{"id":"https://openalex.org/W4318185178","doi":"https://doi.org/10.1109/bigdata55660.2022.10020373","title":"Exploring Multi-Time Context Vector and Randomness for Stock Movement Prediction","display_name":"Exploring Multi-Time Context Vector and Randomness for Stock Movement Prediction","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4318185178","doi":"https://doi.org/10.1109/bigdata55660.2022.10020373"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020373","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020373","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","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/A5051037317","display_name":"Kanghyeon Seo","orcid":"https://orcid.org/0000-0002-0062-7749"},"institutions":[{"id":"https://openalex.org/I148751991","display_name":"Sogang University","ror":"https://ror.org/056tn4839","country_code":"KR","type":"education","lineage":["https://openalex.org/I148751991"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Kanghyeon Seo","raw_affiliation_strings":["Sogang University,Department of Computer Science and Engineering,Seoul,Korea","Department of Computer Science and Engineering, Sogang University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Sogang University,Department of Computer Science and Engineering,Seoul,Korea","institution_ids":["https://openalex.org/I148751991"]},{"raw_affiliation_string":"Department of Computer Science and Engineering, Sogang University, Seoul, Korea","institution_ids":["https://openalex.org/I148751991"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046628656","display_name":"Jihoon Yang","orcid":"https://orcid.org/0000-0002-0300-7705"},"institutions":[{"id":"https://openalex.org/I148751991","display_name":"Sogang University","ror":"https://ror.org/056tn4839","country_code":"KR","type":"education","lineage":["https://openalex.org/I148751991"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jihoon Yang","raw_affiliation_strings":["Sogang University,Department of Computer Science and Engineering,Seoul,Korea","Department of Computer Science and Engineering, Sogang University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Sogang University,Department of Computer Science and Engineering,Seoul,Korea","institution_ids":["https://openalex.org/I148751991"]},{"raw_affiliation_string":"Department of Computer Science and Engineering, Sogang University, Seoul, Korea","institution_ids":["https://openalex.org/I148751991"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5051037317"],"corresponding_institution_ids":["https://openalex.org/I148751991"],"apc_list":null,"apc_paid":null,"fwci":1.2867,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.81199786,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1114","last_page":"1123"},"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/T10047","display_name":"Financial Markets and Investment Strategies","score":0.9973999857902527,"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":0.9969000220298767,"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/randomness","display_name":"Randomness","score":0.8508027195930481},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6696410179138184},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5295461416244507},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.4908791184425354},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4802539646625519},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.43634361028671265},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4311752915382385},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4298107624053955},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.4121350049972534},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3646162748336792},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.36341631412506104},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32131919264793396},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1813056468963623},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1526394784450531},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09609264135360718}],"concepts":[{"id":"https://openalex.org/C125112378","wikidata":"https://www.wikidata.org/wiki/Q176640","display_name":"Randomness","level":2,"score":0.8508027195930481},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6696410179138184},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5295461416244507},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.4908791184425354},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4802539646625519},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.43634361028671265},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4311752915382385},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4298107624053955},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.4121350049972534},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3646162748336792},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.36341631412506104},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32131919264793396},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1813056468963623},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1526394784450531},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09609264135360718},{"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020373","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020373","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W619758126","https://openalex.org/W1491302875","https://openalex.org/W1522301498","https://openalex.org/W1595982529","https://openalex.org/W1966676388","https://openalex.org/W2064675550","https://openalex.org/W2097152068","https://openalex.org/W2157331557","https://openalex.org/W2403106500","https://openalex.org/W2613328025","https://openalex.org/W2734986640","https://openalex.org/W2744043447","https://openalex.org/W2766736793","https://openalex.org/W2784044818","https://openalex.org/W2896457183","https://openalex.org/W2899575547","https://openalex.org/W2900880305","https://openalex.org/W2901852093","https://openalex.org/W2922268266","https://openalex.org/W2964413206","https://openalex.org/W2990393290","https://openalex.org/W3006401393","https://openalex.org/W3010607176","https://openalex.org/W3034478396","https://openalex.org/W3035414307","https://openalex.org/W3037742583","https://openalex.org/W3093690785","https://openalex.org/W3122799874","https://openalex.org/W3152893301","https://openalex.org/W3161070973","https://openalex.org/W3161749414","https://openalex.org/W3170487013","https://openalex.org/W3208624098","https://openalex.org/W4212774754","https://openalex.org/W4214717370","https://openalex.org/W4224220755","https://openalex.org/W4288283362","https://openalex.org/W4313048032","https://openalex.org/W4385245566","https://openalex.org/W6619558275","https://openalex.org/W6631190155","https://openalex.org/W6638667902","https://openalex.org/W6750995883","https://openalex.org/W6755207826","https://openalex.org/W6765602453","https://openalex.org/W6766978945","https://openalex.org/W6780226713"],"related_works":["https://openalex.org/W3034924094","https://openalex.org/W1488708774","https://openalex.org/W3094954546","https://openalex.org/W2981906196","https://openalex.org/W1513698804","https://openalex.org/W4310560702","https://openalex.org/W2029712093","https://openalex.org/W1982811510","https://openalex.org/W4255503743","https://openalex.org/W52943787"],"abstract_inverted_index":{"We":[0,15],"propose":[1],"a":[2,40,66,76,85],"novel":[3],"stock":[4,19,41],"movement":[5],"prediction":[6,109,135],"model":[7,54,106],"that":[8,17],"learns":[9,55],"Multi-Time":[10],"Contexts":[11],"and":[12,30,60,80,100,115],"Randomness":[13],"(MTC-R).":[14],"assume":[16],"the":[18,53,62,72,82,95,98,101,108,116,131,134],"price":[20],"movements":[21],"are":[22],"affected":[23],"by":[24,33],"(1)":[25],"combination":[26],"of":[27,97,113,133],"time-based":[28],"momentums":[29],"(2)":[31],"tradings":[32],"noise":[34],"traders":[35],"which":[36],"cause":[37],"randomness":[38],"in":[39,111,140],"market.":[42],"MTC-R":[43,70],"has":[44],"three":[45],"main":[46],"procedures":[47],"for":[48],"modeling":[49],"our":[50],"hypothesis.":[51],"First,":[52],"time":[56],"representations":[57],"using":[58,65,75,137],"time2vec":[59],"encodes":[61],"multi-time":[63,73],"views":[64],"GRU":[67],"network.":[68],"Second,":[69],"generates":[71],"contexts":[74],"multi-head":[77],"attention":[78],"mechanism":[79],"inserts":[81],"randomness.":[83],"Third,":[84],"loss":[86],"function":[87],"is":[88],"designed":[89],"to":[90,125],"learn":[91],"temporal":[92],"differences":[93],"between":[94],"results":[96,110,136],"second":[99],"current":[102],"time-embedded":[103],"vector.":[104],"Our":[105],"improves":[107],"terms":[112],"accuracy":[114],"Matthews":[117],"correlation":[118],"coefficient":[119],"on":[120],"six":[121],"benchmark":[122],"datasets":[123],"compared":[124],"baseline":[126],"models.":[127],"Furthermore,":[128],"MCT-R":[129],"shows":[130],"effectiveness":[132],"cumulative":[138],"returns":[139],"portfolio":[141],"trading":[142],"simulations.":[143]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}