Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for December 2021 (Final)

Momentum Investing Strategy (Strategy Overview)

Allocations for December 2021 (Final)
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Strategic Allocation

Is there a best way to select and weight asset classes for long-term diversification benefits? These blog entries address this strategic allocation question.

SACEMS with Margin

Is leveraging with margin a good way to boost the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS)? To investigate effects of margin, we augment SACEMS by: (1) initially applying 2X leverage via margin (limited by Federal Reserve Regulation T); (2) for each month with a positive portfolio return, adding margin at the end of the month to restore 2X leverage; and, (3) for each month with a negative portfolio return, liquidating shares at the end of the month to pay down margin and restore 2X leverage. Margin rebalancings are concurrent with portfolio reformations. We focus on gross monthly Sharpe ratiocompound annual growth rate (CAGR) and maximum drawdown (MaxDD) for committed capital as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. We use the 3-month Treasury bill (T-bill) yield as the risk-free rate and consider a range of margin interest rates as increments to this yield. Using monthly gross total returns for SACEMS and monthly T-bill yields during July 2006 through February 2021, we find that:

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Ascendance of Automated ETF Allocation Models

Investors seeking low-cost, automated, tax-efficient and potentially alpha-generating solutions increasingly follow model portfolios of exchange-traded funds (ETF). Is there a top-down way to characterize those models? In their November 2020 paper entitled “Using Data Science to Identify ETF Model Followers”, Ananth Madhavan and Aleksander Sobczyk apply machine learning methods and cluster analysis to identify all models using at least three iShares ETFs based on monthly holdings data. Using monthly data on positions and accounts holding those positions across all iShares ETFs (370 at the end of the sample period) during January 2013 through June 2020, they find that:

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Diversifying across Growth/Inflation States of the Economy

Can diversification across economic states improve portfolio performance? In their November 2020 paper entitled “Investing Through a Macro Factor Lens”, Harald Lohre, Robert Hixon, Jay Raol, Alexander Swade, Hua Tao and Scott Wolle study interactions between three economic “factors” (growth, defensive/U.S. Treasuries and inflation) and portfolio building blocks (asset classes and conventional factor portfolios). Their proxies for economic factors are: broad equity market for growth; U.S. Treasuries for defensive; and, spread between inflation-linked bonds and U.S. Treasuries for inflation. To diversify across economic states, they calculate historical performance of each portfolio building block during each of four economic regimes: (1) rising growth and rising inflation; (2) rising growth and falling inflation; (3) falling growth and rising inflation; and, (4) falling growth and falling inflation. They then look at benefits of adding defensive and inflation economic factor overlays to a classis 60%/40% global equities/bonds portfolio. Using monthly economic factor data and asset class/conventional factor portfolio returns during February 2001 through May 2020, they find that: Keep Reading

SACEVS and SACEMS Performance by Calendar Month

A subscriber asked whether the Simple Asset Class ETF Momentum Strategy (SACEMS) exhibits monthly calendar effects. In investigating, we also look at the Simple Asset Class ETF Value Strategy (SACEVS)? We consider the “Best Value” (most undervalued asset) and “Weighted” (assets weighted by degree of undervaluation) versions of SACEVS. We consider the Top 1, equally weighted “(EW) Top 2” and “EW Top 3” versions of SACEMS, which each month equally weights the top one, two or three of nine ETFs/cash with the highest total returns over a specified lookback interval. We further compare seasonalities of these strategies to those of their benchmarks: for SACEVS, a monthly rebalanced 60% stocks-40% bonds portfolio (60-40); and, for SACEMS an equally weighted and monthly rebalanced portfolio of the SACEMS universe (EW All). Using monthly gross total returns for SACEVS since August 2002 and for SACEMS since July 2006, both through January 2021, we find that:

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Testing the 3-ETF Strategy

A subscriber asked for a 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 the following monthly rebalanced allocations to three exchange-traded funds (3-ETF):

Using monthly returns for SACEVS-SACEMS 50-50 and month-end dividend-adjusted prices for VTI, VXUS and BND during January 2011 (limited by inception of VXUS) through January 2021, we find that: Keep Reading

Update on Classic Portfolio Allocations with Leveraged ETFs

Can investors use leveraged exchange-traded funds (ETF) as building blocks for long-term portfolios? In his January 2021 presentation package entitled “One Year Later. Leveraged ETFs in Portfolio Construction and Portfolio Protection”, Mikhail Smirnov updates multi-year performance of a monthly rebalanced partially 3X-leveraged portfolio consisting of:

  • 40% ProShares UltraPro QQQ (TQQQ)
  • 20% Direxion Daily 20+ Year Treasury Bull 3X Shares (TMF)
  • 40% iShares 20+ Year Treasury Bond ETF (TLT)

The last three years are out-of-sample with respect to specification of this portfolio. He also looks at a more conservative portfolio of 20% TQQQ and 80% TLT, rebalanced monthly. Using pre-inception simulated and actual monthly total returns for these ETFs during January 1, 2005 through January 15, 2021, he finds that: Keep Reading

SACEVS and SACEMS from a European Perspective

A European subscriber asked about the effect of the dollar-euro exchange rate on the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS). To investigate, we each month adjust the gross returns for these strategies for the change in the dollar-euro exchange rate that month. We consider all strategy variations: Best Value and Weighted for SACEVS; and, Top 1, equally weighted (EW) Top 2 and EW Top 3 for SACEVS. We focus on SACEVS Best Value and SACEMS EW Top 3. We consider effects on four gross performance metrics: average monthly return; standard deviation of monthly returns; compound annual growth rate (CAGR); and, maximum drawdown (MaxDD). Using monthly returns for the strategies and monthly changes in the dollar-euro exchange rate since August 2002 for SACEVS and since July 2006 for SACEMS, both through December 2020, we find that: Keep Reading

SACEVS with SMA Filter

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 December 2020, we find that:

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Combining an All Weather Portfolio, Crash Protection and Unemployment Trend

Is it possible to achieve an attractive return with very low risk from a small universe of exchange-traded funds (ETF)? In the January 2021 revision of his paper entitled “Lazy Momentum with Growth-Trend Timing: Resilient Asset Allocation (RAA)”, Wouter Keller constructs and tests the Resilient Asset Allocation (RAA) strategy as a more aggressive version of the Lethargic Asset Allocation (LAA) strategy. LAA switches between risky (equal-weighted  QQQ, IWD, IEF and GLD) and defensive (equal-weighted IWD, GLD, SHY and IEF) portfolios based on trend in the U.S. unemployment rate (bullish when above and bearish when below its 12-month simple moving average). RAA alters LAA in three ways:

  1. Changes the risky portfolio to an All Weather-like portfolio, with five equal-weighted assets (QQQ, IWN, IEF, TLT, GLD) instead of four and only two equal-weighted assets (IEF and TLT) as a risk-off portfolio.
  2. Adds “canary” crash protection from the Defensive Asset Allocation strategy for signaling the market trend with a fast filter (bearish when either or both of VWO or BND turn bad).
  3. Slows the unemployment rate trend signal, simply comparing recent unemployment rate with that of a year ago.

RAA is in the defensive portfolio only when both the canary and unemployment rate trend signals are bearish. Using a combination of modeled and (as available) actual monthly price data for the specified ETFs during December 1970 through November 2020, he finds that: Keep Reading

Testing the All Weather Portfolio

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:

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 2020, we find that: Keep Reading

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