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.

For U.S. equities, fixed income, and commodities, reconstructing long-term historical datasets is relatively straightforward, and we have already explored these challenges in several previous studies, including 100 Years of Multi-Asset Trend Following, Extending Historical Daily Bond Data to 100 Years, and Extending Historical Daily Commodities Data to 100 Years. Moreover, the broader methodology of reconstructing missing market histories shares many similarities with the techniques discussed in How to Replicate Any Portfolio. Emerging markets, however, represent a particularly interesting opportunity for historical reconstruction, as reliable long-term data is often unavailable for much of the 20th century despite the growing importance of these markets in modern portfolio construction and asset allocation. In this article, we present the framework we developed to extend emerging market histories in a consistent and economically meaningful way, enabling more robust long-term quantitative research and modelling.

In the high-stakes arena of prediction markets, a counterintuitive pattern emerges: retail traders who correctly pick winners more than half the time still lose money, while automated traders with coin-flip accuracy pocket nine-figure profits. Using 222 million prediction market tradeswith directly observable terminal payoffs, the paper “Who Profits from Prediction? Execution, Not Information” presents a clean answer to why it is so. The authors decompose trader returns into a directional component and an execution component, revealing that the execution component, not the directional component, determines which trader types earn positive returns. 

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.

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– Expansion of Quantpedia’s API
– Introduction of Bookmarks
– Quantpedia Awards 2026 Top 10 papers
– 12 new Quantpedia Premium strategies
– 7 new related research papers
– 8 new backtests
– and finally, 8 new posts on our Quantpedia blog

From the trading desk to the portfolio committee, investors face a familiar question: how should alternative stores of value fit into a diversified portfolio? This research explores that question through a systematic dual-momentum framework comparing Bitcoin and physical gold in a rules-based tactical allocation model. Rather than debating ideology, we focus on practical portfolio construction and risk-adjusted returns. The goal is to examine whether “digital gold” can complement its physical counterpart within a disciplined investment process, and whether the distinct behavior of these assets can be used to build a more effective systematic strategy.