{"id":"https://openalex.org/W4396792921","doi":"https://doi.org/10.3233/jcm-237097","title":"Financial market trend prediction model based on LSTM neural network algorithm","display_name":"Financial market trend prediction model based on LSTM neural network algorithm","publication_year":2024,"publication_date":"2024-03-01","ids":{"openalex":"https://openalex.org/W4396792921","doi":"https://doi.org/10.3233/jcm-237097"},"language":"en","primary_location":{"id":"doi:10.3233/jcm-237097","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jcm-237097","pdf_url":null,"source":{"id":"https://openalex.org/S2765058733","display_name":"Journal of Computational Methods in Sciences and Engineering","issn_l":"1472-7978","issn":["1472-7978","1875-8983"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational Methods in Sciences and Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101351419","display_name":"Peilin Dong","orcid":null},"institutions":[{"id":"https://openalex.org/I41208885","display_name":"Harbin University of Commerce","ror":"https://ror.org/03zsxkw25","country_code":"CN","type":"education","lineage":["https://openalex.org/I41208885"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peilin Dong","raw_affiliation_strings":["Harbin University of Commerce","Harbin University of Commerce, Harbin, Heilongjiang, China"],"affiliations":[{"raw_affiliation_string":"Harbin University of Commerce","institution_ids":["https://openalex.org/I41208885"]},{"raw_affiliation_string":"Harbin University of Commerce, Harbin, Heilongjiang, China","institution_ids":["https://openalex.org/I41208885"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100357613","display_name":"Xiaoyu Wang","orcid":"https://orcid.org/0000-0002-6431-8822"},"institutions":[{"id":"https://openalex.org/I41208885","display_name":"Harbin University of Commerce","ror":"https://ror.org/03zsxkw25","country_code":"CN","type":"education","lineage":["https://openalex.org/I41208885"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaoyu Wang","raw_affiliation_strings":["Harbin University of Commerce","Harbin University of Commerce, Harbin, Heilongjiang, China"],"affiliations":[{"raw_affiliation_string":"Harbin University of Commerce","institution_ids":["https://openalex.org/I41208885"]},{"raw_affiliation_string":"Harbin University of Commerce, Harbin, Heilongjiang, China","institution_ids":["https://openalex.org/I41208885"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108928028","display_name":"Zhouhao Shi","orcid":null},"institutions":[{"id":"https://openalex.org/I169572211","display_name":"Northeast Agricultural University","ror":"https://ror.org/0515nd386","country_code":"CN","type":"education","lineage":["https://openalex.org/I169572211"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhouhao Shi","raw_affiliation_strings":["Northeast Agricultural University","Northeast Agricultural University, Harbin, Heilongjiang, China"],"affiliations":[{"raw_affiliation_string":"Northeast Agricultural University","institution_ids":["https://openalex.org/I169572211"]},{"raw_affiliation_string":"Northeast Agricultural University, Harbin, Heilongjiang, China","institution_ids":["https://openalex.org/I169572211"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100357613"],"corresponding_institution_ids":["https://openalex.org/I41208885"],"apc_list":null,"apc_paid":null,"fwci":2.064,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.86683988,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"24","issue":"2","first_page":"745","last_page":"755"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9957000017166138,"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":0.9957000017166138,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9886999726295471,"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"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9235000014305115,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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":0.6577123403549194},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.64400714635849},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.4594171643257141},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.44763362407684326},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38408926129341125},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35426968336105347},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.13286924362182617}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6577123403549194},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.64400714635849},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.4594171643257141},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44763362407684326},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38408926129341125},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35426968336105347},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.13286924362182617}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/jcm-237097","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jcm-237097","pdf_url":null,"source":{"id":"https://openalex.org/S2765058733","display_name":"Journal of Computational Methods in Sciences and Engineering","issn_l":"1472-7978","issn":["1472-7978","1875-8983"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational Methods in Sciences and Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W2652812245","https://openalex.org/W2952572411","https://openalex.org/W2999361040","https://openalex.org/W3019706231","https://openalex.org/W3081651172","https://openalex.org/W3123978597","https://openalex.org/W3132961639","https://openalex.org/W3137365559","https://openalex.org/W3170107550","https://openalex.org/W3179411408","https://openalex.org/W3195437903","https://openalex.org/W3196503391","https://openalex.org/W4205663256","https://openalex.org/W4206501653","https://openalex.org/W4213157882","https://openalex.org/W4225856010","https://openalex.org/W4296550921","https://openalex.org/W4312300168","https://openalex.org/W4316590801","https://openalex.org/W4318827166","https://openalex.org/W4321523394","https://openalex.org/W4321749570","https://openalex.org/W4321792508","https://openalex.org/W4361007018","https://openalex.org/W4366587675","https://openalex.org/W4382175553","https://openalex.org/W4382487938","https://openalex.org/W4382628531","https://openalex.org/W4382724157","https://openalex.org/W6800611529"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"The":[0,116,193],"financial":[1,10,18,22,40,50,136],"market":[2,11,23,51,137,184],"has":[3],"randomness,":[4],"and":[5,65,84,93,241],"the":[6,9,17,26,71,80,98,106,112,121,132,143,153,187,215],"prediction":[7,24,27,53,139,144],"of":[8,135,157,167,231],"is":[12,174,196,230],"an":[13,67],"important":[14],"task":[15],"in":[16,126,186,222,238],"market.":[19,192],"In":[20],"traditional":[21,169],"models,":[25],"results":[28,118],"are":[29],"often":[30],"unsatisfactory.":[31],"So":[32],"it":[33,87],"needs":[34],"to":[35,97,110,197,206,217],"introduce":[36],"new":[37],"models":[38,140],"for":[39,181],"analysis.":[41],"To":[42],"solve":[43],"this":[44,46,103,127,149,227],"problem,":[45],"paper":[47,77,104,128,150],"analyzed":[48,125],"a":[49,223],"trend":[52,138],"model":[54],"based":[55],"on":[56,70],"LSTM":[57,81,113,122,159],"(Long":[58],"Short-Term":[59],"Memory)":[60],"NN":[61,82,114,123,160,170],"(Neural":[62],"Network)":[63],"algorithm,":[64,83],"conducted":[66],"empirical":[68],"analysis":[69],"Shanghai":[72],"stock":[73,183],"index":[74],"dataset.":[75],"This":[76,172],"first":[78],"introduced":[79],"then":[85],"divided":[86],"into":[88],"training":[89],"set,":[90],"test":[91],"set":[92,95],"comparison":[94],"according":[96],"data":[99,107],"characteristics.":[100],"At":[101],"last,":[102],"used":[105],"preprocessing":[108],"method":[109],"verify":[111],"algorithm.":[115,171],"experimental":[117,147],"showed":[119],"that":[120,152,166],"algorithm":[124,161],"can":[129],"effectively":[130],"improve":[131],"generalization":[133],"ability":[134],"while":[141],"ensuring":[142],"accuracy.":[145],"Through":[146],"analysis,":[148],"found":[151],"average":[154],"accuracy":[155],"rate":[156],"using":[158,168],"was":[162],"2.25%":[163],"higher":[164],"than":[165],"research":[173,228],"primarily":[175],"aimed":[176],"at":[177],"developing":[178],"effective":[179],"methods":[180],"predicting":[182],"trends":[185],"continuously":[188],"evolving":[189],"Chinese":[190],"securities":[191],"core":[194],"objective":[195],"empower":[198],"investors":[199],"with":[200],"precise":[201],"guidance":[202],"by":[203],"enabling":[204],"them":[205],"make":[207],"well-informed":[208],"investment":[209],"decisions.":[210],"Achieving":[211],"accurate":[212],"predictions":[213],"holds":[214],"potential":[216],"significantly":[218],"impact":[219],"economic":[220],"operations":[221],"positive":[224],"way.":[225],"Therefore,":[226],"direction":[229],"paramount":[232],"importance,":[233],"offering":[234],"substantial":[235],"value":[236],"both":[237],"academic":[239],"exploration":[240],"practical":[242],"application.":[243]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2025-12-16T23:43:54.943958","created_date":"2025-10-10T00:00:00"}
