Asset allocation

When using machine learning to forecast global equity returns, it’s tempting to focus on the raw prediction—whether some stock market is expected to go up or down. But our research shows that the real value lies elsewhere. What matters most isn’t the level or direction of the machine learning model’s forecast but how much it differs from a simple, price-based benchmark—such as a naive moving average signal. When that gap is wide, it often reveals hidden mispricings. In other words, it’s not about whether the ML model predicts positive or negative returns but whether its view disagrees sharply with what a basic trend-following model would suggest. Those moments of disagreement offer the most compelling opportunities for tactical country allocation.

Over the past decade and a half, the US equities have set the hard-to-beat performance benchmark. Nearly all of the other countries, no matter if small or big, emerging or developed, have lagged behind. However, what are the forces behind this outperformance? Why did most of the other markets and even investing styles bow to the US large-cap growth dominance? A new paper written by David Blitz nicely analyses the rise of the behemoth.

Valuation models for equity indexes are essential tools for investors seeking to assess long-term market conditions. Traditional models like the CAPE ratio, introduced by Robert J. Shiller, or the Buffett Indicator often rely on macroeconomic variables such as corporate earnings or GDP. While informative, these models can be complex and dependent on data that may be revised or vary across regions. In this article, we introduce a simpler alternative: a valuation ratio based solely on the inflation-adjusted total return of the index, offering a streamlined and transparent approach to index valuation. Finally, our goal would be to answer the question from the title – Are the indexes fairly priced at the moment?

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 …

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.