Momentum Investing

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

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 March 2025.

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

“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:

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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:

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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

A subscriber wondered whether choosing the fourth place asset class exchange-traded fund (ETF) rather than the third place class ETF for monthly reformation of the Simple Asset Class ETF Momentum Strategy (SACEMS) would matter if the difference in respective past returns over the ranking interval is less than 0.5%. To investigate, we take a broad, systematic approach and test the following two scenarios:

1. Impose a buffer of -0.5% when reforming the SACEMS portfolio. Specifically, each month subtract 0.5% from the past returns of the first, second and third places of last month before reranking. This test captures the subscriber question as a subset, but tends to increase trading due to small ranking return differences.
2. Impose a buffer of 0.5% when reforming the SACEMS portfolio. Specifically, each month add 0.5% to the past returns of ETFs for the first, second and third places of last month before reranking. This test tends to suppress trading due to small ranking return differences. 

For the second scenario, we also look at effects of buffers larger than 0.5% for the Equal-Weighted (EW) Top 2 SACEMS portfolio. Using monthly SACEMS outputs during June 2006 through November 2024, we find that: Keep Reading

Does degree of co-movement among commodity exchange-traded funds (ETF) predict whether momentum or reversal is imminent? In their September 2024 paper entitled “How to Improve Commodity Momentum Using Intra-Market Correlation”, Radovan Vojtko and Margaréta Pauchlyová investigate whether the relationship between short-term and long-term average pairwise return correlations indicates when to pursue momentum and when to pursue reversal among commodity ETFs. Based on prior research, they consider four ETFs: DBA (agriculture), DBB (base metals), DBE (energy) and DBP (precious metals). Their strategies consists of each month:

1. Ranking the four ETFs by 12-month past return.
2. Calculating average pairwise 20-day and 250-day daily return correlations for the four ETFs.
3. If the average short-term correlation is higher (lower) than the average long-term correlation, executing an equal-weighted momentum (reversal) strategy by buying (selling) the two top-ranked ETFs and selling (buying) the two bottom-ranked ETFs.

Using daily adjusted closes for the selected ETFs from the end of 2007 through early 2024, they find that: Keep Reading

Is momentum a rational firm earnings growth proxy rather than a manifestation of investor underreaction/overreaction to news? In their August 2024 paper entitled “A Unified Framework for Value and Momentum”, Jacob Boudoukh, Tobias Moskowitz, Matthew Richardson and Lei Xie present an asset pricing model that treats value and momentum as complementary inputs to a present value of earnings estimate. They view momentum, return from 12 months ago to one month ago, as a noisy proxy for earnings growth. They test this view by relating momentum retrospectively to actual earnings growth. They further construct an asset pricing model based on a single growth-adjusted value factor and compare its effectiveness to that of the widely used 4-factor (market, size, book-to-market, momentum) model. They calculate growth-adjusted value factor returns via monthly, 5-year smoothed bivariate value-growth regressions, with three alternatives for earnings growth adjustment: (1) momentum as a proxy for growth; (2) a combination of momentum and analyst earnings forecasts as a proxy for growth; and, (3) retrospective actual earnings. They focus on individual U.S. stocks, but also look at U.S. industries, stocks across 23 developed equity markets and Japanese stocks. Using monthly book-to-market ratios, stock returns, next-year earnings growth forecasts and actual annual earnings as available for Russell 3000 stocks since the end of March 1984, for stocks in 23 developed country markets since the end of January 1989 and for stocks in the MSCI Japan Index since the end of August 1988, all through December 2019, they find that:

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