A graph-based CNN-LSTM stock price prediction algorithm with leading indicators

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dc.contributor.authorWu, Jimmy MingTaics
dc.contributor.authorLi, Zhongcuics
dc.contributor.authorHerencsár, Norbertcs
dc.contributor.authorVo, Baycs
dc.contributor.authorLin, Jerry Chun-Weics
dc.coverage.issue0cs
dc.coverage.volume28cs
dc.date.issued2021-02-22cs
dc.description.abstractIn today's society, investment wealth management has become a mainstream of the contemporary era. Investment wealth management refers to the use of funds by investors to arrange funds reasonably, for example, savings, bank financial products, bonds, stocks, commodity spots, real estate, gold, art, and many others. Wealth management tools manage and assign families, individuals, enterprises, and institutions to achieve the purpose of increasing and maintaining value to accelerate asset growth. Among them, in investment and financial management, people's favorite product of investment often stocks, because the stock market has great advantages and charm, especially compared with other investment methods. More and more scholars have developed methods of prediction from multiple angles for the stock market. According to the feature of financial time series and the task of price prediction, this article proposes a new framework structure to achieve a more accurate prediction of the stock price, which combines Convolution Neural Network (CNN) and Long-Short-Term Memory Neural Network (LSTM). This new method is aptly named stock sequence array convolutional LSTM (SACLSTM). It constructs a sequence array of historical data and its leading indicators (options and futures), and uses the array as the input image of the CNN framework, and extracts certain feature vectors through the convolutional layer and the layer of pooling, and as the input vector of LSTM, and takes ten stocks in U.S.A and Taiwan as the experimental data. Compared with previous methods, the prediction performance of the proposed algorithm in this article leads to better results when compared directly.en
dc.formattextcs
dc.format.extent1-20cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationMULTIMEDIA SYSTEMS. 2021, vol. 28, issue 0, p. 1-20.en
dc.identifier.doi10.1007/s00530-021-00758-wcs
dc.identifier.issn0942-4962cs
dc.identifier.orcid0000-0002-9504-2275cs
dc.identifier.other172618cs
dc.identifier.researcheridA-6539-2009cs
dc.identifier.scopus23012051100cs
dc.identifier.urihttp://hdl.handle.net/11012/201733
dc.language.isoencs
dc.publisherSpringercs
dc.relation.ispartofMULTIMEDIA SYSTEMScs
dc.relation.urihttps://link.springer.com/article/10.1007/s00530-021-00758-wcs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/0942-4962/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectConvolution neural networken
dc.subjectLong&#8211en
dc.subjectshort-term memory neural networken
dc.subjectStock price predictionen
dc.subjectLeading indicatorsen
dc.titleA graph-based CNN-LSTM stock price prediction algorithm with leading indicatorsen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-172618en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:42:16en
sync.item.modts2025.01.17 16:42:45en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
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