Stock prediction: an event-driven approach based on bursty keywords

Wu, Di, Fung, Gabriel Pui Cheong, Yu, Jeffrey Xu and Pan, Qi (2009) Stock prediction: an event-driven approach based on bursty keywords. Frontiers of Computer Science in China, 3 2: 145-157. doi:10.1007/s11704-009-0029-z

Author Wu, Di
Fung, Gabriel Pui Cheong
Yu, Jeffrey Xu
Pan, Qi
Title Stock prediction: an event-driven approach based on bursty keywords
Journal name Frontiers of Computer Science in China   Check publisher's open access policy
ISSN 1673-7350
Publication date 2009-06-01
Sub-type Article (original research)
DOI 10.1007/s11704-009-0029-z
Open Access Status Not Open Access
Volume 3
Issue 2
Start page 145
End page 157
Total pages 13
Place of publication Beijing, China
Publisher Gaodeng Jiaoyu Chubanshe
Language eng
Subject 1700 Computer Science
2614 Theoretical Computer Science
Formatted abstract
There are many real applications existing where the decision making process depends on a model that is built by collecting information from different data sources. Let us take the stock market as an example. The decision making process depends on a model which that is influenced by factors such as stock prices, exchange volumes, market indices (e.g. Dow Jones Index), news articles, and government announcements (e.g., the increase of stamp duty). Yet Nevertheless, modeling the stock market is a challenging task because (1) the process related to market states (rise state/drop state) is a stochastic process, which is hard to capture using the deterministic approach, and (2) the market state is invisible but will be influenced by the visible market information, like stock prices and news articles. In this paper, we propose an approach to model the stock market process by using a Non-homogeneous Hidden Markov Model (NHMM). It takes both stock prices and news articles into consideration when it is being computed. A unique feature of our approach is event driven. We identify associated events for a specific stock using a set of bursty features (keywords), which has a significant impact on the stock price changes when building the NHMM. We apply the model to predict the trend of future stock prices and the encouraging results indicate our proposed approach is practically sound and highly effective.
Keyword Event-driven
Hidden Markov model
Trend prediction
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 5 times in Thomson Reuters Web of Science Article | Citations
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