Studies using machine learning techniques for return forecasting have shown considerable promise. However, as in empirical asset pricing, researchers face numerous decisions around sampling methods and model estimation. This raises an important question: how do these methodological choices impact the performance of ML-driven trading strategies? Recent research by Vaibhav, Vedprakash, and Varun demonstrates that even small decisions can significantly affect overall performance. It appears that in machine learning, the old adage also holds true: the devil is in the details.

Our article explores a simple mean reversion trading strategy applied to FX futures, focusing on identifying undervalued and overvalued currencies to generate returns. Using FX futures rather than spot rates allows for the inclusion of interest rate differentials, simplifying the analysis. The strategy employs two position-sizing methods—linear and exponential—both rebalanced monthly based on currency deviations from their mean. While the linear method offers stability, its returns are limited. In contrast, the exponential method, despite higher risk and deeper drawdowns, ultimately delivers stronger growth and better overall performance by leveraging the mean reversion tendencies of FX pairs.

Short sellers often have a bad reputation, seen as market disruptors who profit from declining prices. Yet, they play a crucial role in making markets more efficient by identifying overvalued assets and correcting mispricings. A recent study uncovers another surprising aspect of their behavior: rather than just demanding liquidity, the most informed short sellers actually provide it. Using transaction-level data, the research shows that these traders supply liquidity, especially on news days and when trading on known anomalies, challenging the conventional view of short sellers as merely aggressive market participants.

Our research will explore the intriguing phenomenon of the Pre-Holiday effect in commodities, particularly crude oil and gasoline. Historical data reveals a short-term price drift prior to major U.S. holidays, suggesting a trend in these markets. We hypothesize that this anomaly may be driven by increased demand for oil and its derivatives, such as gasoline, as people prepare for travel, often by car, during the holiday season. This seasonal behavior offers unique opportunities for market participants.

In this article, we explore the historical performance of sector momentum strategies and examine how their alpha has diminished over time. By analyzing the underlying causes behind this decline, we identify key factors contributing to the underperformance. Most importantly, we introduce an enhanced approach to sector momentum, demonstrating how this solution significantly improves the performance of an ETF sector momentum strategy, making it once again an effective tool for systematic investors.

Hello all,

What have we accomplished in the last month?

– A new Optimization Report
– 11 new Quantpedia Premium strategies have been added to our database
– 12 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 7 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