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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">PBES</journal-id><journal-title-group><journal-title>Proceedings of Business and Economic Studies</journal-title></journal-title-group><issn>2209-2641</issn><eissn>2209-265X</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/pbes.v4i3.2183</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Quantitative Stock Selection Model Based on Long-Short Term Memory (LSTM) Neural Network</title><url>https://artdesignp.com/journal/PBES/4/3/10.26689/pbes.v4i3.2183</url><author>WuXiao,TangYanqiu</author><pub-date pub-type="publication-year"><year>2021</year></pub-date><volume>4</volume><issue>3</issue><history><date date-type="pub"><published-time>2021-06-18</published-time></date></history><abstract>This article attempted to construct a multi-factor quantitative stock selection model, analyze the financial indicators and transaction data of listed companies in detail via the big data statistical test method, and to find out the alpha excess return relative to the market in the case of short stock index futures as a hedge in the Chinese market.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Chen G, 2015, Quantitative investment analysis. Economic Management Press.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B2" content-type="article"><label>2</label><element-citation publication-type="journal"><p>Ding P, 2016, Quantitative investment: strategy and technology. Publishing House of Electronics Industry.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B3" content-type="article"><label>3</label><element-citation publication-type="journal"><p>Ouyang J, Lu L, 2011, Application of comprehensively improved BP neural network algorithm in stock price prediction. Computer and Digital Engineering, (2): 57-59.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B4" content-type="article"><label>4</label><element-citation publication-type="journal"><p>Chen W, 2018, Comparative study of Shanghai stock exchange index volatility prediction effect based on deep learning. Statistics and Information Forum, 33(5): 99-106.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B5" content-type="article"><label>5</label><element-citation publication-type="journal"><p>Chen K, Zhou Y, Dai F, 2015, A LSTM-based method for stock returns prediction: a case study of China stock market. IEEE International Conference on Big Data, IEEE Press, : 2823-2824.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B6" content-type="article"><label>6</label><element-citation publication-type="journal"><p>Cai L, 2017, Quantitative investment: using Python as a tool. Publishing House of Electronics Industry.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
