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
Introduction The monthly US nonfarm payroll (NFP) announcement by the United States Bureau of Labor Statistics (BLS) is one of the most closely watched economic indicators, for economists and investors alike. (When I was teaching a class at a well-known proprietary trading firm, the traders suddenly ran out of the classroom to their desks on a Friday morning just before 8:30am EST.) Naturally, there were many efforts in the past trying to predict this number, ranging from using other macroeconomic indicators such as credit spreads to using Twitter sentiment as predictive features. In this article, I will report on research conducted by Radu Ciobanu and I using the unique and proprietary continuous survey data provided by RIWI Corp. to predict this important number. RIWI is an alternative data provider that conducts online surveys and risk measurement monitoring in all countries of the world anonymously, without collecting any personally identifiable information or providing incentive