Abstract
In this paper we build on previous work related to predicting the MSCI EURO index based on content analysis of ECB statements. Our focus is on reducing the number of features employed for prediction through feature selection. For this purpose we rely on two methodologies: (stepwise) linear regression and greedy forward feature subset selection. The original dataset consists of 13 features (General Inquirer content categories). Both methodologies provide an improvement in the overall accuracy of the model, while reducing the number of features employed. Through linear regression we achieve an accuracy of 67.58% on the testing set by relying on six features, while greedy forward selection enables an accuracy on the test set of 69.50% while relying on eight features.
Original language | English |
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Title of host publication | Proceedings of the 2011 IEEE Symposium on Computational Intelligence for Financial Engineering & Economics (CIFEr 2011), April 11-15, 2011, Paris, France |
Place of Publication | Piscataway, USA |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 140-147 |
ISBN (Print) | 978-1-4244-9934-2 |
DOIs | |
Publication status | Published - 2011 |
Event | conference; 2011 IEEE Symposium on Computational Intelligence for Financial Engineering & Economics (CIFEr 2011); 2011-04-11; 2011-04-15 - Duration: 11 Apr 2011 → 15 Apr 2011 |
Conference
Conference | conference; 2011 IEEE Symposium on Computational Intelligence for Financial Engineering & Economics (CIFEr 2011); 2011-04-11; 2011-04-15 |
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Period | 11/04/11 → 15/04/11 |
Other | 2011 IEEE Symposium on Computational Intelligence for Financial Engineering & Economics (CIFEr 2011) |