Variable Selection for Artificial Neural Networks with Applications for Stock Price Prediction

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We propose a new Artificial neural network (ANN) method where we select a set of variables as input variables to the ANN. The selection is made so that the input variables may be informative for a target variable as much as possible. The proposed method compared favorably with the existing ANN methods when their performances were evaluated based on 488 stocks in S&P500 in terms of prediction accuracy.
Publisher
TAYLOR & FRANCIS INC
Issue Date
2019-01
Language
English
Article Type
Article
Citation

APPLIED ARTIFICIAL INTELLIGENCE, v.33, no.1, pp.54 - 67

ISSN
0883-9514
DOI
10.1080/08839514.2018.1525850
URI
http://hdl.handle.net/10203/250505
Appears in Collection
MA-Journal Papers(저널논문)
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