Cross-Sectional and Dollar Components of Currency Risk Premia

Currency strategies often appear simple on the surface – go long high-yielding currencies, short low-yielding ones, or take a position on the U.S. dollar. But these trades actually mix two distinct components: a Dollar component, which bets on broad movements of the U.S. dollar against all others, and a Cross-Sectional (CS) component, which exploits relative differences across countries. The question is, which of these components really drives currency risk premia? A new paper by Vahid Rostamkhani tackles this long-standing question by decomposing the predictive power of eleven macroeconomic fundamentals—such as interest rates, inflation, unemployment, and fiscal variables—into these two components across almost a century of data (1926-2023). This approach directly tests whether it is more rewarding to time the dollar itself or to focus on cross-country fundamental spreads.

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The Best Strategies for FX Hedging

Foreign exchange (FX) markets are a cornerstone of global finance, offering investors and corporations opportunities to manage currency risk, enhance returns, and optimize portfolio performance. Among the most critical challenges in FX is the design of robust hedging strategies to mitigate exposure to volatile currency movements. How does the financial industry deal with this task? We can draw inspiration from the paper written by Castro, Hamill, Harber, Harvey, and Van Hemert, which explores strategies such as dynamic hedging, trend-following, and momentum-based approaches, the concept of carry, and the interplay of these strategies with fundamental concepts like Purchasing Power Parity (PPP) and valuation metrics.

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How to Identify Ponzi Funds?

Can we spot a Ponzi scheme before it collapses? That question haunts regulators, investors, and journalists alike. But what if some modern investment funds operate on dynamics that, while not technically illegal, closely resemble Ponzi-like behavior? A new paper by Philippe van der Beck, Jean-Philippe Bouchaud, and Dario Villamaina examines whether certain actively managed funds inflate their own performance — and in doing so, unwittingly mislead investors chasing past returns.

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Can We Profit from Disagreements Between Machine Learning and Trend-Following Models?

When using machine learning to forecast global equity returns, it’s tempting to focus on the raw prediction—whether some stock market is expected to go up or down. But our research shows that the real value lies elsewhere. What matters most isn’t the level or direction of the machine learning model’s forecast but how much it differs from a simple, price-based benchmark—such as a naive moving average signal. When that gap is wide, it often reveals hidden mispricings. In other words, it’s not about whether the ML model predicts positive or negative returns but whether its view disagrees sharply with what a basic trend-following model would suggest. Those moments of disagreement offer the most compelling opportunities for tactical country allocation.

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Can We Finally Use ChatGPT as a Quantitative Analyst?

In two of our previous articles, we explored the idea of using artificial intelligence to backtest trading strategies. Since then, AI has continued to develop, with tools like ChatGPT evolving from simple Q&A assistants into more complex tools that may aid in developing and testing investment strategies—at least, according to some of the more optimistic voices in the field. Over a year has passed since our first experiments, and with all the current hype around the usefulness of large language models (LLMs), we believe it’s the right time to critically revisit this topic. Therefore, our goal is to evaluate how well today’s AI models can perform as quasi-junior quantitative analysts—highlighting not only the promising use cases but also the limitations that still remain.

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Revisiting Pragmatic Asset Allocation: Simple Rules for Complex Times

Pragmatic Asset Allocation (PAA) represents a portfolio construction approach that seeks to balance the benefits of systematic trend-following with the realities faced by semi-active investors (mainly taxes and lack of time to manage positions). Approximately a month ago, we ran a test and filtered asset allocation strategies from our Screener and looked for those that performed well on a YTD basis. One of those models that fared surprisingly well was the PAA model, and given the challenging market conditions so far in 2025, with mixed signals across asset classes and increased macroeconomic uncertainty, we believe it is an ideal time to revisit the PAA framework. This analysis may help clarify whether a pragmatic, rules-based approach can still hold its ground—or even outperform—in a year when many models have struggled.

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