{"id":"https://openalex.org/W4402352703","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651353","title":"TCL: Trend-consistent Contrastive Learning for Stock Movement Prediction","display_name":"TCL: Trend-consistent Contrastive Learning for Stock Movement Prediction","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402352703","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651353"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10651353","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10651353","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-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/A5100646502","display_name":"Li Xia","orcid":"https://orcid.org/0000-0002-3193-7336"},"institutions":[{"id":"https://openalex.org/I186272606","display_name":"Guangdong University of Foreign Studies","ror":"https://ror.org/00fhc9y79","country_code":"CN","type":"education","lineage":["https://openalex.org/I186272606"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xia Li","raw_affiliation_strings":["Guangdong University of Foreign Studies,School of Information Science and Technology,Guangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangdong University of Foreign Studies,School of Information Science and Technology,Guangzhou,China","institution_ids":["https://openalex.org/I186272606"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108982273","display_name":"Rihu Liao","orcid":null},"institutions":[{"id":"https://openalex.org/I186272606","display_name":"Guangdong University of Foreign Studies","ror":"https://ror.org/00fhc9y79","country_code":"CN","type":"education","lineage":["https://openalex.org/I186272606"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rihu Liao","raw_affiliation_strings":["Guangdong University of Foreign Studies,School of Information Science and Technology,Guangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangdong University of Foreign Studies,School of Information Science and Technology,Guangzhou,China","institution_ids":["https://openalex.org/I186272606"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I186272606"],"apc_list":null,"apc_paid":null,"fwci":0.3975,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.65187074,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9901000261306763,"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9861999750137329,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6319738626480103},{"id":"https://openalex.org/keywords/movement","display_name":"Movement (music)","score":0.5635886192321777},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.5150739550590515},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44588765501976013},{"id":"https://openalex.org/keywords/history","display_name":"History","score":0.10150521993637085}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6319738626480103},{"id":"https://openalex.org/C2780226923","wikidata":"https://www.wikidata.org/wiki/Q929848","display_name":"Movement (music)","level":2,"score":0.5635886192321777},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.5150739550590515},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44588765501976013},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.10150521993637085},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C107038049","wikidata":"https://www.wikidata.org/wiki/Q35986","display_name":"Aesthetics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10651353","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10651353","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W108600807","https://openalex.org/W1924770834","https://openalex.org/W2003274998","https://openalex.org/W2006632042","https://openalex.org/W2115839797","https://openalex.org/W2250886571","https://openalex.org/W2536835239","https://openalex.org/W2600306306","https://openalex.org/W2613328025","https://openalex.org/W2734986640","https://openalex.org/W2774559076","https://openalex.org/W2798413829","https://openalex.org/W2893230400","https://openalex.org/W2896457183","https://openalex.org/W2964413206","https://openalex.org/W2969677753","https://openalex.org/W3099645789","https://openalex.org/W3103677861","https://openalex.org/W3116238789","https://openalex.org/W3122913300","https://openalex.org/W3123329971","https://openalex.org/W3124986135","https://openalex.org/W3153427360","https://openalex.org/W3170487013","https://openalex.org/W3172807453","https://openalex.org/W4231546411","https://openalex.org/W4233585739","https://openalex.org/W4285089540","https://openalex.org/W4287812705","https://openalex.org/W4289285046","https://openalex.org/W4303415434","https://openalex.org/W4312776912","https://openalex.org/W4328055046","https://openalex.org/W4385574410","https://openalex.org/W6640212811","https://openalex.org/W6681479411","https://openalex.org/W6755207826","https://openalex.org/W6756384632","https://openalex.org/W6767528804","https://openalex.org/W6776700526","https://openalex.org/W6784723893","https://openalex.org/W6785148927"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"Global":[0],"stock":[1,5,22,33,42,137,186],"dynamics":[2],"deeply":[3],"influence":[4],"price":[6,43],"movements,":[7],"which":[8],"are":[9],"often":[10,58],"reflected":[11],"in":[12],"the":[13,17,73,91,127,133,136,139,157,170,195,199,202],"consistent":[14,146],"trends":[15,147,155],"of":[16,72,132,204],"market":[18,47,75],"index":[19,48,76],"and":[20,30,32,54,60,70,78,119,138,150,169,184,188,198],"individual":[21,167],"prices.":[23],"Nevertheless,":[24],"external":[25],"events":[26],"(e.g.,":[27],"economic":[28],"policies":[29],"geopolitics)":[31],"industry":[34],"features":[35],"could":[36],"make":[37,61,94],"special":[38],"cases":[39],"where":[40],"local":[41],"movements":[44],"deviate":[45],"from":[46],"trends.":[49],"In":[50,106,173],"these":[51,84],"cases,":[52],"investors":[53],"financial":[55],"analysts":[56],"can":[57,87],"identify":[59],"reasonable":[62],"investment":[63,68,85,116],"decisions":[64],"based":[65],"on":[66,194],"their":[67],"experience":[69,117],"knowledge":[71,118],"global":[74,171],"trend":[77],"specific":[79],"stocks.":[80],"We":[81,190],"argue":[82],"that":[83],"insights":[86],"be":[88],"incorporated":[89],"into":[90,126],"model":[92,128,158,177],"to":[93,114,129,161,180],"a":[95,111,121],"more":[96,163,182],"accurate":[97,183],"decision.":[98],"However,":[99],"few":[100],"existing":[101],"works":[102],"investigate":[103],"this":[104,107,174],"issue.":[105],"paper,":[108],"we":[109],"design":[110],"novel":[112],"prompt":[113],"introduce":[115,120],"trend-consistent":[122],"contrastive":[123],"learning":[124],"strategy":[125],"improve":[130],"awareness":[131],"relationship":[134],"between":[135,166],"market.":[140,172],"Specifically,":[141],"by":[142],"pulling":[143],"stocks":[144,152,168],"with":[145,153],"closer":[148],"together":[149],"moving":[151],"inconsistent":[154],"apart,":[156],"is":[159,178],"expected":[160],"learn":[162],"personalized":[164],"relationships":[165],"way,":[175],"our":[176,205],"able":[179],"provide":[181],"comprehensive":[185],"analysis":[187],"prediction.":[189],"conduct":[191],"extensive":[192],"experiments":[193],"public":[196],"dataset,":[197],"results":[200],"demonstrate":[201],"effectiveness":[203],"model.":[206]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}
