Abstract

In this study, we use a machine learning framework and draw on an extensive body of media articles on Brexit to provide evidence of cointegration and causality between the sentiments of the media and the movement of British currency. Our contribution to the literature is novel. In addition to applying lexicons commonly used in sentiment analysis, we devise a method using Bayesian learning to create a more context-aware and informative lexicon for Brexit. By leveraging and extending this method, we reveal the relationship between original media sentiment and related economic and financial variables. Our method is a distinct improvement over existing methods and can better predict out-of-sample outcomes than conventional ones.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.