Momentum

Trend following was one of the most persistent anomalies in finance for nearly two centuries, yet its performance deteriorated sharply after the 2008 financial crisis. An analysis of approximately 100 liquid futures contracts from 1995 to 2025 shows that this decline is highly selective. The decisive factor is not asset class, liquidity, market electronification, or strategy crowding, but volatility-normalized tick size. After 2008, trend-following profits collapsed almost entirely on small-tick contracts across all signal horizons, while remaining largely intact on large-tick contracts. This finding suggests that modern trend-following portfolios are fundamentally split into two distinct regimes governed by market microstructure rather than traditional asset classifications.

This paper tests whether that rotation in attention leaves a systematic footprint. Using Donchian breakout signals across QQQ and Bitcoin, with cash as a fallback during periods of consolidation, we examine whether investors can harvest momentum without remaining permanently exposed to either asset’s full drawdown profile. The results suggest that the answer is yes: retail attention does not move randomly. It rotates, it concentrates, and—when measured through price breakouts—it can be systematically exploited.

This study investigates short-term price reversals—temporary retracements following adverse daily returns—and develops a systematic trading framework to capture this effect across multiple asset classes. Using daily data from six liquid ETFs spanning equities, fixed income, currencies, gold, and commodities over the period 2006–2025, the strategy applies a long-term trend filter based on a 200-day moving average combined with a multi-day pullback trigger. Trades are executed dynamically with volatility-adjusted position sizing and equal-weighted allocation across active signals. Parameter sweeps, sensitivity analyses, and sub-period tests are conducted to evaluate the robustness of the approach, including variations in moving average length, number of consecutive down days, holding periods, and alternative momentum indicators such as short-term RSI. The study also explores the practical integration of AI tools— ChatGPT and Claude—to assist in research, analysis, and visualization, assessing their effectiveness in generating coherent quantitative insights.

Commodities represent a vital but highly volatile asset class, characterized by pronounced cyclicality, lack of yield, and susceptibility to severe macroeconomic drawdowns. While cross-sectional (relative) momentum is a well-documented anomaly, its application in commodities often forces portfolios to hold the “least declining” assets during broad-based bear markets, resulting in unacceptable tail-risk. This study empirically evaluates the efficacy of a Dual Momentum framework—combining relative strength ranking with an absolute time-series trend filter—applied to a diversified suite of commodity sector ETFs (DBA, DBB, DBE, DBP) from 2007 to 2026. We demonstrate that while pure relative momentum exhibits high parameter sensitivity and inconsistent benchmark outperformance, the inclusion of an absolute momentum filter structurally mitigates drawdowns and universally outperforms a static, equally weighted benchmark across all tested parameter combinations. The findings suggest that Dual Momentum provides a robust, parameter-agnostic framework for portfolio managers seeking tactical commodity exposure with superior risk-adjusted return profiles.

Quantitative investors usually start their research by analyzing individual trading strategies. They compare performance, risk, implementation complexity, market exposure, and the economic intuition behind each anomaly. However, once historical equity curves of individual strategies are available, a different research question becomes possible. Instead of asking only which individual strategy looks attractive, we can ask how to allocate capital across a broad universe of strategies.

The Quantpedia API makes this type of analysis practical. It provides access not only to strategy metadata, but also to historical strategy equity curves. Therefore, users can move from strategy discovery to systematic strategy portfolio construction.

Our study explores a weekly-rebalanced dual-momentum-based Global Tactical Asset Allocation (GTAA) strategy applied to a diversified set of ETFs. The strategy selects assets based on relative momentum and applies an absolute momentum filter to avoid declining investments. Ultimately, a single combined strategy was created by merging two sub-strategies, incorporating both shorter- and longer-term momentum signals. Backtesting over an extended period demonstrates that this approach delivers attractive risk-adjusted returns, achieving attractive Sharpe and Calmar ratios, while maintaining lower drawdowns compared to a simple equally weighted benchmark.