Strategic Allocation

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

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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What is the overall best way to weight stock portfolios? In their February 2022 paper entitled “Weighting for the Right One: Weighting Scheme Design for Systematic Equity Portfolios”, Wei Dai, Namiko Saito and Gigi Wang compare eight stock portfolio weighting schemes frequently used in systematic strategies, five that ignore prices and three that do not, as follows:

• Weighting schemes that ignore prices are:
  1. Equal weighting – assign all stocks the same dollar weight.
  2. Rank weighting – separately rank all stocks from large to small, growth to value and low to high profitability, and then re-rank and weight based on averages of individual ranks.
  3. Z-score weighting: separately calculate z-scores (number of standard deviations from average) for each firm’s market capitalization, relative price and profitability, transform the z-scores into a value between 0 and 1, and weight in proportion to the product of the three standardized z-scores.
  4. Inverse volatility weighting: weight each stock in proportion to the inverse of its daily return volatility over the last 60 trading days.
  5. Fundamental weighting: weight each stock in proportion to the sum of book equity, sales and cash flow per share during its latest fiscal year.
• Weighting schemes that incorporate prices are:
  1. Rank x mcap: weight each stock in proportion to the product of its rank weighting (as defined above) and its market capitalization.
  2. Z-score x mcap: weight each stock in proportion to the product of its standardized z-scores (as defined above) and its market capitalization.
  3. Integrated core: separately sort all firms by market capitalization, relative price and profitability into groups with similar characteristics; within each group, weight firms in proportion to their market capitalizations; and, further weight each group in proportion to its aggregate market capitalization times a multiplier capturing its overall size, value and profitability premiums as modified for interactions among them.

They rebalance each portfolio semiannually. They consider stock universes with and without microcaps (bottom 4% of market capitalizations). Their approach focuses on the importance of accounting for current market prices that reflect the latest news and market expectations. Using data as described for all U.S. common stocks (excluding REITs, tracking stocks and investment companies) during July 1974 through December 2019, they find that:

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Using quarterly data in their April 2013 paper entitled “Analyzing Federal Reserve Asset Purchases: From Whom Does the Fed Buy?” Seth Carpenter, Selva Demiralp, Jane Ihrig and Elizabeth Klee find that some categories of investors appear to sell U.S. Treasuries to the Federal Reserve and rebalance toward riskier assets (corporate bonds, commercial paper, and municipal debt). Are stocks, proxied by for SPDR S&P 500 (SPY), a part of this process? To investigate, we relate weekly, monthly and quarterly U.S. stock market returns to changes in the Federal Reserve’s System Open Market Account (SOMA) holdings, comprised of U.S. Treasury bills, U.S. Treasury notes and bonds, U.S. Treasury Inflation-Protected Securities (TIP) and Mortgage-Backed Securities (MBS). The Federal Reserve reports these holdings as of Wednesday, typically with a 1-day lag. Using weekly (Thursday close) dividend-adjusted prices for SPY and weekly total SOMA holdings during early July 2003 through January 2022, we find that:

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A subscriber suggested expanding and modifying the asset universe for the Simple Asset Class ETF Momentum Strategy (SACEMS) to consist of the following exchange-traded funds (ETF):

• SPDR Portfolio S&P 500 Growth (SPYG)
• SPDR Portfolio S&P 500 Value (SPYV)
• iShares Russell 2000 Growth (IWO)
• iShares Russell 2000 Value (IWN)
• Invesco QQQ Trust (QQQ)
• iShares MSCI EAFE Index (EFA)
• iShares MSCI Emerging Markets Index (EEM)
• iShares Barclays 20+ Year Treasury Bond (TLT)
• iShares Core U.S. Aggregate Bond (AGG)
• iShares U.S. Real Estate ETF (IYR)
• SPDR Gold Shares (GLD)
• Invesco DB Commodity Index Tracking (DBC)
• 3-month Treasury bills (Cash)

To investigate attractiveness of this alternative, we first look at compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) for the expanded universe across SACEMS momentum measurement (lookback) intervals ranging from 1 to 12 months to identify effective lookback intervals. We then compare annual performance statistics of the Top 1, equal-weighted (EW) Top 2, EW Top 3 and EW Top 4 portfolios for the expanded and baseline asset universes with the SACEMS baseline lookback interval. Using monthly dividend-adjusted returns for the expanded asset universe during February 2006 (limited by DBC) through December 2021 and monthly returns for baseline SACEMS over the same period, we find that: Keep Reading