{"id":"https://openalex.org/W4406458806","doi":"https://doi.org/10.1109/bigdata62323.2024.10825870","title":"Stock Movement Prediction Using Mamba And Ensemble Learning","display_name":"Stock Movement Prediction Using Mamba And Ensemble Learning","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406458806","doi":"https://doi.org/10.1109/bigdata62323.2024.10825870"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825870","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825870","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5115904568","display_name":"Halil \u0130brahim Akg\u00fcn","orcid":null},"institutions":[{"id":"https://openalex.org/I13236232","display_name":"TOBB University of Economics and Technology","ror":"https://ror.org/03ewx7v96","country_code":"TR","type":"education","lineage":["https://openalex.org/I13236232"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Halil \u0130brahim Akg\u00fcn","raw_affiliation_strings":["TOBB University,Department of Computer Engineering,Ankara,Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TOBB University,Department of Computer Engineering,Ankara,Turkey","institution_ids":["https://openalex.org/I13236232"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048947308","display_name":"Ahmet Murat \u00d6zbayo\u011flu","orcid":"https://orcid.org/0000-0001-7998-5735"},"institutions":[{"id":"https://openalex.org/I13236232","display_name":"TOBB University of Economics and Technology","ror":"https://ror.org/03ewx7v96","country_code":"TR","type":"education","lineage":["https://openalex.org/I13236232"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Ahmet Murat \u00d6zbayo\u011flu","raw_affiliation_strings":["TOBB University,Department of Computer Engineering,Ankara,Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TOBB University,Department of Computer Engineering,Ankara,Turkey","institution_ids":["https://openalex.org/I13236232"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I13236232"],"apc_list":null,"apc_paid":null,"fwci":0.6781,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.71481393,"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":"6894","last_page":"6903"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9973000288009644,"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":0.9973000288009644,"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/T14319","display_name":"Currency Recognition and Detection","score":0.9588000178337097,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.955299973487854,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6217789053916931},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.5266387462615967},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4429859220981598},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.4130079746246338},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3852802515029907},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10922306776046753}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6217789053916931},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.5266387462615967},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4429859220981598},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.4130079746246338},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3852802515029907},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10922306776046753},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825870","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825870","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1815076433","https://openalex.org/W2105934661","https://openalex.org/W2112796928","https://openalex.org/W2119821739","https://openalex.org/W2133564696","https://openalex.org/W2800569739","https://openalex.org/W3007066689","https://openalex.org/W3022746105","https://openalex.org/W3064840847","https://openalex.org/W3209374680","https://openalex.org/W4290802752","https://openalex.org/W4313442864","https://openalex.org/W4385245566","https://openalex.org/W4389326242","https://openalex.org/W6638545294","https://openalex.org/W6739901393","https://openalex.org/W6782420349","https://openalex.org/W6803444062","https://openalex.org/W6841205203","https://openalex.org/W6859298233"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"The":[0,174],"stock":[1,19,89,112,142],"market":[2],"is":[3,107,133],"influenced":[4],"by":[5],"various":[6],"factors":[7],"such":[8],"as":[9,32,100],"national":[10],"policies,":[11],"economic":[12],"conditions,":[13],"and":[14,30,52,124],"global":[15],"events.":[16],"Accurately":[17],"predicting":[18,167],"price":[20,113,172],"changes":[21,86],"has":[22],"long":[23],"been":[24],"a":[25,101,145],"critical":[26],"challenge":[27],"for":[28,46,150],"investors":[29,197],"economists,":[31],"it":[33],"can":[34,41,195],"significantly":[35],"reduce":[36],"investment":[37,43],"risks.":[38,206],"Accurate":[39],"forecasts":[40],"enhance":[42],"strategies,":[44],"allowing":[45],"maximized":[47],"returns.":[48],"However,":[49],"the":[50,84,98,105,111,130,138,168],"volatile":[51],"non-linear":[53],"nature":[54],"of":[55,147,164,170,180,191],"financial":[56],"markets":[57],"makes":[58],"this":[59,77],"task":[60],"particularly":[61],"complex.":[62],"Recently":[63],"state":[64],"space":[65],"models,":[66],"like":[67],"Mamba,":[68],"have":[69],"shown":[70],"promising":[71],"results":[72],"in":[73,87,166],"sequence":[74],"modeling.":[75],"In":[76],"work,":[78],"we":[79,96,140],"apply":[80],"Mamba":[81],"to":[82,108],"predict":[83],"percentage":[85],"daily":[88],"closing":[90],"prices.":[91],"By":[92,118],"forecasting":[93],"these":[94],"changes,":[95],"frame":[97],"problem":[99],"classification":[102],"task,":[103],"where":[104],"goal":[106],"determine":[109],"whether":[110],"will":[114],"increase":[115],"or":[116],"not.":[117],"training":[119],"models":[120],"with":[121,187],"different":[122],"hyperparameters":[123],"combining":[125],"them":[126],"through":[127],"ensemble":[128],"learning,":[129],"prediction":[131],"accuracy":[132],"further":[134],"improved.":[135],"To":[136],"evaluate":[137],"model,":[139],"analyze":[141],"movements":[143],"over":[144,182],"series":[146],"trading":[148],"days":[149,186],"six":[151],"Nasdaq-listed":[152],"companies.":[153],"Our":[154],"model":[155,175],"demonstrates":[156],"notable":[157],"performance,":[158],"achieving":[159],"an":[160,177,188],"average":[161,178],"F1":[162],"score":[163],"60.5%":[165],"direction":[169],"next-day":[171],"movement.":[173],"generated":[176],"profit":[179],"$4150":[181],"120":[183],"test":[184],"transaction":[185],"initial":[189],"capital":[190],"$10,000.":[192],"These":[193],"insights":[194],"help":[196],"make":[198],"more":[199],"informed":[200],"decisions,":[201],"optimizing":[202],"returns":[203],"while":[204],"minimizing":[205]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}