Presentation 2012-02-09
Stock Price Prediction by Combining Stock Price Regression and Web News Text Mining
Hiroyoshi TAKAHASHI, Kazuhiro SEKI, Kuniaki UEHARA,
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Abstract(in English) In this study, we propose a method for stock price prediction using web news articles. There are two types of information available to predict stock prices : numeric information such as stock prices, and textual information such as news articles. Prediction using only numeric information is insufficient because company's news also has some influence on their stock price. We apply a regression analysis for predicting using features extracted from news articles. Evaluative experiments using web news articles as textual information examine whether stock price can be predicted more accurately.
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Keyword(in English) stock price pridiction / support vector regression / web news articles
Paper # PRMU2011-203,SP2011-118
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Conference Date 2012/2/2(1days)
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Language JPN
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Title (in English) Stock Price Prediction by Combining Stock Price Regression and Web News Text Mining
Sub Title (in English)
Keyword(1) stock price pridiction
Keyword(2) support vector regression
Keyword(3) web news articles
1st Author's Name Hiroyoshi TAKAHASHI
1st Author's Affiliation Graduate School of System Informatics, Kobe University()
2nd Author's Name Kazuhiro SEKI
2nd Author's Affiliation Graduate School of System Informatics, Kobe University
3rd Author's Name Kuniaki UEHARA
3rd Author's Affiliation Graduate School of System Informatics, Kobe University
Date 2012-02-09
Paper # PRMU2011-203,SP2011-118
Volume (vol) vol.111
Number (no) 431
Page pp.pp.-
#Pages 6
Date of Issue