assets based on the number of assets with negative returns (momentum breadth). Using daily prices for all assets as available from the end of December 2002 through the end of May 2022 (or daily associated index levels before respective funds are available), they find that:

Keep Reading

A subscriber asked for an update of the performance comparison between 50% Simple Asset Class ETF Value Strategy (SACEVS) Best Value-50% Simple Asset Class ETF Momentum Strategy (SACEMS) equal-weighted top two (EW Top 2), rebalanced monthly (SACEVS-SACEMS 50-50), and a strategy that is each week in stocks or bonds according to whether the return on lumber is greater than the return on gold over the past 13 weeks (L-G Strategy). To test the latter strategy we use the following exchanged-traded fund (ETF) proxies:

• SPDR S&P 500 (SPY) for stocks.
• iShares 20+ Year Treasury Bond (TLT) for bonds.
• Invesco MSCI Global Timber (CUT) for lumber.
• SPDR Gold Shares (GLD) for gold.

Using weekly dividend-adjusted prices for SPY, TLT, CUT and GLD during early February 2008 (limited by inception of CUT) through mid-September 2022 and roughly matched start and stop performance for monthly SACEVS-SACEMS 50-50 , we find that:

Keep Reading

The “Simple Asset Class ETF Value Strategy” (SACEVS) seeks diversification across a small set of asset class exchanged-traded funds (ETF), plus a monthly tactical edge from potential undervaluation of three risk premiums:

1. Term – monthly difference between the 10-year Constant Maturity U.S. Treasury note (T-note) yield and the 3-month Constant Maturity U.S. Treasury bill (T-bill) yield.
2. Credit – monthly difference between the Moody’s Seasoned Baa Corporate Bonds yield and the T-note yield.
3. Equity – monthly difference between S&P 500 operating earnings yield and the T-note yield.

Premium valuations are relative to historical averages. How might this strategy react to changes in the Effective Federal Funds Rate (EFFR)? Using end-of-month values of the three risk premiums, EFFR, total 12-month U.S. inflation and core 12-month U.S. inflation during March 1989 (limited by availability of operating earnings data) through August 2022, we find that: Keep Reading

Referencing Eurekahedge CTA/Managed Futures Hedge Fund Index (Eurekahedge) used as a benchmark in “Are Managed Futures ETFs Working?”, a subscriber asked about adding a managed futures fund index to the Simple Asset Class ETF Momentum Strategy (SACEMS) asset universe. To investigate, we apply the methodology of  “SACEMS Portfolio-Asset Addition Testing” by adding either Eurekahedge or the SG Trend Index (SG Trend) to the base set of nine assets. We consider effects on Top 1, equal-weighted top two (EW Top 3) and EW Top 3 SACEMS portfolios. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics. Using monthly total returns for the base set of assets and the two managed futures fund indexes during February 2006 through August 2022, we find that: Keep Reading

The body of research indicates that low-volatility/low-beta stock investing suppresses exposure to overall equity market risk. Does it work equally well for different sources of such risk? In his September 2022 paper entitled “Macro Risk of Low-Volatility Portfolios”, David Blitz examines the separate exposures of low-volatility portfolios to interest rate, implied volatility, liquidity, commodity, sentiment, macroeconomic and climate (CO₂ emissions) risk factors. Specifically, he compares the contemporaneous interactions with these risks of the MSCI USA Minimum Volatility Index (based on minimum variance optimization), the S&P 500 Low Volatility Index (the 100 inverse volatility-weighted stocks in the S&P 500 with the lowest volatilities over the past one year) and the S&P 500 Index as the market benchmark. He measures risk factor-index interactions via univariate regressions of monthly excess returns versus monthly risk factor values. He also considers risk factor interactions with ten (decile) equally weighted portfolios of the 1,000 largest U.S. stocks at each point in time sorted by preceding 36-month volatilities. Using monthly total returns for the indexes/portfolios in U.S. dollars in excess of the risk-free rate and monthly risk factor values during January 1991 through December 2021, he finds that:

Keep Reading

Should investors believe that long-term asset class return forecasts are useful? In his brief August 2022 paper entitled “How Accurate are Capital Market Assumptions, and How Should We Use Them?”, Mike Sebastian employs 10 years of annual Survey of Capital Market Assumptions by Horizon Actuarial Services to assess the industry’s ability to gauge 10-year future asset class returns. This survey presents inputs from leading consulting and investment management firms and includes composite, minimum and maximum forecasted returns for 15 asset classes. Using forecast data for 2012 through 2021, he finds that: Keep Reading

One concern about simple momentum strategies is data snooping bias impounded in selection of the lookback interval(s) used to measure asset momentum. To circumvent this concern, we consider the following argument:

• The CBOE Volatility Index (VIX) broadly indicates the level of financial markets distress and thereby the tendency of investors to act complacently (when VIX is low) or to act in panic (when VIX is high).
• Complacency translates to resistance in changing market outlook (long memory and lookback intervals), while panic translates to rapid changes of mind (short memory and short lookback intervals).
• The inverse of VIX is therefore indicative of the actual aggregate current lookback interval affecting investor actions.

We test this argument by:

• Setting a range for VIX using monthly historical closes from January 1990 through July 2002, before the sample period used for any tests of the Simple Asset Class ETF Momentum Strategy (SACEMS).
• Applying buffer factors to the bottom and top of this actual inverse VIX range to recognize that it could break above or below the historical range in the future.
• Segmenting the buffer-extended inverse VIX range into 12 equal increments and mapping these increments by rounding into momentum lookback intervals of 1 month (lowest segment) to 12 months (highest segment).
• Applying this same method to future end-of-month inverse VIX levels to select the SACEMS lookback interval for the next month.

We test the top one (Top 1), the equally weighted top two (EW Top 2) and the equally weighted top three (EW Top 3) SACEMS portfolios. We focus on compound annual growth rate (CAGR), maximum drawdown based on monthly measurements, annual returns and Sharpe ratio as key performance statistics. To calculate excess annual returns for the Sharpe ratio, we use average monthly yield on 3-month Treasury bills during a year as the risk-free rate for that year. Benchmarks are these same statistics for tracked SACEMS. Using monthly levels of VIX since inception in January 1990 and monthly dividend-adjusted prices of SACEMS assets since February 2006 (initial availability of a commodities ETF), all through July 2022, we find that: Keep Reading