Using SVM to Predict Stock Price Changes from Online Financial News

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.

关键词： information retrieval stock analysis SVM classification financial analysis

作者: Shuyan Dai Ning Li

作者单位: Department of Finance and Planning, Beihua University, Jilin, China, 132000 School of Economics and Management, China University of Petroleum, Qingdao, China, 266555

会议类型: 国际会议

会议名称: 2011 International Conference on Mechatronics and Applied Mechanics(2011年机电一体化与应用力学国际会议 ICMAM 2011)

会议地点: 香港

会议语种:英文

页码: 1586-1590

在线出版日期: 2011-12-27（万方平台首次上网日期，不代表论文的发表时间）