Integrating multiple data sources for stock prediction

Wu, D., Fung, G. P.C., Yu, J. X. and Liu, Z. (2008). Integrating multiple data sources for stock prediction. In: J. Bailey and et al., Web Information Systems Engineering - WISE 2008. The Ninth International Conference on Web Information Systems Engineering (WISE 2008), Auckland, New Zealand, (77-89). 1-3 September 2008. doi:10.1007/978-3-540-85481-4_8

Author Wu, D.
Fung, G. P.C.
Yu, J. X.
Liu, Z.
Title of paper Integrating multiple data sources for stock prediction
Conference name The Ninth International Conference on Web Information Systems Engineering (WISE 2008)
Conference location Auckland, New Zealand
Conference dates 1-3 September 2008
Proceedings title Web Information Systems Engineering - WISE 2008   Check publisher's open access policy
Place of Publication Berlin, Germany
Publisher Springer-Verlag
Publication Year 2008
Sub-type Fully published paper
DOI 10.1007/978-3-540-85481-4_8
Open Access Status Not yet assessed
ISBN 978-3-540-85480-7
ISSN 0302-9743
Editor J. Bailey
et al.
Volume 5175
Start page 77
End page 89
Total pages 13
Language eng
Abstract/Summary In many real world applications, decisions are usually made by collecting and judging information from multiple different data sources. Let us take the stock market as an example. We never make our decision based on just one single piece of advice, but always rely on a collection of information, such as the stock price movements, exchange volumes, market index, as well as the information from the news articles, expert comments and special announcements (e.g., the increase of stamp duty). Yet, modeling the stock market is difficult because: (1) The process related to market states (up and down) is a stochastic process, which is hard to capture by using the deterministic approach; and (2) The market state is invisible but will be influenced by the visible market information, such as stock prices and news articles. In this paper, we try to model the stock market process by using a Non-homogeneous Hidden Markov Model (NHMM) which takes multiple sources of information into account when making a future prediction. Our model contains three major elements: (1) External event, which denotes the events happening within the stock market (e.g., the drop of US interest rate); (2) Observed market state, which denotes the current market status (e.g. the rise in the stock price); and (3) Hidden market state, which conceptually exists but is invisible to the market participants. Specifically, we model the external events by using the information contained in the news articles, and model the observed market state by using the historical stock prices. Base on these two pieces of observable information and the previous hidden market state, we aim to identify the current hidden market state, so as to predict the immediate market movement. Extensive experiments were conducted to evaluate our work. The encouraging results indicate that our proposed approach is practically sound and effective.
Subjects E1
080109 Pattern Recognition and Data Mining
890399 Information Services not elsewhere classified
Keyword Non-homogeneous Hidden Markov Model (NHMM)
Stock market process
Data sources
Q-Index Code E1
Q-Index Status Confirmed Code
Additional Notes Proceedings published in 'Lecture Notes in Computer Science' Book series.

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Created: Mon, 13 Apr 2009, 23:47:19 EST by Donna Clark on behalf of School of Information Technol and Elec Engineering