Momentum Investing

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:

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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:

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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:

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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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