文章基本信息

标题：A multicriteria optimization approach for the stock market feature selection
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作者：Radojičić Dragana ; Radojičić Nina ; Kredatus Simeon 等
期刊名称：Computer Science and Information Systems
印刷版ISSN：1820-0214
电子版ISSN：2406-1018
出版年度：2021
卷号：18
期号：3
页码：749-769
DOI：10.2298/CSIS200326044R
语种：English
出版社：ComSIS Consortium
摘要：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 Fβ score. Seven variations of approaches to select features are proposed and the best is selected by incorporation of multicriteria optimization.
关键词：Limit order book;multicriteria optimization;time-series;feature selection;machine learning