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 …

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.

The Internet (and especially the part related to finance, trading, and cryptocurrencies) can be dangerous and full of offers of guaranteed returns, pictures of forever-growing bank accounts, and guys with golden rings swimming in the bathtub filled with cash. The truth is usually less rosy. Lucrative frauds, so-called white color crimes, have always been there, but with new technologies, they can spread faster and hide under a colorful disguise. One of the oldest concepts, from the beginnings of conceptualizing probability and statistics branches of mathematics, is Martingale betting, and this method is very often exploited to lure inexperienced new traders, who are then eaten alive by marketing sharks, selling them seemingly non-losing signals. How? An interesting paper by Carlo Zarattini and Andrew Aziz sheds some some light on these schemes.

We mentioned multiple times that we at Quantpedia love historical analysis that spans over a long period of time as it offers a unique glimpse into the different macro environments and periods of political and economic instabilities. These long-term studies help a lot in risk management, and they also help investors set the right expectations about the range of outcomes in the future. Historical analysis of equity and fixed-income markets is not rare, but currency markets are less explored. Therefore, we are happy to investigate a recent paper by Joseph Chen that analyzes carry, momentum, and value strategies in the currency markets over the 200-year history.

Forex markets lure retail traders into a game of “hunting pips” with high leverage and high turnover scalping strategies, in which small traders often lose more than they can afford. But there are other ways of trading currencies. The smart money knows how to exploit interest rate spreads that this asset class offers by employing the FX Carry Trade strategy. In the past decade, the low interest rates of the most developed countries made the FX Carry strategy less profitable, but as inflation returned, higher interest rates returned in some countries, too, and with them, the interest rate spreads widened. And FX Carry is back, and the question stands: Can we improve this well-known trading style?

Finding high-quality data with a long history can be challenging. We have already examined How To Extend Historical Daily Bond Data To 100 years, How To Extend Daily Commodities Data To 100 years, and How To Build a Multi-Asset Trend-Following Strategy With a 100-year Daily History. Following the theme of our previous articles, we decided to extend historical data of a new factor, the Dollar Factor. This article explains how to combine multiple data sources to create a 100-year daily data history for the Dollar Factor (the value of the United States Dollar relative to its most important trading partners’ currencies), introduces data sources, and explains the methodology.