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.
A subscriber suggested that the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS) may each exhibit return momentum at the strategy level, such that an investor considering both as in Combined Value-Momentum Strategy may want to pick the one with a stronger recent return. To investigate, we test a SACEVS Best Value-SACEMS Equal-Weighted (EW) Top 2 combination strategy that each month picks the strategy with the higher return over a specified lookback interval (SACEVS-SACEMS Momentum). We consider lookback intervals of 1 to 12 months. We use monthly rebalanced 50% SACEVS Best Value-50% SACEMS EW Top 2 (SACEVS-SACEMS 50-50) as a benchmark. We focus on gross compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance metrics. Using SACEVS Best Value and SACEMS EW Top 2 gross monthly returns during July 2006 (limited by SACEMS) through January 2026, we find that:
Does adding a position take-profit (stop-gain) rule improve the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) by harvesting some upside volatility? SACEMS each months picks winners from among the a set of eight asset class exchange-traded fund (ETF) proxies plus cash based on past returns over a specified interval. To investigate the value of stop-gains, we augment SACEMS with a simple rule that: (1) exits to Cash from any current winner ETF when its intra-month return rises above a specified threshold; and, (2) re-sets positions per winners at the end of the month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using monthly total (dividend-adjusted) returns and intra-month maximum returns for the specified assets during February 2006 through January 2026, we find that:Keep Reading
Does adding a position stop-loss rule improve the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) by avoiding some downside volatility? SACEMS each months picks winners from among the a set of eight asset class exchange-traded fund (ETF) proxies plus cash based on past returns over a specified interval. To investigate the value of stop-losses, we augment SACEMS with a simple rule that: (1) exits to Cash from any current winner ETF when its intra-month return falls below a specified threshold; and, (2) re-sets positions per winners at the end of the month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using monthly total (dividend-adjusted) returns and intra-month drawdowns for the specified assets during February 2006 through January 2026, we find that:Keep Reading
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:
