Momentum Investing

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 September 2025.

The home page, Simple Asset Class ETF Momentum Strategy (SACEMS) and Simple Asset Class ETF Value Strategy (SACEVS) now show preliminary positions for October 2025. SACEMS rankings probably will not change by the close, but positions 1 and 2 are somewhat close. SACEVS allocations are unlikely to change by the close.

Are the “Simple Asset Class ETF Value Strategy” (SACEVS) and the “Simple Asset Class ETF Momentum Strategy” (SACEMS) mutually diversifying. To check, based on feedback from subscribers about combinations of interest, we look at three equal-weighted (50-50) combinations of the two strategies, rebalanced monthly:

1. 50-50 Best Value – EW Top 2: SACEVS Best Value paired with SACEMS Equally Weighted (EW) Top 2 (aggressive value and somewhat aggressive momentum).
2. 50-50 Best Value – EW Top 3: SACEVS Best Value paired with SACEMS EW Top 3 (aggressive value and diversified momentum).
3. 50-50 Weighted – EW Top 3: SACEVS Weighted paired with SACEMS EW Top 3 (diversified value and diversified momentum).

We consider as a benchmark a simple technical strategy (SPY:SMA10) that holds SPDR S&P 500 ETF Trust (SPY) when the S&P 500 Index is above its 10-month simple moving average and 3-month U.S. Treasury bills (Cash, or T-bills) when below. We also test sensitivity of results to deviating from equal SACEVS-SACEMS weights. Using monthly gross returns for SACEVS, SACEMS, SPY and T-bills during July 2006 through August 2025, we find that: Keep Reading

Does alignment of return-based factors with informed traders and against noise traders produce a superior model of stock returns? In his August 2025 paper entitled “An Auto-Residual Factor Model”, Malek Alkshaik introduces and tests a 5-factor Auto-Residual Factor Model of stock returns comprised of: market excess return; market capitalization (size); residual short-term reversal (last month); residual momentum (measured from 12 months ago to one month ago): and, residual long-term reversion (measured from 24 months ago to 13 months ago). This model uses no firm fundamental data. He postulates that the latter three factors occur due to interactions between noise traders and informed traders. He calculates (purifies) residuals via regressions against five principal components derived from the last 24 months of returns for all stocks, thereby aligning residuals with informed traders and against noise traders (purifying). He emphasizes maximum squared Sharpe ratio (based on mean-variance optimal factor allocations) to compare the new model to seven widely used alternatives. Using a main sample of U.S. listed common stocks during 1972 through 2022, plus a 1932 through 1971 U.S. sample and a 1992 through 2022 global sample for robustness tests, he finds that:

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Are equity multifactor strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider eight multifactor ETFs, all currently available:

• iShares Edge MSCI Multifactor USA (LRGF) – holds large and mid-cap U.S. stocks with focus on quality, value, size and momentum, while maintaining a level of risk similar to that of the market. The benchmark is iShares Russell 1000 (IWB).
• iShares Edge MSCI Multifactor International (INTF) – holds global developed market ex U.S. large and mid-cap stocks based on quality, value, size and momentum, while maintaining a level of risk similar to that of the market. The benchmark is iShares MSCI ACWI ex US (ACWX).
• Goldman Sachs ActiveBeta U.S. Large Cap Equity (GSLC) – holds large U.S. stocks based on good value, strong momentum, high quality and low volatility. The benchmark is SPDR S&P 500 (SPY).
• John Hancock Multifactor Large Cap (JHML) – holds large U.S. stocks based on smaller capitalization, lower relative price and higher profitability, which academic research links to higher expected returns. The benchmark is SPY.
• John Hancock Multifactor Mid Cap (JHMM) – holds mid-cap U.S. stocks based on smaller capitalization, lower relative price and higher profitability, which academic research links to higher expected returns. The benchmark is SPDR S&P MidCap 400 (MDY).
• JPMorgan Diversified Return U.S. Equity (JPUS) – holds U.S. stocks based on value, quality and momentum via a risk-weighting process that lowers exposure to historically volatile sectors and stocks. The benchmark is SPY.
• Xtrackers Russell 1000 Comprehensive Factor (DEUS) – seeks to track, before fees and expenses, the Russell 1000 Comprehensive Factor Index, which seeks exposure to quality, value, momentum, low volatility and size factors. The benchmark is IWB.
• Vanguard U.S. Multifactor (VFMF) – uses a rules-based quantitative model to evaluate U.S. common stocks and construct a U.S. equity portfolio that seeks to achieve exposure to multiple factors across market capitalizations (large, mid and small). The benchmark is iShares Russell 3000 (IWV).

We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the seven equity multifactor ETFs and benchmarks as available through August 2025, we find that: Keep Reading

Do alternative “Cash” (deemed risk-free) instruments materially affect performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS)? Changing the proxy for Cash can affect how often the model selects Cash, as well as the return on Cash when selected. To investigate, we test separately each of the following yield and exchange-traded funds (ETF) as the risk-free asset:

• 3-month Treasury bills (Cash), a proxy for the money market as in base SACEMS
• SPDR Bloomberg Barclays 1-3 Month T-Bill (BIL)
• iShares 1-3 Year Treasury Bond (SHY)
• iShares 7-10 Year Treasury Bond (IEF)
• Vanguard Short-Term Inflation-Protected Securities Index Fund (VTIP)
• iShares TIPS Bond (TIP)

We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance metrics and consider Top 1, equally weighted (EW) EW Top 2 and EW Top 3 SACEMS portfolios. Using end-of-month total (dividend-adjusted) returns for the specified assets during February 2006 (except May 2007 for BIL and October 2012 for VTIP) through July 2025, we find that:

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Does adding an exchange-traded fund (ETF) or note (ETN) to the Simple Asset Class ETF Momentum Strategy (SACEMS) boost performance via consideration of more trending/diversifying options? To investigate, we add the following 24 ETF/ETN asset class proxies one at a time to the base set and measure effects on the Top 1, equally weighted (EW) Top 2 and EW Top 3 SACEMS portfolios:

Alerian MLP ETF (AMLP)
VanEck Vectors BDC Income (BIZD)
Vanguard Total Bond Market (BND)
SPDR Barclays International Treasury Bond (BWX)
Invesco DB Agriculture Fund (DBA)
iShares MSCI Emerging Markets (EEM)
First Trust US IPO Index (FPX)
iShares iBoxx High-Yield Corporate Bond (HYG)
iShares 7-10 Year Treasury Bond (IEF)
iShares Latin America 40 (ILF)
iShares National Muni Bond ETF (MUB)
Invesco Closed-End Fund Income Composite (PCEF)
Invesco Global Listed Private Equity (PSP)
IQ Hedge Multi-Strategy Tracker (QAI)
Invesco QQQ Trust (QQQ)
SPDR Dow Jones International Real Estate (RWX)
ProShares UltraShort S&P 500 (SDS)
iShares Short Treasury Bond (SHV)
ProShares Short 20+ Year Treasury (TBF)
iShares TIPS Bond (TIP)
United States Oil (USO)
Invesco DB US Dollar Index Bullish Fund (UUP)
ProShares VIX Short-Term Futures (VIXY)
ProShares VIX Mid-Term Futures (VIXM)

We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics, ignoring monthly reformation costs. Using end-of-month, dividend-adjusted returns for all assets as available during February 2006 through July 2025, we find that: Keep Reading

Are all of the potentially trending/diversifying asset class proxies used in the Simple Asset Class ETF Momentum Strategy (SACEMS) necessary? Might one or more of them actually be harmful to performance? To investigate, we each month rank the nine SACEMS assets based on past return with one excluded (nine separate test series) and reform the Top 1, equally weighted (EW) Top 2 and EW Top 3 SACEMS portfolios. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics, ignoring monthly portfolio reformation costs. Using end-of-month, dividend-adjusted returns for SACEMS assets during February 2006 through July 2025, we find that: Keep Reading

Is U.S. equity factor return momentum broader and stronger for a short momentum measurement lookback interval than for a long one? In their July 2025 paper entitled “Revisiting Factor Momentum: A One-month Lag Perspective”, Mikael Rönkkö and Joonas Holmi compare U.S. equity factor momentum for 1-month and 12-month lookback intervals. They consider individual factor time series momentum and a momentum portfolio that is each month long or short factors based on the signs of respective factor returns the previous month or year. Their benchmark strategy takes long (short) positions in factors with positive (negative) historical average returns. Using monthly gross returns for 138 non-momentum, value-weighted U.S. equity factors with at least 10 years of data as available during July 1963 through December 2024, they find that: Keep Reading

Why do factor timing strategies that shine in research papers disappoint in real life? In his May 2025 paper entitled “Caveats of Simple Factor Timing Strategies”, David Blitz discusses the following  simple factor timing strategies with material and statistically significant outperformance per published studies:

• Short-term factor momentum – each month allocates 40%, 30%, 20%, 10% and 0% to the five factors based on prior-month highest to lowest returns.
• Medium-term factor momentum – each month allocates 40%, 30%, 20%, 10% and 0% to the five factors based on past 12-month highest to lowest returns.
• Structurally overweighting momentum – each month gives double weight to the momentum factor and zero weight to size factor.
• Volatility scaling of the momentum factor – each month scales the momentum factor allocation between 40% and 0% based on the ratio of its 20-year volatility to its 12-month volatility, with remaining funds allocated equally to the other four factors.
• Seasonal momentum – each month allocates 40%, 30%, 20%, 10% and 0% to the five factors based on their average historical returns for the same calendar month over the last 20 years.
• Positioning based on investor sentiment – each month takes 200% (0%) exposure to an equal-weighted factor portfolio when last-month Baker-Wurgler investor sentiment is positive (negative).
• Exploiting long-term factor decay – takes an initial 200% exposure to an equal-weighted factor portfolio and linearly reduces exposure to 0% at the end of the sample.

He applies these strategies to five widely accepted U.S. stock market factors: size, value, profitability, investment and momentum. His benchmark is the monthly rebalanced equal-weighted portfolio of these five factors. For each strategy, he addresses general concerns such as portfolio maintenance frictions and recent performance decay, and he identifies strategy-specific concerns. He concludes with minimum standards for future factor timing studies (see the table below). Using monthly returns for the selected factors during July 1963 until December 2024, he finds that:

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