The Predicting Power of Textual Information on Financial Markets

Abstract-- Mining textual documents and time series concurrently, such as predicting the movements of stock prices based on the contents of the news stories, is an emerging topic in data mining community. Previous researches have shown that there is a strong relationship between the time when the news stories are released and the time when the stock prices fluctuate. In this paper, we propose a systematic framework for predicting the tertiary movements of stock prices by analyzing the impacts of the news stories on the stocks. To be more specific, we investigate the immediate impacts of news stories on the stocks based on the Efficient Markets Hypothesis. Several data mining and text mining techniques are used in a novel way. Extensive experiments using real-life data are conducted, and encouraging results are obtained.

• Type: Journal
• Year: 2005
• Where: CIB
• Authors: Gabriel Pui Cheong Fung, Jeffrey Xu Yu, Hongjun Lu
• Keywords: CIB 2005, Mining, Mining Textual Documents, Stock Prices