Active Dual Momentum GTAA Strategy Weekly Rebalancing

This research analyzes a Global Tactical Asset Allocation strategy using ETFs, combining relative and absolute momentum signals. The approach utilizes weekly rebalancing and multi-timeframe signals to optimize Sharpe and Calmar ratios while reducing drawdowns.

Strategy Decoder Editorial · 2026-05-22 · 4 min read

Key takeaways

A dual momentum GTAA strategy can significantly enhance risk-adjusted returns compared to a simple equally-weighted benchmark.
Combining relative and absolute momentum filters helps avoid assets in a downtrend, even if they are top performers within a declining universe.
Weekly rebalancing offers a balance between responsiveness to market changes and mitigating excessive transaction costs.
The strategy proved robust across various lookback periods and numbers of selected assets, with 1-week momentum being an outlier.
Careful selection of the investment universe is a critical, often hidden, parameter that can significantly impact strategy performance.

Global Tactical Asset Allocation (GTAA) strategies offer a dynamic alternative to static investment approaches, aiming to improve risk-adjusted returns by actively adjusting asset allocations based on evolving market conditions. These strategies move beyond simply replicating market performance, instead seeking to optimize portfolios across various asset classes like equities, bonds, and commodities. A notable example is Meb Faber's work from 2007, which leveraged straightforward trend-following rules to guide asset selection.

Traditional GTAA models, such as those discussed as "Pragmatic Asset Allocation" (PAA), often focus on minimizing volatility for a given level of expected return. However, a recent exploration, as reported by QuantPedia, investigates a more active variant: an "Active Dual Momentum GTAA Strategy." This strategy seeks to be more responsive to market signals, selecting assets not only based on risk but also on their potential for future returns, while avoiding the pitfalls of excessively high-frequency trading.

The Dual Momentum Approach

The core of this strategy lies in its dual momentum methodology, a concept popularized by practitioners like Gary Antonacci. This approach involves two key components:

1. Relative Momentum: Assets are ranked based on their performance over a specified lookback period. The strategy then selects a predetermined number of top-performing assets from this pool. 2. Absolute Momentum Filter: A crucial safeguard is then applied. Even if an asset ranks highly based on relative momentum, it is only included in the portfolio if its absolute momentum (rate of change) is positive. This prevents investment in assets that, while performing better than their peers, are still in an overall decline during broad market downturns.

This two-pronged selection process aims to capture leaders while protecting against systemic market weaknesses. The QuantPedia research used a simplified rate-of-change (RoC) calculation for momentum, minimizing parameters and enhancing transparency, rather than relying on more complex moving average crossovers.

Key Strategy Parameters and Implementation

The effectiveness of such a strategy hinges on several parameters:

Number of Selected Assets: How many of the top-ranked assets should be included in the portfolio?
Lookback Window: Over what period should momentum be calculated? This can range from a few weeks to many months.
Investment Universe: The choice of assets to include in the eligible pool is a critical, often understated, factor. As the report highlights, selecting an asset like Bitcoin in 2015 would have dramatically altered performance, illustrating the significant impact of universe selection. For this research, the universe comprised ETFs such as SHY, IEF, UUP, GLD, USO, SPY, EFA, QQQ, and EEM.
Rebalancing Frequency: The study opted for weekly rebalancing on Wednesdays, striking a balance between reacting to market shifts and managing transaction costs. Weekly data was used, ensuring responsiveness without the excessive turnover associated with daily rebalancing. The mid-week rebalance also helps minimize holiday-related market closure artifacts, which often occur on Mondays or Fridays.

Performance and Robustness

The backtest, spanning from February 2007 to March 2026, compared the dual momentum strategy against a simple equally-weighted (EW) average of all nine ETFs in the investment universe. The results indicated that the dual momentum approach consistently delivered superior risk-adjusted returns. While strategy volatility was sometimes slightly higher than the benchmark, the significantly enhanced performance led to substantially improved Sharpe and Calmar ratios and reduced drawdowns.

Lookback Period Analysis

Initial analysis fixed the number of selected assets at three and varied the momentum lookback periods from 1 week to 50 weeks. Almost every lookback period, barring the 1-week momentum, outperformed the equally-weighted benchmark. The poor performance of the 1-week momentum was attributed to its tendency to capture fleeting price movements rather than sustained trends. This suggests that meaningful momentum typically manifests over longer intervals.

Number of Selected ETFs Analysis

Further investigation fixed the lookback period at 25 weeks (approximately half a year, a common duration for momentum strategies) and varied the number of selected ETFs from one to nine. This analysis further demonstrated the robustness of the strategy, with varying allocations still yielding strong results compared to the benchmark.

Why it matters for algo traders

For algorithmic and quantitative traders, the Active Dual Momentum GTAA Strategy presents several compelling insights. Firstly, the study underscores the power of combining relative and absolute momentum in portfolio construction. This dual filter provides a robust mechanism to not only identify strong performers but also to avoid general market downturns, a critical feature for downside protection. Algo traders can leverage this concept by integrating similar dual momentum screens into their systematic asset allocation models, potentially improving portfolio resilience during bear markets.

Secondly, the choice of weekly rebalancing is highly relevant. It provides a practical balance for algorithmic systems between responsiveness and managing transaction costs and slippage, which can be significant for higher-frequency strategies. Backtesting methodologies should carefully consider the trade-off between rebalancing frequency and realistic implementation costs. Furthermore, the explicit acknowledgment of the investment universe as a 'hidden parameter' is crucial for quants. Defining the investable universe is often overlooked but profoundly impacts strategy performance. Algorithmic traders must rigorously test how different universes affect their backtest results and real-time trading.

Finally, the robustness observed across various lookback periods and numbers of selected assets suggests that the underlying momentum phenomenon is not overly sensitive to precise parameter tuning (beyond very short lookbacks). This robustness can increase confidence in strategy stability and reduce the risk of overfitting during the development phase of new algorithmic trading strategies.

Tags: momentum, gtaa, backtesting, etf, asset allocation

Based on reporting by QuantPedia.