Forecasting Intraday stock price trends with text mining techniques

Author

Mittermayer, Marc-André

Author_Institution

Inst. of Inf. Syst., Bern Univ., Switzerland

fYear

2004

fDate

5-8 Jan. 2004

Abstract

In this paper, we describe NewsCATS (news categorization and trading system), a system implemented to predict stock price trends for the time immediately after the publication of press releases. NewsCATS consists mainly of three components. The first component retrieves relevant information from press releases through the application of text preprocessing techniques. The second component sorts the press releases into predefined categories. Finally, appropriate trading strategies are derived by the third component by means of the earlier categorization. The findings indicate that a categorization of press releases is able to provide additional information that can be used to forecast stock price trends, but that an adequate trading strategy is essential for the results of the categorization to be fully exploited.

Keywords

classification; data mining; electronic trading; forecasting theory; information retrieval; stock markets; text analysis; NewsCATS; information retrieval; news categorization and trading system; press release; stock price trend forecasting; text mining; text preprocessing; trading strategy; Board of Directors; Computer science; Computer security; Data mining; Information retrieval; Information security; Information systems; Mathematics; Retirement; Text mining;

fLanguage

English

Publisher

ieee

Conference_Titel

System Sciences, 2004. Proceedings of the 37th Annual Hawaii International Conference on

Print_ISBN

0-7695-2056-1

Type

conf

DOI

10.1109/HICSS.2004.1265201

Filename

1265201