Using SVM to Predict Stock Price Changes from Online Financial News

Shu Yan Dai; Ning Li

doi:10.4028/www.scientific.net/AMM.157-158.1586

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 157-158Using SVM to Predict Stock Price Changes from...

Using SVM to Predict Stock Price Changes from Online Financial News

Abstract:

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.

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Periodical:

Applied Mechanics and Materials (Volumes 157-158)

Pages:

1586-1590

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.157-158.1586

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Online since:

February 2012

Authors:

Shu Yan Dai, Ning Li

Keywords:

Financial Analysis, Information Retrieval, Stock Analysis, SVM Classiﬁcation

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