Forex-foreteller: Currency trend modeling using news articles

Fang Jin, Nathan Self, Parang Saraf, Patrick Butler, Wei Wang, Naren Ramakrishnan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

26 Scopus citations

Abstract

Financial markets are quite sensitive to unanticipated news and events. Identifying the effect of news on the market is a challenging task. In this demo, we present Forex-foreteller (FF) which mines news articles and makes forecasts about the movement of foreign currency markets. The system uses a combination of language models, topic clustering, and sentiment analysis to identify relevant news articles. These articles along with the historical stock index and currency ex- change values are used in a linear regression model to make forecasts. The system has an interactive visualizer designed specifically for touch-sensitive devices which depicts fore- casts along with the chronological news events and financial data used for making the forecasts.

Original languageEnglish
Title of host publicationKDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
EditorsRajesh Parekh, Jingrui He, Dhillon S. Inderjit, Paul Bradley, Yehuda Koren, Rayid Ghani, Ted E. Senator, Robert L. Grossman, Ramasamy Uthurusamy
PublisherAssociation for Computing Machinery
Pages1470-1473
Number of pages4
ISBN (Electronic)9781450321747
DOIs
StatePublished - Aug 11 2013
Event19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013 - Chicago, United States
Duration: Aug 11 2013Aug 14 2013

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
VolumePart F128815

Conference

Conference19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013
Country/TerritoryUnited States
CityChicago
Period08/11/1308/14/13

Keywords

  • Currency markets
  • Sentiment analysis
  • Topic discovery

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