Own-research

We’ve previously examined how central bank policy decisions—particularly those by the Federal Reserve and the European Central Bank (ECB)—impact stock market behavior. The price drift in U.S. equities around the Federal Open Market Committee (FOMC) meetings is a well-documented phenomenon. Likewise, our research study of the ECB revealed a pre-announcement drift, underscoring the anticipatory nature of equity markets ahead of key policy events and the potential opportunities for trading strategies. But are such price drifts unique to the Fed and ECB? In this article, we broaden the scope to investigate whether similar market behavior occurs around monetary policy announcements by other major central banks: mainly the Swiss National Bank (SNB), the Bank of England (BoE), and the Bank of Japan (BoJ).

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.

This is the moment we all have been waiting for, and today, we would like to acknowledge the accomplishments of the researchers behind innovative studies in quantitative trading. So, what do the top five look like, and what will the authors of the papers receive?

Let’s find out …

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.

Short-term reversal strategies in U.S. large-cap equity indexes, such as the S&P 500, are well-documented and widely followed. These reversals often occur in response to brief periods of market stress, where sharp declines are followed by quick recoveries (as we have experienced in the last few weeks). Traditional approaches typically identify such stress periods using only the price action of the equity index itself. In this research, however, we explore a broader perspective—one that leverages the behavior of other asset classes, including gold, oil, and intermediate-term U.S. Treasuries. We demonstrate that using signals from these correlated assets to detect stress events can enhance the timing and robustness of reversal trades in equities. Furthermore, we show that combining signals across multiple markets leads to a more effective and diversified reversal strategy.

As the world’s attention shifts from the US-centric equity markets to international equity markets (which strongly outperform on the YTD basis), we could review some interesting anomalies and patterns that exist outside of the United States. In the world of monetary policy, traders have long observed a notable positive drift in U.S. equities on days surrounding Federal Reserve (FOMC) meetings. Interestingly, a similar—but slightly shifted—pattern emerges in European markets around European Central Bank (ECB) press conferences. Our quantitative analysis reveals that European equity markets tend to exhibit a strong and consistent upward drift on the day before the ECB’s scheduled press conference. The reason for this timing difference lies in logistics: since the ECB typically speaks at 14:15 CET (8:15 a.m. EST), well before the major U.S. markets open, investors often front-run the potential market-friendly signals from the central bank. Rather than risk holding positions into the uncertainty of the announcement itself, market participants gradually build up exposure the day before, pricing in expectations of dovish or supportive policy moves.