Strategic Allocation

What happens if we extend the “Simple Asset Class ETF Value Strategy” (SACEVS) with a utilities risk premium, derived from the yield on Utilities Select Sector SPDR Fund (XLU)? To investigate, we apply the SACEVS methodology to the following asset class exchange-traded funds (ETF), plus cash:

3-month Treasury bills (Cash)
iShares 20+ Year Treasury Bond ETF (TLT)
iShares iBoxx $ Investment Grade Corporate Bond ETF (LQD)
XLU
SPDR S&P 500 ETF Trust (SPY)

This set of ETFs relates to four risk premiums, as specified below: (1) term; (2) credit (default); (3) utilities; and, (4) equity. We focus on effects of adding the utilities risk premium on gross compound annual growth rates (CAGR), maximum drawdowns (MaxDD) and annual Sharpe ratios of the Best Value (picking the most undervalued premium) and Weighted (weighting all undervalued premiums according to degree of undervaluation) versions of SACEVS. Using lagged quarterly S&P 500 earnings, monthly S&P 500 Index levels and monthly yields for 3-month U.S. Treasury bill (T-bill), the 10-year Constant Maturity U.S. Treasury note (T-note), Moody’s Seasoned Baa Corporate Bonds since March 1989 (limited by availability of earnings data), XLU prices and dividends since December 1998 (inception) and monthly dividend-adjusted closing prices for the above asset class ETFs since July 2002, all through May 2024, we find that: Keep Reading

“Countercyclical Asset Allocation Strategy” summarizes research on a simple countercyclical asset allocation strategy that systematically raises (lowers) the allocation to an asset class when its current aggregate allocation is relatively low (high). The underlying research is not specific on calculating portfolio allocations and returns. To corroborate findings, we use annual mutual fund and exchange-traded fund (ETF) allocations to stocks and bonds worldwide from the 2024 Investment Company Fact Book data tables to determine annual countercyclical allocations for stocks and bonds (ignoring allocations to money market funds). Specifically:

• If actual aggregate mutual fund/ETF allocation to stocks in a given year is above (below) 60%, we set next-year portfolio allocation below (above) 60% by the same percentage.
• If actual aggregate mutual fund/ETF allocation to bonds in a given year is above (below) 40%, we set next-year portfolio allocation below (above) 40% by the same percentage.

We then apply next-year allocations to stock (Fidelity Fund, FFIDX) and bond (Fidelity Investment Grade Bond Fund, FBNDX) mutual funds that have long histories. Based on Fact Book annual publication dates, we rebalance at the end of April each year. Using the specified actual fund allocations for 1984 through 2023 and FFIDX and FBNDX May through April total returns and end-of-April 1-year U.S. Treasury note (T-note) yields for 1985 through 2024, we find that: Keep Reading

Is industry trend-following an attractive strategy over the long run? In their June 2024 paper entitled “A Century of Profitable Industry Trends”, Carlo Zarattini and Gary Antonacci evaluate the long-term performance of a long-only industry trend-following (Timing Industry) strategy, modeled as follows:

• Entry – buy an industry when its daily closing price crosses above the upper band of either its 20-day Keltner Channel (with a multiplier of 2 for the high-low price range component) or its 20-day Donchian Channel.
• Sizing – each day for each open position, calculate 14-day past return volatility as an estimate of its future volatility and resize all open positions so that they contribute equally to overall portfolio volatility, limiting overall portfolio leverage to 200%.
• Exit – each day for each open position, close the position if it crosses below a stop loss represented by the lower band of either its 40-day Keltner Channel (again with a multiplier of 2 for the high-low price range component) or its 40-day Donchian Channel. However, do not ever lower the stop loss. When a position closes, reinvest proceeds into 1-month U.S. Treasury bills.

For a long-term test, they apply these rules to nearly 98 years of daily returns for 48 hypothetical annually rebalanced, capitalization-weighted industry portfolios constructed by assigning a Standard Industrial Classification (SIC) Code to each stock traded on NYSE, AMEX and NASDAQ. For a recent and more realistic test, they apply these rules to 31 sector exchange-traded funds (ETF) offered by State Street Global Advisors. Utilizing daily returns for the 48 industry portfolios since July 1926 and for the 31 sector ETFs as available (inceptions January 2005 to June 2018), all through March 2024, they find that:

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A subscriber asked about the stability of the momentum measurement (lookback) interval used for strategies like the Simple Asset Class ETF Momentum Strategy (SACEMS). To investigate, we run two tests on each of top one (Top 1),  equal-weighted top two (EW Top 2) and equal-weighted top three (EW Top 3) versions of SACEMS:

1. Identify the SACEMS lookback interval with the highest gross compound annual growth rate (CAGR) for a sample starting February 2006 when Invesco DB Commodity Index Tracking Fund (DBC) becomes available and ending each of May 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023 and 2024. We consider lookback intervals of one to 12 months, meaning that earliest allocations are for February 2007 to accommodate the longest interval. The shortest sample period is therefore 5.3 years. This test takes the perspective of an investor who devises SACEMS in May 2012 and each year adds 12 months of data and checks whether the optimal lookback interval has changed.
2. Identify the SACEMS lookback interval with the highest gross CAGR for a sample ending May 2021 and starting each of February 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018 and 2019. The shortest sample period is again 5.3 years. This test takes perspectives of different investors who devise SACEMS at the end of February in different years.

Using monthly SACEMS inputs and the SACEMS model as currently specified for February 2006 through May 2024, we find that: Keep Reading

What happens if we extend the “Simple Asset Class ETF Value Strategy” (SACEVS) with a real estate risk premium, derived from the yield on equity Real Estate Investment Trusts (REIT), represented by the FTSE NAREIT Equity REITs Index? To investigate, we apply the SACEVS methodology to the following asset class exchange-traded funds (ETF), plus cash:

3-month Treasury bills (Cash)
iShares 20+ Year Treasury Bond (TLT)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR Dow Jones REIT (RWR) through September 2004 dovetailed with Vanguard REIT ETF (VNQ) thereafter
SPDR S&P 500 (SPY)

This set of ETFs relates to four risk premiums, as specified below: (1) term; (2) credit (default); (3) real estate; and, (4) equity. We focus on effects of adding the real estate risk premium on gross compound annual growth rates (CAGR), maximum drawdowns (MaxDD) and annual Sharpe ratios of the Best Value (picking the most undervalued premium) and Weighted (weighting all undervalued premiums according to degree of undervaluation) versions of SACEVS. Using lagged quarterly S&P 500 earnings, monthly S&P 500 Index levels and monthly yields for 3-month U.S. Treasury bill (T-bill), the 10-year Constant Maturity U.S. Treasury note (T-note), Moody’s Seasoned Baa Corporate Bonds and FTSE NAREIT Equity REITs Index since March 1989 (limited by availability of earnings data), and monthly dividend-adjusted closing prices for the above asset class ETFs since July 2002, all through May 2024, we find that: Keep Reading

In response to “Tech Equity Premium?”, a subscriber asked about substituting Invesco QQQ Trust (QQQ) for SPDR S&P 500 (SPY) in the “Simplest Asset Class ETF Momentum Strategy?”, which each month holds SPY or iShares Barclays 20+ Year Treasury Bond (TLT) depending on which has the higher total return over the last three months. To investigate, we run a horse race between the strategy executed with SPY (SPY-TLT) and the strategy executed with QQQ (QQQ-TLT). We focus on compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) as performance metrics and assess robustness across lookback intervals of one to 12 months. Using monthly dividend-adjusted prices for SPY, QQQ and TLT during July 2002 (limited by TLT) through May 2024, we find that: Keep Reading

After seeing “Review of the Quantified Market Psychology Strategy”, reader Steve Ruoff requested review of his U.S. Treasuries timing strategy as recorded at Timertrac (Duration Strategy), which approximately every four weeks generates allocations to: Direxion Daily 7-10 Year Treasury Bull 3X Shares (TYD); Direxion Daily 7-10 Year Treasury Bear 3X Shares (TYO); and cash, for which we use SPDR Bloomberg 1-3 Month T-Bill ETF (BIL). Strategy inputs encompass:

1. Current economic activity, inflation metrics and monetary policy factors.
2. Valuation estimates focused on long-term mean reversion thresholds.
3. Technical rules focused on price momentum.

To investigate, we download the Timertrac trade history for the Duration Strategy and replicate its performance using the selected exchange-traded funds (ETF). We look at average trading activity, average trade return, standard deviation of trade returns, trade reward/risk (average return divided by standard deviation), compound annual growth rate (CAGR) and maximum drawdown (MaxDD) at the trade frequency. We look at buying and holding iShares 7-10 Year Treasury Bond ETF (IEF) as an alternative and also look at buying and holding SPDR S&P 500 ETF Trust (SPY). Using the Duration Strategy trade history and daily adjusted opening prices for all specified ETFs during mid-January 2014 through early March 2024, we 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 April 2024 and roughly matched start and stop performance for monthly SACEVS-SACEMS 50-50 , we find that:

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Can investors beat a traditional 60%-40% stocks-bonds portfolio by adjusting allocations based on the earnings yield of stocks and the current yield of government bonds? In his March 2024 paper entitled “A Yield-based Asset Ratio to Boost Minimum Investment Returns”, Arthur Eschenlauer tests a strategy that allocates to the S&P 500 Index or 10-year U.S. Treasury notes (T-note) via a Yield-based Asset Ratio strategy (YBAR), specified as follows:

• Compute minimum and maximum stock allocations that vary with S&P 500 long-term past earnings yield and current nominal T-note yield. The earnings yield is average earnings over the past 10 years divided by stock index level.
• Buy the stock index incrementally to rise to the minimum allocation whenever the stock allocation falls below the minimum minus 6% (a margin of safety).
• Sell the stock index incrementally to fall to the maximum allocation whenever the stock allocation rises above the maximum allocation plus 6% (a margin of folly).
• Whenever the stock index is very high, apply a cap to the stock allocation (a margin of reversion). The margin of reversion reflects how the earnings yield stands relative to historical values.

YBAR testing assumes 6% minimum acceptable stock allocation, 85% maximum acceptable stock allocation, 25% maximum reversion hazard and monthly portfolio assessment. Using Robert Shiller’s data as proxies for S&P 500 Index levels and earnings and for T-note yields during 1911 through 2022, he finds that: Keep Reading

A subscriber asked for assessment of a strategy that holds 70% SPDR S&P 500 ETF Trust (SPY) and 30% SPDR Bloomberg 1-3 Month T-Bill ETF (BIL) (SPY-BIL 70-30), rebalanced every eight weeks, with explicit comparison to the 50% Simple Asset Class ETF Value Strategy Best Value-50% Simple Asset Class ETF Momentum Strategy Equal-Weighted Top 2 combined strategy (Best Value-EW Top 2). We measure performance of SPY-BIL 70-30 at 8-week intervals to match the specified rebalancing schedule. We measure performance of Best Value-EW Top 2 bimonthly for approximate comparability. We focus on 8-week or bimonthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). We also look at buy-and-hold SPY (SPY) as a simple alternative. Using weekly SPY and BIL dividend-adjusted prices and monthly Best Value -EW Top 2 returns from late May 2007 (BIL inception) through early February 2024, we find that: Keep Reading