Momentum Investing

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 2026, we find that: Keep Reading

Having iteratively compared the Simple Asset Class ETF Momentum Strategy (SACEMS) with a fixed lookback interval to SACEMS with a VIX-based variable lookback interval over several years, we are dropping the former and adopting the latter as the tracked baseline. We are making one other change to eliminate a legacy data workaround. Specifically: 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 August 2026.

The home page, Simple Asset Class ETF Momentum Strategy (SACEMS) and Simple Asset Class ETF Value Strategy (SACEVS) now show preliminary positions for September 2026. Past returns for the top assets are closely bunched, so SACEMS rankings could change by the close. SACEVS allocations are unlikely to change by the close.

Under what conditions does equity market time series momentum (TSMOM) work and not work? In their June 2026 paper entitled “Boundaries of Time Series Momentum”, Matti Suominen and Erik Hjalmarsson examine performance of equity market TSMOM across ranges of three valuation metrics: cyclically adjusted price-to-earnings ratio (CAPE), dividend yield and term spread (difference between long-maturity and short-maturity Treasury instrument yields). They specify TSMOM as long (short) the market when past market return in excess of the risk-free rate over a specified lookback interval is positive (negative). They specify a Boundaries variable for predicting TSMOM performance as follows:

1. Scale each of the CAPE, dividend yield and term spread to values between -1 and 1 as follows:
   1. Subtract from its 12-month average the past 10-year or 20-year minimum observation and divide the difference by the past 10-year or 20-year range (maximum minus minimum).
   2. Multiply results by two and subtract one.
2. Compute a Boundaries variable as the square of the scaled term spread plus the square of scaled CAPE or scaled dividend yield.

For a given equity market, they construct a TSMOM index as an equal-weighted average of 25 time series momentum strategies, with lookback and investment intervals of 1, 3, 6, 9 or 12 months. They then explore how index returns interact with the Boundaries variable. Using the specified inputs and stock index returns during July 1927 through December 2024 for the U.S. and during January 1989 through December 2024 for a 20-country international sample, they find that: Keep Reading

Does breadth of equity sector performance predict overall stock market return? To investigate, we relate next-month stock market return to sector breadth (number of sectors with positive past returns) over lookback intervals ranging from 1 to 12 months. We consider the following nine sector exchange-traded funds (ETF) offered as Standard & Poor’s Depository Receipts (SPDR):

State Street Materials Select Sector SPDR (XLB)
State Street Energy Select Sector SPDR (XLE)
State Street Financial Select Sector SPDR (XLF)
State Street Industrial Select Sector SPDR (XLI)
State Street Technology Select Sector SPDR (XLK)
State Street Consumer Staples Select Sector SPDR (XLP)
State Street Utilities Select Sector SPDR (XLU)
State Street Health Care Select Sector SPDR (XLV)
State Street Consumer Discretionary Select Sector SPDR (XLY)

We use State Street SPDR S&P 500 ETF Trust (SPY) to represent the overall stock market and also relate next-month SPY return to the sign of past SPY return. Using monthly dividend-adjusted returns for SPY and the sector ETFs during December 1998 through April 2026, we find that: Keep Reading

Should investors seeking to exploit both value and momentum factors in U.S. stocks prefer two separate portfolios or one that integrates stock selection? In his February 2026 paper entitled “Don’t Mix What Should Be Separated: Why Combining Value and Momentum Signals Destroys Alpha”, Carlos Morales compares:

1. A combined ranking method, integrating value and momentum signals for each stock into one composite score and forming a single hedge portfolio with 100 long and 100 short equal-weighted positions.
2. Two separate and independent value and momentum long-short hedge portfolios, each with of 100 long and 100 short equal-weighted positions.

His value ranking system is an equal-weighted, industry-normalized composite of price-to-book value, free cash flow yield, sales-to-enterprise value and earnings yield. His momentum ranking system is an equal-weighted combination of return from 12 months ago to one month ago and 6 months ago to one month ago. As in many academic studies, he ignores trading frictions. For Sharpe ratio calculations, he assumes a constant 1.8% risk-free rate. Using specified value and momentum inputs for the 1,000 U.S. stocks with the largest market capitalizations during January 2000 through December 2025, he finds that:

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Is there a way to enhance time series momentum by considering both trend regime and expected risk-adjusted performance during each state? In his March 2026 paper entitled “Rethinking Trend Following: Optimal Regime-Dependent Allocation”, Valeriy Zakamulin describes and tests his optimal regime-dependent allocation (OPT) strategy, which:

1. Determines the trend following regime based on either 2-regime (Bull/Bear) or 4-regime (Bull/Correction/Bear/Rebound) models.
2. Assigns asset exposures that historically maximize gross Sharpe ratio during each regime, with 100% long for the regime with the highest estimated Sharpe ratio and exposures scaled down across other regimes according to their relative signal-to-noise ratios. Regimes with sufficiently negative estimates may reach 100% short, but regimes with weak/very noisy signals have weights close to zero.

Backtests employ data:

• Since July 1926 for value-weighted U.S. equity indexes for the overall market, the largest and smallest fifths of stocks, and the fifths of stocks with the highest and lowest book-to-market ratios.
• Since January 1975 (1977 for Canada) for value-weighted developed market equity indexes of 14 other countries.
• For robustness, since July 1963 for 18 U.S. stock portfolios formed on multiple factors/firm characteristics.

Sharpe ratio estimates derive from expanding windows of historical training data initially through 1968 for the long U.S. samples, December 2003 for the other country samples and 1997 for the U.S. factor/firm characteristics portfolios. Using the specified datasets through December 2025, he finds that:

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“Simple Term Structure ETF/Mutual Fund Momentum Strategy” tests a simple relative momentum strategy on the term structure of U.S. Treasuries using exchange-traded fund (ETF) and mutual fund proxies. Here, we update and extend that analysis with the following seven ETFs:

State Street SPDR Bloomberg 1-3 Month T-Bill ETF (BIL)
iShares 1-3 Year Treasury Bond ETF (SHY)
Vanguard Short-Term Inflation-Protected Securities Index ETF (VTIP)
iShares 3-7 Year Treasury Bond ETF (IEI)
iShares 7-10 Year Treasury Bond ETF (IEF)
iShares TIPS Bond ETF (TIP)
iShares 20+ Year Treasury Bond ETF (TLT)

We allocate all funds at the end of each month to the one ETF with the highest total return over a specified ranking (lookback) interval, ranging from one month to 12 months. We start the test in July 2002 and add ETFs as they become available. To accommodate the longest lookback interval, portfolio formation commences 12 months after the start of the sample. We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance metrics. Using monthly dividend-adjusted closing prices for the seven ETFs as they become available through February 2026, we find that: Keep Reading

A subscriber suggested adding a simple moving average (SMA) timing rule to “Top 5 or Top 10 NASDAQ 100 Momentum Stocks?” in order to suppress relatively deep maximum drawdowns (MaxDD). To investigate, we consider SMAs for Invesco QQQ Trust (QQQ) ranging from two months to 24 months. We then hold Top 5 or Top 10 (3-month U.S. Treasury bills, T-bills) when prior-month QQQ is above (below) its SMA. As key performance metrics, we use gross compound annual growth rate (CAGR), MaxDD and Sharpe ratio with average monthly yield on T-bills during a year as the risk-free rate for that year. Using monthly Top 5 and Top 10 portfolio returns and T-bill yields since January 2008 and end-of-month dividend-adjusted QQQ prices since February 2006, all through January 2026, we find that: Keep Reading