NDLI: Evolving hypernetwork models of binary time series for forecasting price movements on stock markets

Content Provider	IEEE Xplore Digital Library
Author	Bautu, E. Sun Kim Bautu, A. Luchian, H. Byoung-Tak Zhang
Copyright Year	2009
Description	Author affiliation: School of Computer Science and Engineering, Seoul National University, 151-744, Korea (Sun Kim; Byoung-Tak Zhang) \|\| Faculty of Computer Science, Al. I Cuza University, Ia¿i 700483, România (Bautu, E.; Bautu, A.; Luchian, H.)
Abstract	The paper proposes a hypernetwork-based method for stock market prediction through a binary time series problem. Hypernetworks are a random hypergraph structure of higher-order probabilistic relations of data. The problem we tackle concerns the prediction of price movements (up/down) on stock markets. Compared to previous approaches, the proposed method discovers a large population of variable subpatterns, i.e. local and global patterns, using a novel evolutionary hypernetwork. An output is obtained from combining these patterns. In the paper, we describe two methods for assessing the prediction quality of the hypernetwork approach. Applied to the Dow Jones Industrial Average Index and the Korea Composite Stock Price Index data, the experimental results show that the proposed method effectively learns and predicts the time series information. In particular, the hypernetwork approach outperforms other machine learning methods such as support vector machines, naive Bayes, multilayer perceptrons, and k-nearest neighbors.
Starting Page	166
Ending Page	173
File Size	335563
Page Count	8
File Format	PDF
ISBN	9781424429585
DOI	10.1109/CEC.2009.4982944
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-18
Publisher Place	Norway
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Economic forecasting Stock markets Sun Learning systems Support vector machines Multilayer perceptrons Computer science Encoding Meteorology
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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