A Fuzzy Model of a European Index Based on Automatically Extracted Content Information

Viorel Milea; Rui Almeida e Santos Nogueira; U Kaymak; Flavius Frasincar

doi:10.1109/CIFER.2011.5953571

A Fuzzy Model of a European Index Based on Automatically Extracted Content Information

Viorel Milea, Rui Almeida e Santos Nogueira, U Kaymak, Flavius Frasincar

Econometrics

Research output: Chapter/Conference proceeding › Conference proceeding › Academic › peer-review

2 Citations (Scopus)

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
Title of host publication	2011 IEEE Symposium on Computational Intelligence for Financial Engineering & Economics (CIFeEr 2011)
Publisher	IEEE Computational Intelligence Society
DOIs	https://doi.org/10.1109/CIFER.2011.5953571
Publication status	Published - 2011

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EUR ESE 32

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10.1109/CIFER.2011.5953571

http://hdl.handle.net/1765/31235

Cite this

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title = "A Fuzzy Model of a European Index Based on Automatically Extracted Content Information",

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.",

author = "Viorel Milea and {Almeida e Santos Nogueira}, Rui and U Kaymak and Flavius Frasincar",

year = "2011",

doi = "10.1109/CIFER.2011.5953571",

language = "English",

booktitle = "2011 IEEE Symposium on Computational Intelligence for Financial Engineering & Economics (CIFeEr 2011)",

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A Fuzzy Model of a European Index Based on Automatically Extracted Content Information. / Milea, Viorel; Almeida e Santos Nogueira, Rui; Kaymak, U et al.
2011 IEEE Symposium on Computational Intelligence for Financial Engineering & Economics (CIFeEr 2011). IEEE Computational Intelligence Society, 2011.

Research output: Chapter/Conference proceeding › Conference proceeding › Academic › peer-review

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AU - Kaymak, U

AU - Frasincar, Flavius

PY - 2011

Y1 - 2011

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AB - 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.

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DO - 10.1109/CIFER.2011.5953571

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BT - 2011 IEEE Symposium on Computational Intelligence for Financial Engineering & Economics (CIFeEr 2011)

PB - IEEE Computational Intelligence Society

ER -

A Fuzzy Model of a European Index Based on Automatically Extracted Content Information

Abstract

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