What can we realistically expect from investing across different asset classes over the long run? That’s the kind of big-picture question the “Long-Run Asset Returns” paper tackles—offering a sweeping look at how stocks, bonds, real estate, and commodities have performed over the past 200 years. The paper goes beyond just listing historical returns—it explains how reliable (or not) those numbers are by digging into the quirks and issues hidden in very old data. The authors look at what happens to returns when you include countries or time periods that usually get left out, and they show that the past isn’t always as rosy—or as repeatable—as it might seem if you only look at recent decades.

Can machine learning models better predict stock returns if they are tailored to specific industries, or is a one-size-fits-all (generalist) approach sufficient? This question lies at the heart of a recent research paper by Matthias Hanauer, Amar Soebhag, Marc Stam, and Tobias Hoogteijling. Their findings suggest that the optimal solution lies somewhere in between: a “Hybrid” machine learning model that is aware of industry structures but still trained on the full cross-section of stocks offers the best performance.

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.