Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for May 2024 (Final)

Momentum Investing Strategy (Strategy Overview)

Allocations for May 2024 (Final)
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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.

More Aggressive Pursuit of the Credit Premium in SACEVS?

Noting that iShares iBoxx $ Investment Grade Corporate Bond ETF (LQD) and iShares 20+ Year Treasury Bond ETF (TLT) exhibit a moderately positive return correlation, a subscriber asked about substituting Vanguard High-Yield Corporate Fund Investor Shares (VWEHX) for LQD in the Simple Asset Class ETF Value Strategy (SACEVS) to exploit undervaluation of the credit risk premium. 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.

To investigate, we compare performances of SACEVS Best Value and SACEVS Weighted portfolios with either VWEHX or LQD as the credit risk asset. Using monthly total returns for SACEVS assets during July 2002 through September 2022, we find that:

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Optimal Monthly Cycle for SACEMS?

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:

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Global Safe Retirement Withdrawal Rate

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

SACEMS with Three Copies of Cash

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:

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Complex Multi-Asset Class Momentum Strategy

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:

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Add Managed Futures Fund Index to SACEMS?

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

Resilience of Low-volatility Stocks

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:

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Useless Asset Class Return Forecasts?

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

Complex Offensive/Defensive Asset Class Momentum

Can investors achieve attractive asset class momentum strategy performance by applying slow relative momentum to different risk-on (offensive) and risk-off (defensive) sets of exchange-traded funds (ETF), and fast absolute momentum to a separate risk mode identification set of ETFs? In his July 2022 paper entitled “Relative and Absolute Momentum in Times of Rising/Low Yields: Bold Asset Allocation (BAA)”, Wouter Keller presents an aggressive asset allocation strategy that combines features of his previous models (Protective Asset Allocation, Vigilant Asset Allocation and Defensive Asset Allocation). This Bold Asset Allocation strategy consists of the following baseline asset universes and rules:

  1. When none (any) of SPY, VWO, VEA and BND have negative weighted returns over the past 1, 3, 6 and 12 months, use the offensive (defensive) mode. Weights for past 1, 3, 6 and 12 months returns are 12, 4, 2 and 1, respectively.
  2. When in offensive mode, hold the equal-weighted six of SPY, QQQ, IWM, VGK, EWJ, VWO, VNQ, DBC, GLD, TLT, HYG and LQD with the highest ratios of current monthly price to average of the last 13 prices (including current price).
  3. When in defensive mode, hold the equal-weighted three of TIP, DBC, BIL, IEF, TLT, LQD and BND with the highest ratios of current monthly price to average of the last 13 prices (including current price), except replace with BIL any of these top three with past price ratio less than that of BIL.

He reforms the BAA portfolio monthly, assuming constant 0.1% 1-way trading frictions. Using modeled monthly total returns prior to ETF inception and actual monthly total returns after inception for each specified ETF during December 1970 through Jun 2022, he finds that:

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Update of Findings for a Highly Influential Asset Allocation Paper

“A Quantitative Approach to Tactical Asset Allocation” is a highly influential paper (over 253,000 downloads from SSRN) about asset allocation based on trend following, with the original version posted in early 2007 and a revision in early 2013. The strategy in that paper applies a 10-month simple moving average (SMA10) timing rule separately to each of five total return indexes as components of an equally weighted, monthly rebalanced portfolio: (1) S&P 500 Index; (2) 10-Year Treasury note constant duration index; (3) MSCI EAFE international developed markets index; (4) Goldman Sachs Commodity Index (GSCI); and, (5) National Association of Real Estate Investment Trusts index. Specifically, at the end of each month, the model enters from cash (exits to cash) any index crossing above (below) its SMA10. Entry and exit dates are the same as signal dates (requiring some anticipation of signals before the close). This paper (summarized in “Asset Allocation Based on Trends Defined by Moving Averages”) spawned hundreds (thousands?) of trend following/momentum-based asset allocation strategies since publication, including to some degree the Simple Asset Class ETF Momentum Strategy (SACEMS). How well does the original strategy perform during ascendance of exchange-trade funds (ETF) as asset class proxies? To evaluate, we apply the strategy (QA-TAA) to the following five asset class proxy ETFs and cash:

  • SPDR S&P 500 ETF Trust (SPY)
  • iShares Barclays 20+ Year Treasury Bond ETF (TLT)
  • iShares MSCI EAFE ETF (EFA)
  • Invesco DB Commodity Index Tracking Fund (DBC)
  • Vanguard Real Estate Index Fund (VNQ)
  • 3-month Treasury bills (Cash)

We consider buying and holding SPY, the SMA10 ruled applied to SPY (SPY:SMA10) and an equally weighted, monthly rebalanced portfolio of the five asset class ETFs (EW All) as benchmarks. Using monthly dividend-adjusted prices for the specified assets during February 2006 (limited by DBC) through June 2022, we find that:

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