Reversal

One year ago, in our article “Can We Finally Use ChatGPT as a Quantitative Analyst?”, we explored the feasibility of leveraging ChatGPT for quantitative analysis. Since then, a lot has changed: newer models are now available (from OpenAI and also other vendors), and the ecosystem around AI-assisted analysis has evolved significantly. Back then, we encountered numerous challenges, ranging from model hallucinations and faulty code generation to excessive overfitting. In this article, we revisit these issues to assess what has improved and what remains unresolved, with the goal of finally answering whether we can use LLMs to assist with quantitative analysis tasks.

Alternative ETFs sit at an unusual intersection of public-market accessibility and hedge-fund-style investment techniques. They package managed futures, merger arbitrage, and option-based income strategies into exchange-traded products, yet they remain thinly traded and relatively niche compared to mainstream equity or bond ETFs. This combination makes them intriguing: they offer exposure to alternative risk premia, and their limited liquidity raises possibilities to build short-term reversal strategies. 

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.

In this study, we explore the application of simple spread trading strategies using Bitcoin ETFs and cryptocurrency infrastructure ETFs—two highly correlated asset classes due to the broader influence of cryptocurrency market movements. Given their strong relationship, this setup provides a compelling case for implementing pair trading strategies based on mean reversion principles. Building on our previous work, How to Build Mean Reversion Strategies in Currencies, we adapt and extend these models to the cryptocurrency ETF space, demonstrating their broader applicability beyond traditional currency markets. Specifically, we test two sub-methods of mean reversion: linear and exponential. Our goal is to offer a clear and practical example of how traders can leverage these techniques across different asset classes.

For decades, investors and analysts have relied on traditional industry classifications like GICS, NAICS, or SIC to group companies into sectors and peer groups. However, these rigid categorizations often fail to capture the evolving nature of businesses, especially in an era of technological convergence and rapid industry shifts. Machine learning (ML) offers a more dynamic and data-driven alternative by analyzing company visuals—such as logos, product images, and branding elements—to identify similarities that go beyond predefined classifications. A recent study applies this approach to construct new industry groupings and tests them in industry momentum and reversal. The results show that ML-generated groups lead to superior performance, once again highlighting the potential of image-based classification in financial analysis.

The year 2024 is nearly behind us, so it’s an excellent time for a short recapitulation. In the previous 12 months, we have been busy again (as usual) and have published over 70 short analyses of academic papers and our own research articles. The end of the year is a good opportunity to summarize 10 of them, which were the most popular (based on the Google Analytics ranking). The top 10 is diverse, as usual; once again, we hope that you may find something you have not read yet …