By Sergei Belov, Ernest Chan, Nahid Jetha, and Akshay Nautiyal ABSTRACT We applied Corrective AI (Chan, 2022) to a trading model that takes advantage of the intraday seasonality of forex returns. Breedon and Ranaldo (2012) observed that foreign currencies depreciate vs. the US dollar during their local working hours and appreciate during the local working hours of the US dollar. We first backtested the results of Breedon and Ranaldo on recent EURUSD data from September 2021 to January 2023 and then applied Corrective AI to this trading strategy to achieve a significant increase in performance. Breedon and Ranaldo (2012) described a trading strategy that shorted EURUSD during European working hours (3 AM ET to 9 AM ET, where ET denotes the local time in New York, accounting for daylight savings) and bought EURUSD during US working hours (11 AM ET to 3 PM ET). The rationale is that large-scale institutional buying of the US dollar takes place during European working hours to pa
By Ernest Chan and Akshay Nautiyal Features are inputs to supervised machine learning (ML) models. In traditional finance, they are typically called “factors”, and they are used in linear regression models to either explain or predict returns. In the former usage, the factors are contemporaneous with the target returns, while in the latter the factors must be from a prior period. There are generally two types of factors: cross-sectional vs time-series. If you are modeling stock returns, cross-sectional factors are variables that are specific to an individual stock, such as its earnings yield, dividend yield, etc. In our previous blog post , we described how we provide 40 such factors to our subscribers for backtesting and live predictions. But as we advocate using ML for risk management and capital allocation purposes (i.e. metalabeling ), not for returns predictions, you may wonder how these factors can help predict the returns of your trading strategy or portfolio. For example, if yo