{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T16:44:05Z","timestamp":1784738645997,"version":"3.55.0"},"reference-count":51,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,7,30]],"date-time":"2020-07-30T00:00:00Z","timestamp":1596067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["EFOP-3.6.1-16-2016-00010"],"award-info":[{"award-number":["EFOP-3.6.1-16-2016-00010"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The prediction of stock groups values has always been attractive and challenging for shareholders due to its inherent dynamics, non-linearity, and complex nature. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals, and basic metals from Tehran stock exchange were chosen for experimental evaluations. Data were collected for the groups based on 10 years of historical records. The value predictions are created for 1, 2, 5, 10, 15, 20, and 30 days in advance. Various machine learning algorithms were utilized for prediction of future values of stock market groups. We employed decision tree, bagging, random forest, adaptive boosting (Adaboost), gradient boosting, and eXtreme gradient boosting (XGBoost), and artificial neural networks (ANN), recurrent neural network (RNN) and long short-term memory (LSTM). Ten technical indicators were selected as the inputs into each of the prediction models. Finally, the results of the predictions were presented for each technique based on four metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. In addition, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting, and XGBoost.<\/jats:p>","DOI":"10.3390\/e22080840","type":"journal-article","created":{"date-parts":[[2020,7,31]],"date-time":"2020-07-31T04:15:31Z","timestamp":1596168931000},"page":"840","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":312,"title":["Deep Learning for Stock Market Prediction"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7154-9445","authenticated-orcid":false,"given":"M.","family":"Nabipour","sequence":"first","affiliation":[{"name":"Faculty of Mechanical Engineering, Tarbiat Modares University, Tehran 14115-143, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P.","family":"Nayyeri","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran 1439956153, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H.","family":"Jabani","sequence":"additional","affiliation":[{"name":"Department of Economics, Payame Noor University, West Tehran Branch, Tehran 1455643183, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4842-0613","authenticated-orcid":false,"given":"A.","family":"Mosavi","sequence":"additional","affiliation":[{"name":"Faculty of Civil Engineering, Technische Universit\u00e4t Dresden, 01069 Dresden, Germany"},{"name":"Department of Informatics, J. Selye University, 94501 Komarno, Slovakia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"E.","family":"Salwana","sequence":"additional","affiliation":[{"name":"Institute of IR4.0, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6605-498X","authenticated-orcid":false,"given":"Shahab","family":"S.","sequence":"additional","affiliation":[{"name":"Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.knosys.2012.05.003","article-title":"Hybridization of evolutionary Levenberg\u2013Marquardt neural networks and data pre-processing for stock market prediction","volume":"35","author":"Asadi","year":"2012","journal-title":"Knowl.-Based Syst."},{"key":"ref_2","first-page":"35","article-title":"Capital markets efficiency: Evidence from the emerging capital market with particular reference to Dhaka stock exchange","volume":"12","author":"Akhter","year":"2005","journal-title":"South Asian J. 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