Using SVM to Predict Stock Price Changes from Online Financial News

被引：0
|
作者：
Dai, Shuyan [1 ]
Li, Ning [2 ]
机构：
[1] Beihua Univ, Dept Finance & Planning, Beihua 132000, Jilin, Peoples R China
[2] China Univ Petr, Sch Econ & Management, Qingdao 266555, Peoples R China
来源：
MECHATRONICS AND APPLIED MECHANICS, PTS 1 AND 2 | 2012年 / 157-158卷
关键词：
information retrieval; stock analysis; SVM classification; financial analysis;
D O I：
10.4028/www.scientific.net/AMM.157-158.1586
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Many technical analysis use financial indices to predict stock price changes. In this paper, we present a different approach for prediction stock price fluctuations using financial news. Our method approaches the stock price prediction problem from an information retrieval perspective. We apply both text analysis and pattern classification techniques to search for important online news that are relevant for stock price changes. First, the online financial news and the corresponding stocks are extracted. Then we apply Support Vector Machine (SVM) to construct a model that predicts the price changes for the stocks. Finally, the stock changes prediction model is used to classify and extract upcoming important financial news. The experimental results demonstrate our method is effective for seeking the important financial news for stock price changes.
引用
收藏
页码：1586 / +
页数：2
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