{"id":"https://openalex.org/W3134206323","doi":"https://doi.org/10.1108/ijcs-05-2020-0012","title":"A stock price prediction method based on deep learning technology","display_name":"A stock price prediction method based on deep learning technology","publication_year":2021,"publication_date":"2021-03-04","ids":{"openalex":"https://openalex.org/W3134206323","doi":"https://doi.org/10.1108/ijcs-05-2020-0012","mag":"3134206323"},"language":"en","primary_location":{"id":"doi:10.1108/ijcs-05-2020-0012","is_oa":true,"landing_page_url":"https://doi.org/10.1108/ijcs-05-2020-0012","pdf_url":"https://www.emerald.com/insight/content/doi/10.1108/IJCS-05-2020-0012/full/pdf?title=a-stock-price-prediction-method-based-on-deep-learning-technology","source":{"id":"https://openalex.org/S4210199319","display_name":"International Journal of Crowd Science","issn_l":"2398-7294","issn":["2398-7294"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Crowd Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.emerald.com/insight/content/doi/10.1108/IJCS-05-2020-0012/full/pdf?title=a-stock-price-prediction-method-based-on-deep-learning-technology","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5040341611","display_name":"Xuan Ji","orcid":"https://orcid.org/0000-0002-9810-4574"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuan Ji","raw_affiliation_strings":["School of Management and Economics, Beijing Institute of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Management and Economics, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100404531","display_name":"Jiachen Wang","orcid":"https://orcid.org/0000-0001-8665-1557"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiachen Wang","raw_affiliation_strings":["School of Management and Economics, Beijing Institute of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Management and Economics, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101635549","display_name":"Zhijun Yan","orcid":"https://orcid.org/0000-0003-1727-1176"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhijun Yan","raw_affiliation_strings":["School of Management and Economics, Beijing Institute of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Management and Economics, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":19.8961,"has_fulltext":true,"cited_by_count":169,"citation_normalized_percentile":{"value":0.9969175,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"5","issue":"1","first_page":"55","last_page":"72"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10047","display_name":"Financial Markets and Investment Strategies","score":0.9887999892234802,"subfield":{"id":"https://openalex.org/subfields/2003","display_name":"Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9819999933242798,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6760737895965576},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.6723472476005554},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.5258960127830505},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.5047553777694702},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35649001598358154},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34511756896972656},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.18306490778923035},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11038756370544434}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6760737895965576},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.6723472476005554},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.5258960127830505},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5047553777694702},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35649001598358154},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34511756896972656},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.18306490778923035},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11038756370544434},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1108/ijcs-05-2020-0012","is_oa":true,"landing_page_url":"https://doi.org/10.1108/ijcs-05-2020-0012","pdf_url":"https://www.emerald.com/insight/content/doi/10.1108/IJCS-05-2020-0012/full/pdf?title=a-stock-price-prediction-method-based-on-deep-learning-technology","source":{"id":"https://openalex.org/S4210199319","display_name":"International Journal of Crowd Science","issn_l":"2398-7294","issn":["2398-7294"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Crowd Science","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:c108e0f438be4136a71e0b6c2fdaf475","is_oa":true,"landing_page_url":"https://doaj.org/article/c108e0f438be4136a71e0b6c2fdaf475","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"International Journal of Crowd Science, Vol 5, Iss 1, Pp 55-72 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1108/ijcs-05-2020-0012","is_oa":true,"landing_page_url":"https://doi.org/10.1108/ijcs-05-2020-0012","pdf_url":"https://www.emerald.com/insight/content/doi/10.1108/IJCS-05-2020-0012/full/pdf?title=a-stock-price-prediction-method-based-on-deep-learning-technology","source":{"id":"https://openalex.org/S4210199319","display_name":"International Journal of Crowd Science","issn_l":"2398-7294","issn":["2398-7294"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Crowd Science","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2658593801","display_name":"\u5728\u7ebf\u533b\u7597\u670d\u52a1\u4e2d\u533b\u751f\u4e2a\u4f53\u884c\u4e3a\u548c\u56e2\u961f\u7279\u5f81\u5bf9\u7ee9\u6548\u7684\u5f71\u54cd\u673a\u5236\u7814\u7a76","funder_award_id":"71872013","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4867235609","display_name":"\u79fb\u52a8\u5065\u5eb7APP\u7684\u76d1\u7763\u673a\u5236\u5bf9\u516c\u4f17\u5065\u5eb7\u884c\u4e3a\u7684\u5f71\u54cd\u6a21\u5f0f\u7814\u7a76","funder_award_id":"72072011","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5876774959","display_name":null,"funder_award_id":"18JDGLB040","funder_id":"https://openalex.org/F4320334112","funder_display_name":"Beijing Municipal Social Science Foundation"},{"id":"https://openalex.org/G602779820","display_name":"\u793e\u4ea4\u5a92\u4f53\u5065\u5eb7\u77e5\u8bc6\u53d1\u73b0\u4e0e\u4e2a\u6027\u5316\u8bca\u7597\u65b9\u6cd5\u7814\u7a76","funder_award_id":"71572013","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320334112","display_name":"Beijing Municipal Social Science Foundation","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3134206323.pdf","grobid_xml":"https://content.openalex.org/works/W3134206323.grobid-xml"},"referenced_works_count":45,"referenced_works":["https://openalex.org/W1642072541","https://openalex.org/W1689711448","https://openalex.org/W1982675331","https://openalex.org/W2000868436","https://openalex.org/W2006742092","https://openalex.org/W2011586511","https://openalex.org/W2015174807","https://openalex.org/W2025053102","https://openalex.org/W2029316659","https://openalex.org/W2037449179","https://openalex.org/W2050643653","https://openalex.org/W2059770064","https://openalex.org/W2067057029","https://openalex.org/W2074250525","https://openalex.org/W2094665138","https://openalex.org/W2103849785","https://openalex.org/W2110798204","https://openalex.org/W2122016003","https://openalex.org/W2124553913","https://openalex.org/W2128792405","https://openalex.org/W2128853545","https://openalex.org/W2131744502","https://openalex.org/W2171468534","https://openalex.org/W2177066871","https://openalex.org/W2296438605","https://openalex.org/W2418351164","https://openalex.org/W2566564364","https://openalex.org/W2618694545","https://openalex.org/W2734777338","https://openalex.org/W2734986640","https://openalex.org/W2762466482","https://openalex.org/W2767547957","https://openalex.org/W2865675487","https://openalex.org/W2897098357","https://openalex.org/W2910552573","https://openalex.org/W2912036663","https://openalex.org/W2914753286","https://openalex.org/W2956885731","https://openalex.org/W2963282319","https://openalex.org/W2963514026","https://openalex.org/W2963808864","https://openalex.org/W3123661710","https://openalex.org/W6665367396","https://openalex.org/W6679775712","https://openalex.org/W6697136110"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W1977056376","https://openalex.org/W1990545028","https://openalex.org/W4312622923","https://openalex.org/W2728430307","https://openalex.org/W2107786128","https://openalex.org/W2048368023","https://openalex.org/W2735469505","https://openalex.org/W2484264353","https://openalex.org/W2053241453"],"abstract_inverted_index":{"Purpose":[0],"Stock":[1],"price":[2,76,168,231],"prediction":[3,10,77,93,116,232],"is":[4,169],"a":[5,60,74,91,228],"hot":[6],"topic":[7],"and":[8,17,39,48,54,107,131,152,215,239],"traditional":[9,102,236],"methods":[11],"are":[12,23,245],"usually":[13],"based":[14,95,158,250],"on":[15,96,159,251],"statistical":[16],"econometric":[18],"models.":[19],"However,":[20],"these":[21],"models":[22,208],"difficult":[24],"to":[25,72,122,144,171,189],"deal":[26],"with":[27],"nonstationary":[28],"time":[29,163],"series":[30,164],"data.":[31,86],"With":[32],"the":[33,37,40,115,134,137,146,162,173,191,200],"rapid":[34],"development":[35],"of":[36,43,62,83,114,136,166,212],"internet":[38],"increasing":[41],"popularity":[42],"social":[44,84,108,129,240,248],"media,":[45],"online":[46],"news":[47],"comments":[49],"often":[50],"reflect":[51],"investors\u2019":[52],"emotions":[53],"attitudes":[55],"toward":[56],"stocks,":[57],"which":[58,100,244],"contains":[59],"lot":[61],"important":[63],"information":[64],"for":[65],"predicting":[66],"stock":[67,75,103,153,167,178,192,219,230],"price.":[68,193,220],"This":[69,88,118],"paper":[70],"aims":[71],"develop":[73],"method":[78,94,201],"by":[79,141,177],"taking":[80],"full":[81],"advantage":[82],"media":[85,109,130,241,249],"Design/methodology/approach":[87],"study":[89,119,183,226],"proposes":[90,227],"new":[92,229],"deep":[97,252],"learning":[98,253],"technology,":[99],"integrates":[101],"financial":[104,154,237],"index":[105,155],"variables":[106,151],"text":[110,125,138,149,242],"features":[111,238,243],"as":[112],"inputs":[113],"model.":[117],"uses":[120,184],"Doc2Vec":[121],"build":[123],"long":[124,185],"feature":[126,139,150],"vectors":[127,140],"from":[128,247],"then":[132],"reduce":[133],"dimensions":[135,147],"stacked":[142],"auto-encoder":[143],"balance":[145],"between":[148],"variables.":[156],"Meanwhile,":[157],"wavelet":[160],"transform,":[161],"data":[165],"decomposed":[170],"eliminate":[172],"random":[174],"noise":[175],"caused":[176],"market":[179],"fluctuation.":[180],"Finally,":[181],"this":[182,223,225],"short-term":[186],"memory":[187],"model":[188,233],"predict":[190,218],"Findings":[194],"The":[195],"experiment":[196],"results":[197],"show":[198],"that":[199,234],"performs":[202],"better":[203],"than":[204],"all":[205,210],"three":[206],"benchmark":[207],"in":[209],"kinds":[211],"evaluation":[213],"indicators":[214],"can":[216],"effectively":[217],"Originality/value":[221],"In":[222],"paper,":[224],"incorporates":[235],"derived":[246],"technology.":[254]},"counts_by_year":[{"year":2026,"cited_by_count":14},{"year":2025,"cited_by_count":43},{"year":2024,"cited_by_count":44},{"year":2023,"cited_by_count":41},{"year":2022,"cited_by_count":22},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}