Do financial market prices reliably exhibit momentum? If so, why, and how can traders best exploit it? These blog entries relate to momentum investing/trading.
Should investors who believe that the U.S. dollar (USD) is doomed by deficits/debt consider a momentum strategy holding the USD hedge that most recently performed best? To investigate, we test a simple momentum strategy (Winner) that each month holds the one of the following three assets with the highest prior-month return:
We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) for performance comparison. We use a equal-weighted, monthly rebalanced (EW) portfolio of the three assets as a benchmark. We commence testing in September 2015 to allow momentum measurement (lookback) interval sensitivity analysis. Using monthly total returns for the above three assets during September 2014 (limited by BTC-USD) through December 2025, we find that:Keep Reading
Time-series momentum (TSMOM) is a well-documented finding that past returns predict next-period returns for many asset types. Is the relationship between past and future performance linear? In their December 2025 paper entitled “Nonlinear Time Series Momentum”, Tobias Moskowitz, Riccardo Sabbatucci, Andrea Tamoni and Björn Uhl compare a TSMOM trading strategy with non-linear weights to: (1) theoretically optimal weights; (2) published alternative weighting schemes; and, (3) a machine learning (neural network) method. They consider:
Time series for 8 equity index futures, 24 commodity futures and 21 interest rate and currency futures contracts. They roll futures contracts on the earlier of the last trade date or the first day of the futures contract month.
Momentum measurement (lookback) intervals of 21, 62 or 260 trading days.
Daily, weekly or monthly reweighting frequencies.
They seek to maximize out-of-sample gross Sharpe ratio based on TSMOM signals. They set asset position weights by dividing past return by most recent 260-day volatility and adjusting it to an arbitrary 12% annualized volatility target. Using front-month data for the selected futures contracts as available during January 1980 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 2025, we find that:Keep Reading
Does lack of liquidity among stocks in anomaly portfolios effectively block exploitation? In their November 2025 paper entitled “Liquidity Constraints and the Illusion of Anomaly Profitability”, Álvaro Cartea, Mihai Cucuringu, Qi Jin and Jiexiu Zhu assess exploitability of anomaly trading strategies after accounting for individual stock liquidities. They define liquidity of a stock as its capacity to absorb incremental volume relative to recently observed average daily volume without material price impact. They estimate anomaly portfolio profitability based on liquidity-constrained dollar trade sizes/profit for each anomaly portfolio stock. They apply this approach to 128 U.S. stock return anomalies, with both in-sample (same as originally published) and out-of-sample results. They initially assume zero trading costs to isolate the impact of liquidity constraints. They then estimate trading costs (either half the bid-ask spread or price impact estimates), exclude trades expected to be unprofitable and generate the combined effects of liquidity constraints and trading costs. Using data for stocks per the 128 anomalies during January 1930 through December 2023, they find that:
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:
Identifies the 1,000 most liquid components of the Russell 3000 Index.
Finds the 100 stocks with highest total returns from 12 months ago to one month ago.
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:
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
Become a CXO Member
Gain access to hundreds of premium investing research articles and CXO's trading strategies
