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 John Ryle, CFA === I’ve long been interested in the behavior of corporate insiders and how their actions may impact their company’s stock. I had done some research on this in the past, albeit in a very low-tech way using mostly Excel. It’s a highly compelling subject, intuitively aligned with a company’s equity performance - if those individuals most in-the-know are buying, it seems sensible that the stock should perform well. If insiders are selling, the opposite is implied. While reality proves more complex than that, a tremendous amount of literature has been written on the topic , and it has shown to be predictive in prior studies . In generating my thesis to complete Northwestern’s MS in Predictive Analytics program, I figured employing some of the more prominent machine learning algorithms to insider trading could be an interesting exercise. I was concerned, however, that, as the market had gotten smarter over time, returns from insider trading signals may have decayed as well