Rely on extremely robust classification technology that is accurate and consistent in matching a human analyst
Leverage the ACTA news sentiment, which is predictive on price and orthogonal to traditional quant factors
Access archive data that is out of sample from 2005 with no corrections; point-in-time accurate for sentiment with no revisions due to model changes
Data Feed Details
Market sentiment news analysis that covers 110,000+ public companies worldwide. Algorithms trained by buy-side research analysts replicate their own analysis of news items, which yields a more accurate and consistent classification across a deep history of news stories. This product helps quantitative researchers and compliance professionals better understand and explore the impact of news and provide more clarity on trade activity. Data Frequency: Intraday; Update Frequency: Intraday.
Alexandria Technology creates proprietary machine learning algorithms that classify text for entities, events/topics, sentiment, and more. Launched in 2012, Alexandria’s classification systems have been trained by research analysts and portfolio managers, creating an AI analyst that reads text like a human but performs with the speed and consistency of a machine.