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.
%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
}