Thematic Boosting of Investment Strategies Using Language AI

This year's RavenPack Research Symposium brought two intense days of knowledge sharing in London and New York, from 25 top experts in natural language processing, quantitative investing, and machine learning. Together, we explored how firms can leverage new language models to generate alpha, better manage risk and respond to calls for more sustainable investment practices.

Language AI has a wide range of applications beyond simply gauging sentiment. The most powerful trading indicators account for context, and help investors better understand the half-life of a sentiment signal. In his presentation on Thematic Boosting of Investment Strategies Using Language AI , Peter Hafez, RavenPack's Chief Data Scientist showed how to leverage the RavenPack Event Taxonomy to create three thematic signals: Earnings Intelligence, ESG Controversy, and Economic Activity.

Watch the full recording here .