Stock prediction: Integrating text mining approach using real-time news

被引：59

作者：

Fung, GPC ^{[1
]}

Yu, JX ^{[1
]}

Lam, W ^{[1
]}

机构：

[1] Chinese Univ Hong Kong, Dept Syst Engn & Engn Management, Shatin, Hong Kong, Peoples R China

来源：

2003 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR FINANCIAL ENGINEERING, PROCEEDINGS | 2003年

关键词：

multiple time series analysis; automatic news analysis; time series prediction;

D O I：

10.1109/CIFER.2003.1196287

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

Mining textual documents and time series concurrently, such as predicting the movements of stock prices based on news articles, is an emerging topic in data mining society nowadays. Previous researches have already suggested that the relationship between news articles and stock prices do exist. However all of the existing approaches are concerning in mining single time series only. The interrelationships among different stocks are not well-addressed. Mining multiple time series concurrently is not only more informative but also far more challenging. Research in such a direction is lacking. In this paper, we try to explore such an opportunity and propose a systematic framework for mining multiple time series based on Efficient Market Hypothesis.

引用

页码：395 / 402

页数：8

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