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.

With financial markets increasingly whipsawed by geopolitical tensions and unpredictable policy shifts from the Trump administration—investors are once again questioning how to understand risk, fear, and the true drivers of returns. A recent and compelling paper dives into this debate with a provocative thesis: in “Fear, Not Risk, Explains Asset Pricing,” authors Rob Arnott and Edward McQuarrie argue that traditional models built on quantifiable risk have failed to explain real-world returns, and that fear—messy, emotional, and deeply human—is the missing piece.

In recent months, financial markets have experienced heightened volatility as Donald Trump, in his second term as President of the United States, increasingly uses executive orders to steer economic policy. While he also made use of this presidential power during his first term (2017–2021), the volume and impact of executive actions have notably intensified. In this analysis, we explore how markets reacted to Trump’s executive orders in his first presidency compared to the current term, aiming to uncover patterns and draw meaningful conclusions from both periods.

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– A new front-end for Quantpedia Screener
– Integrated Portfolio Manager and Portfolio Analysis reporting
– A reminder for Quantpedia Awards 2025 competition with a $25.000 prize pool
– 3rd episode of the QuantBeats series
– 10 new Quantpedia Premium strategies have been added to our database
– 9 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 6 new backtests written in QuantConnect code
– 6 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

The recent imposition of sweeping global tariffs by President Donald Trump has triggered a sharp and sudden selloff across global equity markets. In times like these, it’s natural for panic to set in. However, as quantitative investors, our strength lies in data-driven decision-making, risk management, and maintaining discipline when others lose theirs.

Rather than reacting emotionally, the prudent course of action is to reassess the robustness of our portfolios. Are we diversified across uncorrelated strategies? Do we have components in place that act as hedges during market crises? Fortunately, the tools provided by Quantpedia can help investors, traders, and portfolio managers identify, test, and deploy crisis-resilient strategies in a structured and evidence-based manner.

Understanding seasonality in financial markets requires recognizing how predictable return patterns can be influenced by investor behavior. One underexplored aspect of this is the impact of front-running—where traders anticipate seasonal trends and act early, shifting returns forward in time. We have already explored seasonality front-running in commodities, stock sectors, and crisis hedge portfolios. Our new research examines whether this phenomenon extends to country ETFs, an asset class where seasonality has been less studied. By applying a front-running strategy to a dataset of country ETFs, we identify opportunities to capitalize on seasonal effects before they fully materialize. Our findings indicate that pre-seasonality drift is strongest in commodities but remains present in country ETFs, offering a potential edge in portfolio construction. Ultimately, our study highlights how front-running seasonality can enhance ETF investing, providing an additional layer of market timing beyond traditional trend-following approaches.