Strategic Allocation

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

A subscriber observed that backtesting of momentum-based trading systems typically assumes perfect rebalancing each month whether or not they select new assets. Would delaying rebalancing until new assets are selected improve strategy performance? To investigate, we compare the following two versions of the Simple Asset Class ETF Momentum Strategy (SACEMS) equal-weighted (EW) Top 2 portfolio:

1. Rinse-and-Repeat – each month rebalance the two positions to equal weights. This is the assumption for tracked SACEMS.
2. Let-It-Ride – rebalance the two positions to equal weights only when the strategy selects two new assets. In other words, as long as at least one of the two selections is a holdover from the prior month, let the two positions drift away from equal weights.

Using monthly returns for the top two SACEMS selections during July 2006 through February 2024, we find that: Keep Reading

Referring to “Substitute QQQ for SPY in SACEVS and SACEMS?” and “Conditionally Substitute SSO for SPY in SACEVS and SACEMS?”, a subscriber requested a horse race for boosting the performance of the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS), and thereby the Combined Value-Momentum Strategy (SACEVS-SACEMS), based on substituting:

1. ProShares Ultra S&P500 (SSO) for SPDR S&P 500 ETF Trust (SPY) in portfolio holdings, but not in SACEMS asset ranking calculations.
2. Invesco QQQ Trust (QQQ) for SPY in both portfolio holdings and SACEMS asset ranking calculations.

In conducting the horse race, we focus on gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and gross annual Sharpe ratio as key performance metrics. In Sharpe ratio calculations, we employ the average monthly yield on 3-month U.S. Treasury bills during a year as the risk-free rate for that year. Using monthly total (dividend-adjusted) returns for SACEVS assets, SACEMS assets, SSO and QQQ as available through February 2024, we find that: Keep Reading

Subscribers asked whether substituting Invesco QQQ Trust (QQQ) for SPDR S&P 500 (SPY) in the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS) improves outcomes. To investigate, we substitute monthly QQQ dividend-adjusted returns for SPY dividend-adjusted returns in the two model strategies. We then compare the modified performance with the original baseline performance, including: gross compound annual growth rates (CAGR) at various horizons, average gross annual returns, standard deviations of gross annual returns, gross annual Sharpe ratios and maximum drawdowns (MaxDD) based on monthly measurements. In Sharpe ratio calculations, we employ the average monthly yield on 3-month U.S. Treasury bills during a year as the risk-free rate for that year. Using the specified methodology and data to generate SACEVS monthly returns starting August 2002 and SACEMS monthly returns starting July 2006, all through January 2024, we find that:

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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 January 2024, we find that: Keep Reading