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Menampilkan postingan dari September, 2017

Applying Corrective AI to Daily Seasonal Forex Trading

  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

StockTwits Sentiment Analysis

By Colton Smith === Exploring alternative datasets to augment financial trading models is currently the hot trend among the quantitative community. With so much social media data out there, its place in financial models has become a popular research discussion. Surely the stock market’s performance influences the reactions from the public but if the converse is true, that social media sentiment can be used to predict movements in the stock market, then this would be a very valuable dataset for a variety of financial firms and institutions. When I began this project as a consultant for QTS Capital Management, I did an extensive literature review of the social media sentiment providers and academic research. The main approach is to take the social media firehose, filter it down by source credibility, apply natural language processing (NLP), and create a variety of metrics that capture sentiment, volume, dispersion, etc. The best results have come from using Twitter or StockTwits as the