%0 Conference Paper %1 Jin:2013:FCT:2487575.2487710 %A Jin, Fang %A Self, Nathan %A <b>Saraf, Parang</b> %A Butler, Patrick %A Wang, Wei %A Ramakrishnan, Naren %B Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining %C New York, NY, USA %D 2013 %I ACM %K currency discovery markets myown sentiment %P 1470--1473 %R 10.1145/2487575.2487710 %T Forex-foreteller: Currency Trend Modeling Using News Articles %U http://doi.acm.org/10.1145/2487575.2487710 %X 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 us- es a combination of language models, topic clustering, and sentiment analysis to identify relevant news articles. These articles along with the historical stock index and curren- cy exchange values are used in a linear regression model to make forecasts. The system has an interactive visualizer de- signed specifically for touch-sensitive devices which depicts forecasts along with the chronological news events and fi- nancial data used for making the forecasts. %@ 978-1-4503-2174-7

@inproceedings{Jin:2013:FCT:2487575.2487710, 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 us- es a combination of language models, topic clustering, and sentiment analysis to identify relevant news articles. These articles along with the historical stock index and curren- cy exchange values are used in a linear regression model to make forecasts. The system has an interactive visualizer de- signed specifically for touch-sensitive devices which depicts forecasts along with the chronological news events and fi- nancial data used for making the forecasts.}, acmid = {2487710}, added-at = {2014-10-23T22:45:00.000+0200}, address = {New York, NY, USA}, author = {Jin, Fang and Self, Nathan and <b>Saraf, Parang</b> and Butler, Patrick and Wang, Wei and Ramakrishnan, Naren}, biburl = {https://www.bibsonomy.org/bibtex/2364f3c28ebb4ae0cfcf8a3efe556368c/parangsaraf}, booktitle = {Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, doi = {10.1145/2487575.2487710}, interhash = {46ee996809c765cc4dfb1ce4fe79264f}, intrahash = {364f3c28ebb4ae0cfcf8a3efe556368c}, isbn = {978-1-4503-2174-7}, keywords = {currency discovery markets myown sentiment}, location = {Chicago, Illinois, USA}, numpages = {4}, pages = {1470--1473}, publisher = {ACM}, series = {KDD '13}, timestamp = {2014-10-23T23:18:52.000+0200}, title = {Forex-foreteller: Currency Trend Modeling Using News Articles}, url = {http://doi.acm.org/10.1145/2487575.2487710}, year = 2013 }