High-Frequency News Sentiment and Its Application to Forex Market Prediction

dc.contributor.author Xing, Frank
dc.contributor.author Hoang, Duc-Hong
dc.contributor.author Vo, Dinh-Vinh
dc.date.accessioned 2020-12-24T19:18:16Z
dc.date.available 2020-12-24T19:18:16Z
dc.date.issued 2021-01-05
dc.description.abstract Financial news has been identified as an important alternative information source for modeling market dynamics in recent years. While most of the attention goes to stock markets, the foreign exchange (Forex) market, in contrast, is much less studied. Most of the existing text mining research for the Forex market combine news sentiment with other text features, making the contribution of each factor unclear. To this end, we want to study the role of news sentiment exclusively. In particular, we propose a FinBERT-based model to extract high-frequency news sentiment as a 4-dimensional time series. We examine the efficacy of this news sentiment for Forex market prediction without involving any other semantic feature. Experiments show that our model outperforms alternative sentiment analysis approaches and confirm that news sentiment alone may have predictive power for Forex price movements. The sentiment analysis method seems to have a big potential to improve despite that the current predictive power is still weak. The results deepen our understanding of financial text processing systems.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2021.191
dc.identifier.isbn 978-0-9981331-4-0
dc.identifier.uri http://hdl.handle.net/10125/70803
dc.language.iso English
dc.relation.ispartof Proceedings of the 54th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Machine Learning and Predictive Analytics in Accounting, Finance, and Management
dc.subject financial application
dc.subject foreign exchange
dc.subject predictive analytics
dc.subject sentiment analysis
dc.subject text mining
dc.title High-Frequency News Sentiment and Its Application to Forex Market Prediction
prism.startingpage 1583
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