Quantpedia in March 2026

Hello all,

What have we accomplished in the last month?

– Launch of Quantpedia’s API
– Invitation to Uncorrelated Puerto Rico conference
– Quantpedia Awards 2026 reminder
– 11 new Quantpedia Premium strategies
– 7 new related research papers
– 7 new backtests
– and finally, 7 new posts on our Quantpedia blog

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Quantpedia Premium Post – April 7th

Five new strategies have been added. Three new related research paper have been included into existing strategy reviews and three new short free blog posts have been published during last few weeks. Plus, four trading strategies have been backtested in QuantConnect in the previous two weeks.

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Systematic Tactical Allocation in Emerging Markets vs. U.S.: A Momentum-Based Approach

The global investment environment is going through a period of meaningful structural change. The dominance of the U.S. dollar is increasingly being questioned, geopolitical tensions are rising, and macroeconomic uncertainty remains elevated. Together, these forces challenge the post-Global Financial Crisis environment in which U.S. equities consistently outperformed most international markets. As a result, investors may be approaching a turning point where relative returns between U.S. equities and international markets—especially Emerging Markets (EM)—begin to shift.

This research focuses on a practical portfolio allocation question: when should investors increase or reduce exposure to Emerging Market equities relative to U.S. equities? Building on our earlier work analyzing the EAFE-USA spread, we extend the framework to Emerging Markets. Our hypothesis is that the relative performance between U.S. and EM equities is not random. Instead, it shows patterns driven by momentum and broader market trends. These patterns likely reflect persistent capital flows and the gradual way macroeconomic information spreads across global markets.

Rather than relying on static asset allocation approaches, we develop a dynamic allocation model that uses momentum and trend signals to generate practical timing signals between U.S. and EM equities. Emerging Markets are particularly interesting in this context because they tend to experience stronger regime shifts and larger performance cycles than developed international markets.

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One Year Later: Is ChatGPT Finally Worth Using for Quantitative Analysis?

One year ago, in our article “Can We Finally Use ChatGPT as a Quantitative Analyst?”, we explored the feasibility of leveraging ChatGPT for quantitative analysis. Since then, a lot has changed: newer models are now available (from OpenAI and also other vendors), and the ecosystem around AI-assisted analysis has evolved significantly. Back then, we encountered numerous challenges, ranging from model hallucinations and faulty code generation to excessive overfitting. In this article, we revisit these issues to assess what has improved and what remains unresolved, with the goal of finally answering whether we can use LLMs to assist with quantitative analysis tasks.

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When Crypto Stopped Diversifying: The ETF Regime Shift

Can crypto still help diversify an equity portfolio—or has that edge disappeared? That’s the practical question behind Crypto Contagion. The paper looks at how shocks move between crypto and U.S. equities, and more importantly, how that relationship changed after the launch of crypto ETFs. Instead of relying on simple correlations, the authors use a combination of jump detection (to isolate real stress events) and machine learning techniques to identify actual spillovers. By comparing periods before and after ETFs, they effectively show how the market structure—and with it, the behavior of crypto—has shifted .

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