{"id":"https://openalex.org/W3170487013","doi":"https://doi.org/10.1145/3447548.3467297","title":"Accurate Multivariate Stock Movement Prediction via Data-Axis Transformer with Multi-Level Contexts","display_name":"Accurate Multivariate Stock Movement Prediction via Data-Axis Transformer with Multi-Level Contexts","publication_year":2021,"publication_date":"2021-08-13","ids":{"openalex":"https://openalex.org/W3170487013","doi":"https://doi.org/10.1145/3447548.3467297","mag":"3170487013"},"language":"en","primary_location":{"id":"doi:10.1145/3447548.3467297","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447548.3467297","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining","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/A5036321139","display_name":"Jaemin Yoo","orcid":"https://orcid.org/0000-0001-7237-5117"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jaemin Yoo","raw_affiliation_strings":["Seoul National University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054525289","display_name":"Yejun Soun","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yejun Soun","raw_affiliation_strings":["Seoul National University &amp; DeepTrade Inc., Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Seoul National University &amp; DeepTrade Inc., Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028859642","display_name":"Yong-chan Park","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yong-chan Park","raw_affiliation_strings":["Seoul National University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074561505","display_name":"U Kang","orcid":"https://orcid.org/0000-0002-8774-6950"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"U Kang","raw_affiliation_strings":["Seoul National University &amp; DeepTrade Inc., Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Seoul National University &amp; DeepTrade Inc., Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139264467"],"apc_list":null,"apc_paid":null,"fwci":14.0024,"has_fulltext":false,"cited_by_count":132,"citation_normalized_percentile":{"value":0.99319771,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2037","last_page":"2045"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9998999834060669,"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":0.9998999834060669,"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9968000054359436,"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"}},{"id":"https://openalex.org/T10047","display_name":"Financial Markets and Investment Strategies","score":0.9955000281333923,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.6096735000610352},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5671033263206482},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.549648642539978},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.5458540916442871},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.48577645421028137},{"id":"https://openalex.org/keywords/competitor-analysis","display_name":"Competitor analysis","score":0.45557963848114014},{"id":"https://openalex.org/keywords/financial-market","display_name":"Financial market","score":0.4390111267566681},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4276922345161438},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3676777184009552},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.30383265018463135},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.1944357454776764},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.1916951835155487},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.14584559202194214},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1289902925491333},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.12391722202301025}],"concepts":[{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.6096735000610352},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5671033263206482},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.549648642539978},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5458540916442871},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.48577645421028137},{"id":"https://openalex.org/C127576917","wikidata":"https://www.wikidata.org/wiki/Q624630","display_name":"Competitor analysis","level":2,"score":0.45557963848114014},{"id":"https://openalex.org/C19244329","wikidata":"https://www.wikidata.org/wiki/Q208697","display_name":"Financial market","level":2,"score":0.4390111267566681},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4276922345161438},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3676777184009552},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30383265018463135},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.1944357454776764},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.1916951835155487},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.14584559202194214},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1289902925491333},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.12391722202301025},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3447548.3467297","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447548.3467297","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:s-space.snu.ac.kr:10371/183724","is_oa":false,"landing_page_url":"https://hdl.handle.net/10371/183724","pdf_url":null,"source":{"id":"https://openalex.org/S4306401345","display_name":"Seoul National University Open Repository (Seoul National University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I139264467","host_organization_name":"Seoul National University","host_organization_lineage":["https://openalex.org/I139264467"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W2094665138","https://openalex.org/W2095705004","https://openalex.org/W2468992649","https://openalex.org/W2470125973","https://openalex.org/W2626778328","https://openalex.org/W2734986640","https://openalex.org/W2744043447","https://openalex.org/W2774559076","https://openalex.org/W2798413829","https://openalex.org/W2883599634","https://openalex.org/W2896457183","https://openalex.org/W2897288200","https://openalex.org/W2903325752","https://openalex.org/W2915043312","https://openalex.org/W2950418200","https://openalex.org/W2952042565","https://openalex.org/W2952277250","https://openalex.org/W2952451326","https://openalex.org/W2964121744","https://openalex.org/W2964308564","https://openalex.org/W2964413206","https://openalex.org/W2964450981","https://openalex.org/W2965446444","https://openalex.org/W2965672544","https://openalex.org/W2966276668","https://openalex.org/W2970631142","https://openalex.org/W2977178908","https://openalex.org/W3028192203","https://openalex.org/W3034478396","https://openalex.org/W3035275162","https://openalex.org/W3035414307","https://openalex.org/W3037932933","https://openalex.org/W3101796500","https://openalex.org/W3104302219","https://openalex.org/W3198316914"],"related_works":["https://openalex.org/W3124118004","https://openalex.org/W2024278573","https://openalex.org/W1505913416","https://openalex.org/W4255873683","https://openalex.org/W1963569934","https://openalex.org/W2031591846","https://openalex.org/W2031104990","https://openalex.org/W1567992464","https://openalex.org/W4380669258","https://openalex.org/W3125980203"],"abstract_inverted_index":{"How":[0],"can":[1],"we":[2,93],"efficiently":[3],"correlate":[4],"multiple":[5,47],"stocks":[6,71,113],"for":[7,104,148],"accurate":[8,68],"stock":[9,105,163],"movement":[10,13,86,106],"prediction?":[11],"Stock":[12],"prediction":[14,39,107],"has":[15,63],"received":[16],"growing":[17],"interest":[18],"in":[19,114],"data":[20],"mining":[21],"and":[22,76,121,142,169,181],"machine":[23],"learning":[24,126,149],"communities":[25],"due":[26],"to":[27,36,42,66,173],"its":[28],"substantial":[29],"impact":[30],"on":[31,137,157,187],"financial":[32],"markets.":[33],"One":[34],"way":[35],"improve":[37],"the":[38,44,55,84,110,154,178,182],"accuracy":[40,156],"is":[41,80],"utilize":[43],"correlations":[45,69,111,123,128],"between":[46,70,112],"stocks,":[48],"getting":[49],"a":[50,88,101,138,145],"reliable":[51],"evidence":[52],"regardless":[53],"of":[54,58,73,87,185],"random":[56],"noises":[57],"individual":[59],"prices.":[60],"However,":[61],"it":[62],"been":[64],"challenging":[65],"acquire":[67],"because":[72],"their":[74],"asymmetric":[75,120],"dynamic":[77,122],"nature":[78],"which":[79],"also":[81],"influenced":[82],"by":[83,124],"global":[85,139],"market.":[89],"In":[90],"this":[91],"work,":[92],"propose":[94],"DTML":[95,118,152],"(Data-axis":[96],"Transformer":[97],"with":[98],"Multi-Level":[99],"contexts),":[100],"novel":[102],"approach":[103],"that":[108],"learns":[109],"an":[115],"end-to-end":[116],"way.":[117],"makes":[119],"a)":[125],"temporal":[127],"within":[129],"each":[130],"stock,":[131],"b)":[132],"generating":[133],"multi-level":[134],"contexts":[135],"based":[136],"market":[140],"context,":[141],"c)":[143],"utilizing":[144],"transformer":[146],"encoder":[147],"inter-stock":[150],"correlations.":[151],"achieves":[153],"state-of-the-art":[155],"six":[158],"datasets":[159],"collected":[160],"from":[161,165],"various":[162],"markets":[164],"US,":[166],"China,":[167],"Japan,":[168],"UK,":[170],"making":[171],"up":[172],"13.8%p":[174],"higher":[175],"profits":[176],"than":[177],"best":[179],"competitors":[180],"annualized":[183],"return":[184],"44.4%":[186],"investment":[188],"simulation.":[189]},"counts_by_year":[{"year":2026,"cited_by_count":11},{"year":2025,"cited_by_count":40},{"year":2024,"cited_by_count":38},{"year":2023,"cited_by_count":29},{"year":2022,"cited_by_count":14}],"updated_date":"2026-07-04T07:58:01.006859","created_date":"2025-10-10T00:00:00"}
