Variable Selection for Artificial Neural Networks with Applications for Stock Price Prediction
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Kim, Gang-Hoo | ko |
| dc.contributor.author | Kim, Sung-Ho | ko |
| dc.date.accessioned | 2019-02-21T01:27:18Z | - |
| dc.date.available | 2019-02-21T01:27:18Z | - |
| dc.date.created | 2018-12-11 | - |
| dc.date.issued | 2019-01 | - |
| dc.identifier.citation | APPLIED ARTIFICIAL INTELLIGENCE, v.33, no.1, pp.54 - 67 | - |
| dc.identifier.issn | 0883-9514 | - |
| dc.identifier.uri | http://hdl.handle.net/10203/250505 | - |
| dc.description.abstract | 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. | - |
| dc.language | English | - |
| dc.publisher | TAYLOR & FRANCIS INC | - |
| dc.title | Variable Selection for Artificial Neural Networks with Applications for Stock Price Prediction | - |
| dc.type | Article | - |
| dc.identifier.wosid | 000457427500003 | - |
| dc.identifier.scopusid | 2-s2.0-85054848849 | - |
| dc.type.rims | ART | - |
| dc.citation.volume | 33 | - |
| dc.citation.issue | 1 | - |
| dc.citation.beginningpage | 54 | - |
| dc.citation.endingpage | 67 | - |
| dc.citation.publicationname | APPLIED ARTIFICIAL INTELLIGENCE | - |
| dc.identifier.doi | 10.1080/08839514.2018.1525850 | - |
| dc.contributor.localauthor | Kim, Sung-Ho | - |
| dc.description.isOpenAccess | N | - |
| dc.type.journalArticle | Article | - |
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