Commodities

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.

Commodity markets are back in investors’ focus. After years in which equities and growth assets dominated portfolios, the recent rise in geopolitical tensions, inflation uncertainty, supply-chain fragmentation, and renewed resource nationalism has reminded allocators that commodities remain a critical macro asset class. That is why a newly released research paper, An Index of Commodity Futures Returns Since 1871, is particularly timely. Using a hand-collected database covering more than 150 years of U.S. commodity futures history, the authors provide one of the most comprehensive long-term perspectives yet on commodity investing — showing not only that diversified commodity futures historically delivered equity-like risk premia, but also that their return drivers were meaningfully different from stocks, offering valuable diversification across economic regimes.

Commodity markets are in the spotlight. Two factors currently stand out. Firstly, the geopolitical tensions, as ongoing instability in the Middle East continues to create uncertainty in energy markets, particularly on the supply side. Secondly, less discussed are climate conditions as the El Niño–Southern Oscillation (ENSO) is a recurring climate cycle that affects temperature and precipitation patterns globally and has historically influenced agricultural yields and supply dynamics.

Together, these forces create a plausible environment for stronger commodity performance, or at least increased dispersion across individual commodities. Instead of expressing this view through a simple buy-and-hold allocation, we approach the problem as a systematic portfolio construction task.

As mentioned several times, Quantpedia is a big fan of transferring ideas from one asset class to another. This article is another example; we use an idea originally tested on Chinese stocks and apply it to the commodity futures investment universe. The resultant return new asymmetry investment factor in commodities is an interesting trading strategy unrelated to other common factors and has a slightly negative correlation to the equity market and can be therefore used as an excellent diversifier in multi-asset multi-strategy portfolios.

In part 1 of our article, we analyzed tendencies and trends among the Top 10 quantitative strategies of 2021. Thanks to Quantpedia Pro’s screener, we published several interesting insights about them.

In part 2 of our article, we got deeper into the first five specific strategies, which are significantly outperforming the rest in 2021. 

Today, without any further thoughts, let’s proceed to the five single best performing strategies of 2021 as of August 2021.