Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12475
Title: An ICA design of intraday stock prediction models with automatic variable selection
Authors: Mok, PY 
Lam, KP
Ng, HS
Keywords: Independent component analysis
Neural nets
Regression analysis
Stock markets
Issue Date: 2004
Publisher: IEEE
Source: 2004 IEEE International Joint Conference on Neural Networks, 2004 : proceedings : 25-29 July 2004, v. 3, p. 2135-2140 How to cite?
Abstract: Independent component analysis (ICA) provides a mechanism of decomposing non-Gaussian data signals into statistically independent components. In this paper, ICA is used to extract the underlying news factors from intraday stock data. A, prediction algorithm is developed to improve stock index predictions using such extracted "news". Both linear regression model and nonlinear artificial neural network model are proposed to predict stock indexes of Open, Close, High and Low using the ICA extracted "news". These models are compared with models using only raw intraday data as "news". It is demonstrated that ICA helps in extracting market underlying affecting "news", and thus improves the stock prediction accuracy. It shows that the proposed ICA prediction algorithm is a simple to use and versatile algorithm that automatically extracts the most relevant news for different stock index predictions.
URI: http://hdl.handle.net/10397/12475
ISBN: 0-7803-8359-1
ISSN: 1098-7576
DOI: 10.1109/IJCNN.2004.1380947
Appears in Collections:Conference Paper

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