Strategic Allocation

A subscriber suggested that the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS) may each exhibit return momentum at the strategy level, such that an investor holding both as in Combined Value-Momentum Strategy (SACEVS-SACEMS) may want to tilt each month toward the one with stronger recent returns. To investigate, we test a SACEVS Best Value-SACEMS Equal-Weighted (EW) Top 2 combination strategy that each month assigns 60% weight to the strategy with the higher return over a specified lookback interval and 40% to the one with the lower return (60-40). We consider lookback intervals of 1 to 12 months. We also look at a “full tilt” version for a selected lookback interval. We use standalone SACEVS Best Value, standalone SACEMS EW Top 2 and monthly rebalanced 50% SACEVS Best Value-50% SACEMS EW Top 2 (50-50) as benchmarks. We look at average gross monthly return, standard deviation of monthly returns, monthly gross reward/risk (average monthly return divided by standard deviation), 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 SACEVS Best Value and SACEMS EW Top 2 gross monthly returns during July 2006 (limited by SACEMS) through February 2023, we find that:

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Can machine learning perfect equity sector rotation? In the January 2023 version of their paper entitled “Deep Sector Rotation Swing Trading”, flagged by a subscriber, Joel Bock and Akhilesh Maewal present a sector rotation strategy guided by multiple-input, multiple output deep learning model. The strategy chooses weekly from among 11 U.S. sectors using exchange-traded fund (ETF) proxies. Specifically, each week during each year, they:

• Train the machine learning model on the last two years of weekly (Friday close) historical sector ETF prices and volumes and sometimes auxiliary economic data (10-year U.S. Treasury yield, USD currency index, crude oil proxy and stock market volatility) to predict next-week opening and closing prices for each ETF.
• Compare the predicted return estimate for each ETF to a dynamically updated threshold return to screen for potential buys.
• Apply additional filters to screen out potential buys with unusual past losses to accommodate investor loss aversion.
• At the next-week open, allocate available capital to surviving sector ETFs based on respective past win rate (profitable trade) and respective past sector trade momentum.
• Liquidate all positions just prior to the next-week close.

Their benchmark is buying and holding the S&P 500 Index with reinvested dividends. Using weekly inputs as described during January 2012 through December 2022, they find that:

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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 February 2023, we find that: Keep Reading

“Simplified Offensive, Defensive and Risk Mode Identification Momentum Strategy” describes a strategy that each month holds offensive (defensive) assets when average return on iShares TIPS Bond ETF (TIP) over the past 1, 3, 6 and 12 months is positive (negative). Is past return of TIP, which purely impounds investor expectations for U.S. inflation, a reliable indicator of future asset class returns? To investigate, we relate TIP returns to future returns for each of the following exchange-traded fund (ETF) asset class proxies:

• Equities:
  • SPDR S&P 500 (SPY)
  • iShares Russell 2000 Index (IWM)
  • iShares MSCI EAFE Index (EFA)
  • iShares MSCI Emerging Markets Index (EEM)
• Bonds:
  • iShares Barclays 20+ Year Treasury Bond (TLT)
  • iShares iBoxx $ Investment Grade Corporate Bond (LQD)
  • iShares JPMorgan Emerging Markets Bond Fund (EMB)
• Real assets:
  • Vanguard REIT ETF (VNQ)
  • SPDR Gold Shares (GLD)
  • Invesco DB Commodity Index Tracking (DBC)

We consider both linear correlation and non-linear ranking tests. We look at TIP returns over the past 1, 3, 6 and 12 months separately, and as an average of these past returns (average past TIP return). Using monthly dividend-adjusted returns for TIP and the above asset class proxies as available during December 2003 (limited by TIP) through February 2023, we find that: Keep Reading

Is portfolio downside risk better manageable by combining drawdown and recovery into a single “submergence” metric? In their February 2023 paper entitled “Submergence = Drawdown Plus Recovery”, Dane Rook, Dan Golosovker and Ashby Monk present submergence density as a new risk metric to help investors analyze asset/portfolio drawdown and recovery jointly. They define submergence (s) of an asset/portfolio as percentage of current value below its past highwater mark. They define submergence density (d) during a given sample period as the κ-root of the sum of measurement interval submergences each raised to the κ-power during the sample period, as follows:

When κ=1,  submergence density is arithmetic average drawdown. When κ=∞ (or a very high number), submergence density is maximum drawdown. They use κ = 5 in examples to represent typical investor drawdown sensitivity. They define submergence risk-adjusted return as excess return (relative to the risk-free rate) minus a fraction (Θ) of submergence density, using the range 0.2 to 0.5 as reasonable for Θ. They apply submergence to several market indexes and discuss portfolio diversification and rebalancing in the context of reducing submergence overlaps. Using monthly excess returns for U.S. stocks, corporate bonds and Treasuries (S&P 500 Index, ICE BoA Corporate Bond Index and Bloomberg Treasuries Index, respectively) during 1979 through 2022, they find that:

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Can investors achieve attractive asset class momentum strategy performance by applying mixed-lookback interval momentum to different risk-on (offensive) and risk-off (defensive) sets of exchange-traded funds (ETF), and to a separate risk mode identification ETF? In their February 2023 paper entitled “Dual and Canary Momentum with Rising Yields/Inflation: Hybrid Asset Allocation (HAA)”, Wouter Keller and Jan Willem Keuning present a simplification of the prior Bold Asset Allocation strategy. This Hybrid Asset Allocation strategy consists of the following baseline asset universes and rules, with a single asset momentum metric (equal-weighted average return over the past 1, 3, 6 and 12 months):

• When TIP momentum is positive (negative), use the offensive (defensive) mode.
• When in offensive mode, hold the equal-weighted four of SPY, IWM, VWO, VEA, VNQ, DBC, IEF and TLT with the strongest momentum, except replace any of the top four with non-positive momentum by the one of BIL and IEF with the strongest momentum for crash protection.
• When in defensive mode, hold the one of BIL and IEF with the strongest momentum.

They reform the 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 December 2022, they 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 only 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 2023, we find that:

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A subscriber requested review of the Golden Butterfly (GB) portfolio, which assigns equal weights to the total stock market, small-capitalization value stocks, long-term government bonds, short-term government bonds and gold. To investigate, we use the following exchange-traded funds (ETF) as asset class proxies, respectively:

• Vanguard Total Stock Market Index Fund (VTI)
• iShares S&P Small-Cap 600 Value Fund (IJS)
• iShares Barclays 20+ Year Treasury Bond (TLT)
• iShares 1-3 Year Treasury Bond (SHY)
• SPDR Gold Shares (GLD)

We consider either monthly or annual rebalancings to equal weight, ignoring associated trading frictions. Using monthly dividend-adjusted prices for the five ETFs during November 2004 (limited by GLD) through December 2022, we find that: Keep Reading

Referring to “Asset Class Momentum Faster During Bear Markets?”, a subscriber asked about performance of a modification of the equal-weighted top three (EW Top 3) version of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) which uses the baseline momentum ranking (lookback) interval when the S&P 500 Index is above its 10-month simple moving average (SMA10) and a shorter lookback interval when the index is below its SMA10. To investigate, we look at average monthly return, standard deviation of monthly returns, monthly reward/risk (average divided by standard deviation), compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance statistics. Using monthly SACEMS returns for different lookback intervals since July 2006 and monthly levels of the S&P 500 Index since September 2005, all through December 2022, we find that: Keep Reading

A subscriber requested a test of Ray Dalio‘s All Weather (AW) portfolio with different rebalancing frequencies, allocated to exchange-traded funds (ETF) as asset class proxies as follows:

30% – Vanguard Total Stock Market (VTI)
40% – iShares 20+ Year Treasury (TLT)
15% – iShares 7-10 Year Treasury (IEF)
7.5% – SPDR Gold Shares (GLD)
7.5% – Invesco DB Commodity Tracking (DBC)

To investigate, we test:

• AW portfolios rebalanced monthly (AW-1), quarterly (AW-3), semiannually at the ends of June and December (AW-6) or annually at the end of December (AW-12).
• As a benchmark, a 60%-40% VTI-TLT portfolio rebalanced annually (60/40-12).
• For comparison, the Simple Asset Class ETF Momentum Strategy equally weighted Top 2 portfolio (SACEMS EW Top 2), the Simple Asset Class ETF Value Strategy (SACEVS) Best Value portfolio and a 50%-50% combination of these two portfolios (SACEVS-SACEMS 50-50).

We consider the following gross performance metrics, all based on monthly measurements: average monthly return, standard deviation of monthly returns, compound annual growth rate (CAGR), maximum drawdown (MaxDD) and Sharpe ratio (with the 3-month Treasury bill yield as the risk-free rate). We also compare number of rebalance actions for each portfolio. Using monthly dividend-adjusted returns for the specified assets during February 2006 (limited by DBC) through December 2022, we find that: Keep Reading