Momentum Investing

A subscriber asked about the performance of a 50-50 combination of a basket of momentum stock exchange-traded funds (ETF) and a basket of quality stock ETFs, specifically with comparison to a 50-50 combination of the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS). To investigate, we employ results from:

• The EW portfolio of momentum ETFs in “Are Equity Momentum ETFs Working?” as the basket of momentum ETFs.
• The equal-weighted (EW) portfolio of quality ETFs in “Are Stock Quality ETFs Working?” as the basket of quality ETFs.
• The 50-50 SACEVS Best Value-SACEMS EW Top 2 portfolio.

We assume monthly rebalancing of the 50-50 momentum-quality portfolio. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). We also use SPDR S&P 500 ETF (SPY) to assess effectiveness of the factor portfolios. Using monthly total returns from the above three sources and SPY during April 2007 (limited by momentum ETF data) through October 2025, we find that: Keep Reading

Are stock and sector momentum strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider nine momentum-oriented equity ETFs, all currently available, in order of longest to shortest available histories:

• Invesco Dorsey Wright Momentum ETF (PDP) – invests at least 90% of assets in approximately 100 U.S. common stocks per a proprietary methodology designed to identify powerful relative strength characteristics, reformed quarterly. Benchmark is iShares Russell 3000 ETF (IWV).
• Invesco Dorsey Wright SmallCap Momentum ETF (DWAS) – invests at least 90% of assets in approximately 200 small-capitalization U.S. common stocks per a proprietary methodology designed to identify powerful relative strength characteristics, reformed quarterly. Benchmark is iShares Russell 2000 ETF (IWM).
• iShares Edge MSCI USA Momentum Factor ETF (MTUM) – holds U.S. large-capitalization and mid-capitalization stocks with relatively high momentum. Benchmark is iShares Russell 1000 ETF (IWB).
• First Trust Dorsey Wright Focus 5 ETF (FV) – holds five equally weighted sector and industry ETFs selected via a proprietary relative strength methodology, reformed twice a month. Benchmark is IWV.
• iShares MSCI Intl Momentum Factor ETF (IMTM) – holds international developed market large-capitalization and mid-capitalization stocks with relatively high momentum. Benchmark is iShares MSCI ACWI ex U.S. ETF (ACWX).
• SPDR Russell 1000 Momentum Focus ETF (ONEO) – tracks the Russell 1000 Momentum Focused Factor Index, picking U.S. stocks that have recently outperformed. Benchmark is IWB.
• Alpha Architect U.S. Quantitative Momentum ETF (QMOM) – seeks to buy stocks with the highest quality momentum via a portfolio that generally holds 50 stocks. Benchmark is IWV.
• First Trust Dorsey Wright Dynamic Focus 5 ETF (FVC) – similar to FV but with added risk management via an increasing allocation to cash equivalents when relative strengths of more than one-third of the universe diminish relative to a cash index, reformed twice a month. Benchmark is IWV.
• JPMorgan U.S. Momentum Factor ETF (JMOM) – holds U.S. common stocks with strong risk-adjusted momentum. Benchmark is IWB.

We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). We assign broad market benchmark ETFs according to momentum fund descriptions. Using monthly dividend-adjusted returns for the nine momentum funds and respective benchmarks as available through October 2025, we find that: Keep Reading

Does bitcoin (BTC) price reliably exhibit momentum or reversion? To investigate, we try three tests:

1. Calculate autocorrelations (serial correlations) between daily, weekly and monthly BTC returns and respective BTC returns for the next 10 intervals (for example, correlation of daily return with returns the next 10 days). Positive and negative correlations suggest momentum and reversion, respectively.
2. Calculate correlations between next-week BTC return and current BTC price relative to its high (percentage below) or low (percentage above) over the last 13 weeks. A positive (negative) correlation to price relative to a recent high or low indicates momentum (reversion).  
3. Calculate average next-week BTC returns by ranked tenth (decile) of BTC price relative to its high or low over the last 13 weeks.

Using daily, weekly and monthly BTC closing prices during September 14, 2014 (the earliest available from the source) through October 22, 2025, we find that:

Keep Reading

Are some calendar months more likely to exhibit stock market continuation or reversal than others, perhaps due to seasonal or fund rebalancing/reporting effects? In other words, is intrinsic (times series or absolute) momentum an artifact of some months or all months? To investigate, we relate U.S. stock index returns for each calendar month to those for the preceding 3, 6 and 12 months. Using monthly closes of the S&P 500 Index since December 1927 and the Russell 2000 Index since September 1987, both through September 2025, we find that: Keep Reading

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 October 2025.

The home page, Simple Asset Class ETF Momentum Strategy (SACEMS) and Simple Asset Class ETF Value Strategy (SACEVS) now show preliminary positions for November 2025. SACEMS rankings are unlikely to  change by the close. SACEVS allocations are unlikely to change by the close.

What are optimal intrinsic/absolute/time series momentum (IM) and simple moving average (SMA) lookback intervals for different asset class proxies? To investigate, we use data for the following ten asset class exchange-traded funds (ETF), plus Cash:

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

For IM tests, we invest in each ETF (Cash) when its return over the past one to 12 months is positive (negative). For SMA tests, we invest in each ETF (Cash) when its price is above (below) its average monthly price at the ends of the last two to 12 months. We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key metrics for comparing different IM and SMA lookback intervals since earliest ETF data availabilities based on the longest IM lookback interval. Using monthly dividend-adjusted closing prices for the asset class proxies and the yield for Cash over the period July 2002 (or inception if not available by then) through September 2025, we find that:

Keep Reading

How can investors and fund managers best exploit premiums associated with value, momentum, profitability, investment and low volatility factors, either to generate absolute return or to beat a market benchmark? In his September 2025 paper entitled “Strategic Style Allocation: Absolute or Relative?”, Pim van Vliet examines strategic allocation across long-only, value-weighted versions of these equity factors, depending on objective: absolute return or benchmark outperformance. To assess absolute return, he evaluates Sharpe ratios of factor allocations. To assess benchmark outperformance, he evaluates information ratios of factor allocations. He also investigates dynamic allocation between low volatility and the other factors, with portfolio adjustment frictions. Using long-only U.S. value-weighted factor returns during July 1963 through May 2025 and global factor index returns during January 1999 through March 2025, he finds that: Keep Reading

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:

Keep Reading