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Developing an approach to evaluate stocks by forecasting effective features with data mining methods

Developing an approach to evaluate stocks by forecasting effective features with data mining methods
Developing an approach to evaluate stocks by forecasting effective features with data mining methods
In this research, a novel approach is developed to predict stocks return and risks. In this three stage method, through a comprehensive investigation all possible features which can be effective on stocks risk and return are identified. Then, in the next stage risk and return are predicted by applying data mining techniques for the given features. Finally, we develop a hybrid algorithm, on the basis of filter and function-based clustering; the important features in risk and return prediction are selected then risk and return re-predicted. The results show that the proposed hybrid model is a proper tool for effective feature selection and these features are good indicators for the prediction of risk and return. To illustrate the approach as well as to train data and test, we apply it to Tehran Stock Exchange (TSE) data from 2002 to 2011.
Classification algorithm, Data mining, Feature selection, Function-based clustering method, Stock market
0957-4174
1325-1339
Barak, Sasan
f82186de-f5b7-4224-9621-a00e7501f2c3
Modarres, Mohammad
9257a9b7-d05c-4d8f-81ab-64e23b7099ec
Barak, Sasan
f82186de-f5b7-4224-9621-a00e7501f2c3
Modarres, Mohammad
9257a9b7-d05c-4d8f-81ab-64e23b7099ec

Barak, Sasan and Modarres, Mohammad (2015) Developing an approach to evaluate stocks by forecasting effective features with data mining methods. Expert Systems with Applications, 42 (3), 1325-1339. (doi:10.1016/j.eswa.2014.09.026).

Record type: Article

Abstract

In this research, a novel approach is developed to predict stocks return and risks. In this three stage method, through a comprehensive investigation all possible features which can be effective on stocks risk and return are identified. Then, in the next stage risk and return are predicted by applying data mining techniques for the given features. Finally, we develop a hybrid algorithm, on the basis of filter and function-based clustering; the important features in risk and return prediction are selected then risk and return re-predicted. The results show that the proposed hybrid model is a proper tool for effective feature selection and these features are good indicators for the prediction of risk and return. To illustrate the approach as well as to train data and test, we apply it to Tehran Stock Exchange (TSE) data from 2002 to 2011.

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More information

e-pub ahead of print date: 23 September 2014
Published date: 15 February 2015
Keywords: Classification algorithm, Data mining, Feature selection, Function-based clustering method, Stock market

Identifiers

Local EPrints ID: 434859
URI: http://eprints.soton.ac.uk/id/eprint/434859
ISSN: 0957-4174
PURE UUID: 6683e539-b1fa-4ee6-b411-fbc3ddf8b267
ORCID for Sasan Barak: ORCID iD orcid.org/0000-0001-7715-9958

Catalogue record

Date deposited: 11 Oct 2019 16:30
Last modified: 10 Dec 2019 01:20

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