Momentum Investing

How material is the rebalance timing luck (RTL) induced by picking a trading day to reform a monthly stock momentum strategy? Is there a way to manage the risk of bad luck? In their November 2025 paper entitled “The Tranching Dilemma. A Cost-Aware Approach to Mitigate Rebalance Timing Luck in Factor Portfolios”, Carlo Zarattini and Alberto Pagani investigate monthly momentum portfolio tranching, holding multiple portfolios with the same strategy but with different reformation cycles, as a way to manage RTL. Their test strategy each month:

1. Identifies the 1,000 most liquid components of the Russell 3000 Index.
2. Finds the 100 stocks with highest total returns from 12 months ago to one month ago.
3. Reforms an equal-weighted portfolio of the 20 out of these 100 stocks with the highest momentum quality based on the percentage of days with positive and negative returns during the ranking interval.

They run this strategy with reformation cycles from the close on trading day one of each month to the close on trading day 20 (or last) of each month. They then consider effects on RTL of holding 2, 4, 5, 10 and 20 tranches across these cycles. They assume portfolio reformation frictions as standard Interactive Brokers fee of $0.0035 per share with minimum $0.35 per trade (doubled for sell transactions to account for SEC clearing fees). Using daily data for Russell 3000 Index components during 1991 through 2024, they find that:

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Can investors rely on price/return momentum as an eternal strategy foundation? In their August 2025 paper entitled “Momentum Factor Investing: Evidence and Evolution”, flagged by a subscriber, Bart van Vliet, Guido Baltussen, Sipke Dom and Milan Vidojevic review the evolution of momentum in the literature and examine momentum factor robustness over a long sample period. Their baseline momentum factor portfolio is each month long (short) the fifth, or quintile, of value-weighted or equal-weighted stocks among the top 80% of NYSE market capitalizations with the highest (lowest) price momentum from 12 months to one month ago. Specifically, they:

• Review empirical evidence on traditional price momentum, including foundational papers and key empirical findings.
• Examine the robustness of momentum to rule out data mining and address out-of-sample decay, including international evidence.
• Address the evolution of momentum beyond price-based measures to momentum in earnings and analyst revisions, industries/networks and equity factors.
• Evaluate crash risk and explore crash-avoidance strategy features.
• Discuss economic/sentiment drivers of momentum.

Based on the body of research on momentum starting in 1967 and using data for new empirical analyses spanning 1866-2024 for the U.S. and 1990-2024 for global equity markets, they find that: Keep Reading

“Developed Country Stock Index Momentum?” summarizes a short paper finding that  MSCI developed country stock market indexes may exhibit exploitable momentum since 1970. However, indexes do not include costs of maintaining index-tracking funds, and the availability of such funds may induce market adaptation. Does the specified strategy work for exchange-traded funds (ETF) designed to track the selected indexes? To investigate, we test a momentum strategy that every six months at the ends of June and December:

• Ranks as available 22 developed country stock market ETFs based on past 6-month total returns in U.S. dollars.
• Reforms an equal-weighted portfolio of winners as the half of ETFs with the highest returns (EW Long).

We use the equal-weighted, similarly rebalanced portfolio of all 22 ETFs (EW All, as available) and SPDR S&P 500 ETF (SPY) as benchmarks. Using monthly total returns (including dividends) for the 22 ETFs, including SPY, during March 1996 (earliest available for non-SPY ETFs) through June 2025, we find that: Keep Reading

Is there an easy, low-frictions way to implement an attractive momentum strategy at the country market level? In his short October 2025 paper entitled “The Lazy Man’s Momentum Strategy”, flagged by a subscriber, Javier Estrada tests a “lazy” momentum strategy that every six months at the ends of June and December:

• Ranks developed country stock market indexes based on past 6-month total returns in U.S. dollars.
• Reforms an equal-weighted portfolio of winners (losers) as the half of indexes with the highest (lowest) returns.

He uses the MSCI World Index, consisting of the market capitalization-weighted developed country indexes in the sample, as a benchmark. Using monthly total returns (including dividends) for the MSCI World Index and for MSCI developed country stock market indexes from inception in December 1969 (17 countries) through December 2024 (23 countries), he finds that: Keep Reading

Does bitcoin now behave like a conventional financial asset? In their short November 2025 paper entitled “From Time-Series Momentum to Size-Factor Comovement: Bitcoin’s Continuing Evolution as a Financial Asset”, Samuel Rosen and Hongcheng Wang investigate the evolution of bitcoin. Specifically, they:

1. Perform autoregressions of weekly bitcoin returns to evaluate time-series momentum at horizons of one to eight weeks.
2. Regress bitcoin returns on returns of U.S. market, size and value equity factors and some financial variables to assess bitcoin behavior in equity context.

To assess changes in relationships over time, they employ rolling windows, split samples and dummy regression variables. Using daily bitcoin returns and monthly returns for the selected equity factors and other financial variables during 2011 through 2024, 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 November 2025.

The home page, Simple Asset Class ETF Momentum Strategy (SACEMS) and Simple Asset Class ETF Value Strategy (SACEVS) now show preliminary positions for December 2025. SACEMS rankings are very close for ranks 3 and 4, which could flip by the close. SACEVS allocations are unlikely to change by the close.

Can insights inferred from real-time financial news by large language models (LLM) such as ChatGPT 4.0 mini enhance a conventional stock momentum strategy? In their October 2025 paper entitled “ChatGPT in Systematic Investing – Enhancing Risk-Adjusted Returns with LLMs”, Nikolas Anic, Andrea Barbon, Ralf Seiz and Carlo Zarattini investigate whether ChatGPT can improve a conventional momentum strategy applied to S&P 500 stocks by extracting predictive signals from minute-level Stock News API news articles. Specifically, they each month:

1. Rank stocks based on returns from 12 months ago to one month ago.
2. Construct an equal-weighted or value-weighted long-only momentum portfolio by buying stocks in the top 20% of rankings (top two deciles).
3. Ask ChatGPT to quantify the potential of each stock in the momentum portfolio to increase Sharpe ratio and suppress maximum drawdown (MaxDD), and weight each stock according to this signal.

They apply 0.02% trading frictions to portfolio changes to test net performance. Using daily total returns for S&P 500 stocks, relevant high-frequency Stock News API articles and the daily U.S. risk-free rate to perform model validation during October 2019 through December 2023 and out-of-sample testing during January 2024 through March 2025, they find that: Keep Reading

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