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A multicriteria optimization approach for the stock market feature selection
Computer Science and Information Systems, Tome 18 (2021) no. 3 Cet article a éte moissonné depuis la source Computer Science and Information Systems website

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This paper studies the informativeness of features extracted from a limit order book data, to classify market data vector into the label (buy/idle) by using the Long short-term memory (LSTM) network. New technical indicators based on the support/resistance zones are introduced to enrich the set of features. We evaluate whether the performance of the LSTM network model is improved when we select features with respect to the newly proposed methods. Moreover, we employ multicriteria optimization to perform adequate feature selection among the proposed approaches, with respect to precision, recall, and F β score. Seven variations of approaches to select features are proposed and the best is selected by incorporation of multicriteria optimization.
Keywords: Limit order book, multicriteria optimization, time-series, feature selection, machine learning 
@article{CSIS_2021_18_3_a7,
     author = {Dragana Radoji\v{c}i\'c and Nina Radoji\v{c}i\'c and Simeon Kredatus},
     title = {A multicriteria optimization approach for the stock market feature selection},
     journal = {Computer Science and Information Systems},
     year = {2021},
     volume = {18},
     number = {3},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2021_18_3_a7/}
}
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AU  - Nina Radojičić
AU  - Simeon Kredatus
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VL  - 18
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%A Nina Radojičić
%A Simeon Kredatus
%T A multicriteria optimization approach for the stock market feature selection
%J Computer Science and Information Systems
%D 2021
%V 18
%N 3
%U http://geodesic.mathdoc.fr/item/CSIS_2021_18_3_a7/
%F CSIS_2021_18_3_a7
Dragana Radojičić; Nina Radojičić; Simeon Kredatus. A multicriteria optimization approach for the stock market feature selection. Computer Science and Information Systems, Tome 18 (2021) no. 3. http://geodesic.mathdoc.fr/item/CSIS_2021_18_3_a7/