文章基本信息

标题：Stock Price Prediction Using Quotes and Financial News
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作者：Manisha V. Pinto ; Kavita Asnani
期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2011
卷号：1
期号：5
页码：266-269
出版社：International Journal of Soft Computing & Engineering
摘要：This paper provides a framework for predicting stock magnitude and trend for making trading decisions by making use of a combination of Data Mining and Text Mining methods. The prediction model predicts the stock market closing price for a given trading day ‘D’, by analysing the information rich unstructured news articles along with the historical stock quotes. In particular, we investigate the immediate impact of the news articles on the time series based on Efficient Market Hypothesis (EMH). Key phrases provide semantic metadata that summarize and characterize documents. This framework incorporates Kea [1], an algorithm for automatically extracting key phrases from news articles. The prediction power of the Neural Network is used for predicting the closing price for a given trading day. The Neural Network is trained on the extracted key phrases and the stock quotes using the Back propagation Algorithm.
关键词：Stock Market; Dow Jones Industrial Average;Key Phrase Extraction Algorithm (KEA); Neural Network;Back Propagation Algorithm