Strategic Allocation

Does adding a position take-profit (stop-gain) rule improve the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) by harvesting some upside volatility? SACEMS each months picks winners from among the a set of eight asset class exchange-traded fund (ETF) proxies plus cash based on past returns over a specified interval. To investigate the value of stop-gains, we augment SACEMS with a simple rule that: (1) exits to Cash from any current winner ETF when its intra-month return rises above a specified threshold; and, (2) re-sets positions per winners at the end of the month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using monthly total (dividend-adjusted) returns and intra-month maximum returns for the specified assets during February 2006 through January 2026, we find that: Keep Reading

Does adding a position stop-loss rule improve the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) by avoiding some downside volatility? SACEMS each months picks winners from among the a set of eight asset class exchange-traded fund (ETF) proxies plus cash based on past returns over a specified interval. To investigate the value of stop-losses, we augment SACEMS with a simple rule that: (1) exits to Cash from any current winner ETF when its intra-month return falls below a specified threshold; and, (2) re-sets positions per winners at the end of the month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using monthly total (dividend-adjusted) returns and intra-month drawdowns for the specified assets during February 2006 through January 2026, we find that: Keep Reading

There is a new “Strategy Tools” visualization feature under the “Strategies” drop-down menu in the navigation bar at the top of each page. These tools let users explore historical performances of the Simple Asset Class ETF Momentum Strategy (SACEMS) and the Simple Asset Class ETF Value Strategy (SACEVS), as well as several simple benchmark strategies. There are two tools:

1. Growth Simulator (publicly available) This tool charts on a logarithmic scale the hypothetical growth of $10,000 invested in one or more strategies over a user-selected time period. Users can:

• • Toggle strategies on and off to compare: (a) SACEMS Top 1 through equal-weighted (EW) Top 5; (b) SACEVS Best Value and Weighted; and, various benchmarks covered in SACEMS/SACEVS descriptions, including the S&P 500 Index as proxied by SPY, SPY:SMA10, EW All and 60/40 SPY/TLT.
  • Adjust date range via dropdowns or preset buttons (Max, 10Y, 5Y, 3Y).
  • View beneath the chart gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and terminal value.

2. Portfolio Blender (subscribers only) This tool allows subscribers to backtest custom allocations to one SACEMS variant and one SACEVS variant. Users can:

• • Select specific variants.
  • Set an allocation split (e.g., 60% SACEMS/ 40% SACEVS) using a slider or preset buttons.
  • View blended portfolio CAGR, MaxDD, Sharpe ratio (with zero risk-free rate for simplicity) and terminal value of a $10,000 initial investment.
  • Compare the blended portfolio plotted as a solid line with its two underlying strategy variants shown as dashed lines.

Input data begin in August 2002 for SACEVS and its benchmarks and June 2006 for SACEMS and its benchmarks.

We used Claude in developing these tools. We specified the design, logic and requirements, and Claude generated the underlying code.

We have updated monthly allocations and performance data for the Simple Asset Class ETF Momentum Strategy (SACEMS) and the Simple Asset Class ETF Value Strategy (SACEVS). We have also updated performance data for the Combined Value-Momentum Strategy.

We have updated the Trading Calendar to incorporate data for January 2026.

The home page, Simple Asset Class ETF Momentum Strategy (SACEMS) and Simple Asset Class ETF Value Strategy (SACEVS) now show preliminary positions for February 2026. SACEMS rankings are not very close, but markets are volatile. SACEVS allocations are unlikely to change by the close.

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 December 2006, before the sample period used for most tests of the Simple Asset Class ETF Momentum Strategy (SACEMS).
• Applying buffer factors to the bottom (0.9) and top (1.1) 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 equal-weighted top two (EW Top 2) and the equal-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 (baseline) 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 December 2025, we find that: Keep Reading

A subscriber asked about an update of “Simplest Asset Class ETF Momentum Strategy?”, which each month holds SPDR S&P 500 ETF Trust (SPY) or iShares 20+ Year Treasury Bond (TLT) depending on which has the higher total return over the last three months, including a direct comparison to a portfolio that each month allocates 50% to Simple Asset Class ETF Value Strategy (SACEVS) Best Value and 50% to Simple Asset Class ETF Momentum Strategy (SACEMS) equal-weighted (EW) Top 2. We begin the test at the end of June 2006, limited by SACEMS inputs. We ignore monthly switching frictions for both strategies. Using monthly dividend-adjusted prices for SPY and TLT starting March 2006 and monthly gross returns for 50-50 SACEVS Best Value and SACEMS EW Top 2 starting July 2006, all through December 2025, we find that: Keep Reading

How sensitive are multi-class asset allocation strategies to variations in backtesting choices? In their December 2025 paper entitled “The Multiverse Across Asset Classes: Design Uncertainty in Asset Allocations”, Arnaud Battistella, Jean-Charles Bertrand, Guillaume Coqueret and Nicholas McLoughlin explore net annualized Sharpe ratio sensitivities of asset class allocation methods with respect to five backtest design choices:

• Alternative investor utility functions (three levels of risk aversion).
• Choice of signal-generating type (eight ranging across economic indicators, trend-following/momentum, risk, valuation, equal return, machine learning).
• Different sample periods (rolling 10-year intervals, each time shifting the start by one year for a total of 16 intervals during May 1999 through May 2025).
• Different rebalancing frequencies (monthly, quarterly, annually).
• Leeway with respect to benchmark tracking (21 error constraints relative to an equal-weighted benchmark).

Their investment universe consists of the S&P 500 Index, commodities, gold, a 70%/30% U.S. Treasuries/U.S. investment grade bonds and U.S. high-yield bonds. They express sensitivities by providing ranges of Sharpe ratios generated by ranges of design choice values (23,040 total outcomes). Using monthly data for the five asset classes and for 19 economic indicators, a 2.5% risk-free rate and 0.1% 2-way frictions for portfolio turnover, they find that:

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“Dominant U.S. Stock Market?” summarizes a paper that (1) examines whether a 60%+ allocation to the U.S. in the MSCI All Country World Index (ACWI) is crazy high and (2) decides maybe not. In “How Much Is Too Much? Part 2: Why 60% in US Equities Might Be Just as Crazy as It Sounds”, the same authors reconsider by testing key assumptions in Part 1. Specifically, they quantify impacts of uncertainty in mean-variance optimization (MVO) via sensitivity analysis/simulations and look at alternative ways to construct long-term equity portfolios. Using monthly returns for U.S., Europe ex UK, UK, Japan, Pacific ex Japan and Emerging Markets in U.S. dollars during December 1987 through May 2025, they find that:

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As of November 2025, the MSCI All Country World Index (ACWI) allocates about 65% to U.S. stocks. Is this allocation to a single country crazy high? In the November 2025 revision of their brief paper entitled “How Much Is Too Much? Part 1: Why 60% in US Equities Isn’t as Crazy as It Might Sound”, Arnaud Battistella and Nicholas McLoughlin assess this allocation by deriving the expected returns implied by ACWI allocations across regions (U.S., Europe ex UK, UK, Japan, Pacific ex Japan and Emerging Markets). Using mid-2025 ACWI allocations and monthly returns for specified regions in U.S. dollars during December 1987 through May 2025, they find that:

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