Momentum Investing

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:

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 May 2025.

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

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 April 2025, we find that: Keep Reading

Can investors exploit the definition of conventional 12-2 stock momentum (long the tenth, or decile, of stocks with the highest returns from 12 months ago to two months ago and short the decile with the lowest) to form more persistent momentum portfolios? In the April 2025 revision of their paper entitled “Momentum at Long Holding Periods”, Paul Calluzzo, Fabio Moneta and Selim Topaloglu address the tradeoff between momentum portfolio reformation frequency (turnover) and returns by exploiting the predictability of future momentum. They first assess exclusion of stocks that: will exit the conventional portfolio next month based on known 11-1 returns (filter 11-1); and, are suffering short-term reversal as indicated by absence from the 11-2 momentum portfolio (filter 11-2). They generalize these ideas as two distinct strategies:

1. Generalized Filter (concentrated) – for each holding interval k, the filter k monthly portfolio consists of stocks that are always in the long or short sides over every window from 12-2 to (12−k+1)-2. For example, the filter 3 portfolio is long (short) stocks in the top (bottom) decile of returns for all of 12-2, 11-2, and 10-2 ranking windows.
2. Blended (diversified) – for each holding interval k, the blended k monthly portfolio assigns 1/k weight to returns for each stock in each window from 12-2 to (12−k+1)-2 before ranking stocks into deciles. For example, the blended 4 portfolio assigns one-fourth weight to the returns for each stock for each of the 12-2, 11-2, 10-2, and 9-2 windows before ranking stocks into deciles and forming a portfolio that is long (short) stocks in the top (bottom) decile.

Both strategies therefore have overlapping monthly portfolios. Their baseline level of round-trip (buy and sell) trading frictions is 0.25%, but they also test levels of 0.125% and 0.375%. Using monthly returns for all U.S. NYSE/AMEX/NASDAQ common stocks, excluding utilities, financials and stocks priced under $5, during January 1927 through December 2022, they find that:

Keep Reading

The performance of conventional factor portfolios, long and short extreme quantiles of assets sorted on the factor metric, faces considerable skepticism (see “Compendium of Live ETF Factor/Niche Premium Capture Tests”). Is their some more surgical way to capture theoretical factor premiums? In their March 2025 paper entitled “Investment Base Pairs”, Christian Goulding and Campbell Harvey offer a factor portfolio construction approach that confines portfolio long-short selections to pairs that most strongly exhibit value, momentum and carry premiums (base pairs). The approach identifies enduring pair relationships, not short-lived price gaps. Base pair identification derives from a combination of five variables:

1. The correlation between an asset’s factor signal and its own subsequent return.
2. The correlation between an asset’s factor signal and the paired asset’s subsequent return.
3. The correlation between factor signals between paired assets.
4. Differences in factor signal volatilities between paired assets.
5. Differences in average signal levels between paired assets.

They apply this base pair identification approach by each month reforming long-short, leveraged portfolios of futures and forwards base pairs to generate 20-year backtests of 12 strategies: Equity Value, Bond Value, Currency Value, Commodity Value, Equity Momentum, Bond Momentum, Currency Momentum, Commodity Momentum, Equity Carry, Bond Carry, Currency Carry and Commodity Carry. They also look at strategy averages by class and factor, and overall (All). Benchmarks are comparable conventional strategies that rank assets only on a factor signal. Using monthly data for 64 liquid futures and forwards series (15 equities, 13 bonds, 9 currencies and 27 commodities) during January 1985 through September 2023, they find that: Keep Reading

“SACEVS and SACEMS Strategy Momentum?” finds support for belief that a strategy exploiting the relative performance of Simple Asset Class ETF Value Strategy (SACEVS) Best Value and Simple Asset Class ETF Momentum Strategy (SACEMS) Equal-Weighted (EW) Top 2 boosts performance, with focus on a 60%-40% tilt toward the strategy with the stronger past returns. It also considers a full tilt (100%-0%) toward the stronger strategy for one lookback interval. Here, we examine sensitivity of the performance of the full tilt alternative (SACEVS-SACEMS Momentum) across lookback intervals ranging from one to 12 months. This alternative holds either SACEVS Best Value or SACEMS EW Top 2 according to which has the higher past return. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as essential performance metrics. As a benchmark, we use the monthly rebalanced SACEVS Best Value-SACEMS EW Top 2 50%-50% baseline (SACEVS-SACEMS 50-50 Baseline). Using monthly returns for SACEVS Best Value and SACEMS EW Top 2 during July 2006 through January 2025, we find that:

Keep Reading

Is stock price all-time high a consistently effective trigger for trend following? In their January 2025 paper entitled “Does Trend-Following Still Work on Stocks?”, Carlo Zarattini, Alberto Pagani and Cole Wilcox revisit and extend the results of prior 2005 research on long-only trend following based on stock price all-time high that used 1980-2004 data. They assemble survivorship bias-free data for all liquid U.S. stocks backward through 1950 and forward through November 2024. They define liquid as unadjusted closing price above $10 and 42-trading day average dollar volume over $1 million (adjusted downward for past years based on inflation) at the time of portfolio reformation. Their trading/rebalancing rules are:

• For each qualifying stock not in the portfolio, if the daily adjusted close (considering splits and dividends) reaches or exceeds its all-time high, buy the stock at the next open.
• For each stock in the portfolio, if the daily close is below the daily stop-loss level, sell the stock at the next open. The daily stop-loss level is either the previous stop-loss or, if higher, a new stop-loss computed from the All-Time High price (ATH) and 42-day Average True Range (ATR) at the most recent close, as follows:

For portfolio testing, they focus on Russell 3000 stocks from 1991 through the end of the sample period. They use 42-trading day actual volatilities to set position sizes for selected stocks to achieve approximately equal expected contributions to a 30% annualized portfolio target volatility. They allow up to 200% leverage to achieve these positions sizes, with adjustment to position size when higher leverage is indicated. They recompute stock weights at each close to reflect new portfolio entries and exits and changes in expected stock volatilities. They assume frictions/costs that cover broker commissions, slippage (impact of trading) and interest/borrowing costs. Using daily interest rates and daily prices, dividends and other price adjustments for a broad sample of U.S. stocks during January 1950 through October 2024, they find that:

Keep Reading

How sensitive is performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) to choice of momentum calculation lookback interval, and what interval works best? To investigate, we generate gross compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) for SACEMS Top 1, equally weighted (EW) EW Top 2 and EW Top 3 portfolios over lookback intervals ranging from one to 12 months. All calculations start at the end of February 2007 based on inception of the commodities exchange-traded fund and the longest lookback interval. Using end-of-month total (dividend-adjusted) returns for the SACEMS asset universe during February 2006 through November 2024, 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 November 2024, we find that: Keep Reading