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.

The dominance of mega-tech stocks, particularly the “Magnificent 7,” in both U.S. and global equity indexes has a profound impact on factor portfolios. When constructing value-weighted smart beta strategies, these portfolios often end up heavily concentrated in a few individual stocks. This concentration introduces idiosyncratic risk, skewing the risk profiles of factor strategies. While no active strategy can entirely avoid the influence of these high-market-cap stocks, it is critical to limit their exposure to reduce idiosyncratic risk and improve the stability of factor-based approaches.

Market practitioners often rely on experience-based wisdom to navigate currency markets, and one such widely held belief is that low dispersion in global bond yields signals weak future returns for carry trades (and high dispersion implies high future carry returns). While this intuition makes sense—when yield differentials are compressed, the incentive to exploit them diminishes—a recent academic study provides a solid theoretical foundation for this idea. The research not only confirms this observation with rigorous empirical analysis but also explains the underlying financial mechanisms that drive the relationship. By quantifying the effect and presenting clear visualizations, the study transforms an intuitive market rule of thumb into a well-grounded principle backed by data.