Strategic Allocation

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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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

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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“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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In response to Very Simple Asset Class ETF Momentum Strategy (VSACEMS), a subscriber requested evaluation of an alternative VSACEMS that considers only the following three exchange-traded funds (ETF):

• SPDR S&P 500 (SPY)
• iShares Barclays 20+ Year Treasury Bond (TLT)
• Invesco DB Commodity Index Tracking (DBC)

To evaluate, we test a strategy that each month picks the one of these ETFs with the highest total return over a set momentum ranking (lookback) interval. We consider lookback intervals of one to 12 months. We then select one of these lookback intervals and generate performance statistics similar to those for SACEMS. We consider three benchmarks:

1. SPY – buy and hold SPY.
2. SPY:SMA10 Cash – Hold SPY (3-month U.S. Treasury bills) when SPY is above (below) its 10-month simple moving average (SMA10) at the end of the prior month.
3. SPY:SMA10 TLT – Hold SPY (TLT) when SPY is above (below) its SMA10 at the end of the prior month.

Using monthly dividend-adjusted prices for the above three assets during February 2006 (limited by DBC) through April 2022, we find that: Keep Reading

A subscriber suggested review of “Accelerating Dual Momentum [ADM] Investing”, which allocates all funds to U.S. stocks, international (ex-U.S.) small-capitalization stocks or long-term U.S. Treasury bonds, as follows:

1. Each month, calculate for each of the two equity assets the sum of its 1-month, 3-month and 6-month past returns.
2. If both sums are negative, buy U.S. Treasury bonds.
3. If both sums are not negative, buy the equity asset with the higher sum.

To investigate, we apply these rules to three exchange-traded funds (ETF):

• SPDR S&P 500 (SPY) to represent U.S. stocks.
• iShares MSCI EAFE Small-Cap ETF (SCZ) to represent international small stocks.
• iShares 20+ Year Treasury Bond (TLT) to represent long-term U.S. Treasury bonds.

Using end-of-month dividend-adjusted prices of these ETFs during December 2007 (limited by SCZ) through April 2022, we find that: Keep Reading

The “Simple Asset Class ETF Value Strategy” (SACEVS) allocates across 3-month Treasury bills (Cash, or T-bill), iShares 20+ Year Treasury Bond (TLT), iShares iBoxx $ Investment Grade Corporate Bond (LQD) and SPDR S&P 500 (SPY) according to the relative valuations of term, credit and equity risk premiums. Does applying a simple moving average (SMA) filter to SACEVS allocations improve its performance? Since many technical traders use a 10-month SMA (SMA10), we apply SMA10 filters to dividend-adjusted prices of TLT, LQD and SPY allocations. If an allocated asset is above (below) its SMA10, we allocate as specified (to Cash). This rule does not apply to any Cash allocation. We focus on gross compound annual growth rates (CAGR), maximum drawdowns (MaxDD) and annual Sharpe ratios (using average monthly T-bill yield during a year as the risk-free rate for that year) of SACEVS Best Value and SACEVS Weighted portfolios. We compare to baseline SACEVS as currently tracked and to the SMA rule applied to a 60%-40% monthly rebalanced SPY-TLT benchmark portfolio (60-40). Finally, we test sensitivity of main findings to varying the SMA lookback interval. Using SACEVS historical data, monthly dividend-adjusted closing prices for the asset class proxies and yield for Cash during July 2002 (the earliest all funds are available) through March 2022, we find that:

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“SACEMS with Momentum Breadth Crash Protection” evaluates in depth the potential of a simple momentum breadth rule to improve performance of the Simple Asset Class ETF Momentum Strategy (SACEMS). This rule forces the model to all cash when fewer than some threshold of the non-cash SACEMS assets have positive returns over a specified lookback interval. Do major findings of that evaluation still hold? To update, we repeat some of the analyses with the minor changes since made to SACEMS plus recent data. We focus on compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) for the Top 1, equal-weighted (EW) Top 2 and EW Top 3 SACEMS portfolios. We look at all possible momentum breadth thresholds for the baseline SACEMS lookback interval. We then consider lookback intervals ranging from one to 12 months for a specific momentum breadth threshold. Using monthly dividend-adjusted closing prices for SACEMS assets and the T-bill yield during February 2006 through February 2022, we find that:

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A subscriber proposed adding Utilities Select Sector SPDR Fund (XLU) to the Simple Asset Class ETF Momentum Strategy (SACEMS) asset universe based on the relatively low correlation of XLU with the broad U.S. stock market. To investigate, we:

• Expand the SACEMS asset universe to include XLU.
• Generate performance data for this expanded universe for the SACEMS Top 1, equal-weighted (EW) Top 2 and EW Top 3 portfolios.
• Compare results to those for baseline SACEMS portfolios.

Using inputs during February 2006 (inception of DBC as a proxy for commodities) through February 2022, we find that: Keep Reading

A subscriber asked whether the optimal momentum ranking (lookback) interval for the “Simple Asset Class ETF Momentum Strategy” (SACEMS) shrinks during bear markets for U.S. stocks. To investigate, we compare SACEMS monthly performance statistics when the S&P 500 Index at the previous monthly close is above (bull market) or below (bear market) its 10-month simple moving average. We consider Top 1, equal-weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners for the baseline SACEMS lookback interval. We focus on monthly return, monthly volatility and compound annual growth rate (CAGR) as key performance metrics. In a robustness test for the EW Top 2 and EW Top 3 portfolios, we consider lookback intervals ranging from one to 12 months. Using monthly total (dividend-adjusted) returns for SACEMS assets since February 2006 and monthly S&P 500 Index level since September 2005, all through January 2022, we find that:

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