Hello all,

– A new Black-Litterman Portfolio Optimization report
– A ranking phase of the Quantpedia Awards 2025 competition with a $25.000 prize pool is unfolding
– 10 new Quantpedia Premium strategies have been added to our database
– 6 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 9 new backtests written in QuantConnect code
– 5 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

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.

Just little over 24 hours remain until the end of the deadline for QUANTPEDIA AWARDS 2025 – April 30th, 2025, at 23:59 UTC. Join the competition now, and don’t miss out on this chance to showcase your skills! Alternatively, if you can’t (or don’t want) to join, then please help us spread the word and let people in your professional network know!

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.

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.