{"id":"https://openalex.org/W7225809040","doi":"https://doi.org/10.5281/zenodo.24130552","title":"Adaptive Performance-Weighted Ensemble for Robust Next-Day Stock Direction Prediction","display_name":"Adaptive Performance-Weighted Ensemble for Robust Next-Day Stock Direction Prediction","publication_year":2026,"publication_date":"2026-11-11","ids":{"openalex":"https://openalex.org/W7225809040","doi":"https://doi.org/10.5281/zenodo.24130552"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.24130552","doi":"https://doi.org/10.5281/zenodo.24130552","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24130552","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"JournalArticle"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.24130552","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5161424283","display_name":"N.  Ravi","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"N.  Ravi","raw_affiliation_strings":["PSG POLYTECHNIC COLLEGE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"PSG POLYTECHNIC COLLEGE","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161456875","display_name":"Rithik N S","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rithik N S","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161454676","display_name":"Rithin Akash P S","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rithin Akash P S","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161469585","display_name":"Chanakya P S","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chanakya P S","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161441977","display_name":"Dharanish A","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dharanish A","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161437697","display_name":"Aakash T","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aakash T","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5161482840","display_name":"Balaji D","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Balaji D","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":1.0,"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":1.0,"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":0.0,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.0,"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"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.7501000165939331},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.5936999917030334},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5685999989509583},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5623999834060669},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.5142999887466431},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.48399999737739563},{"id":"https://openalex.org/C2988984586","wikidata":"https://www.wikidata.org/wiki/Q1020013","display_name":"Stock price","level":3,"score":0.4348999857902527},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.4165000021457672},{"id":"https://openalex.org/C70136482","wikidata":"https://www.wikidata.org/wiki/Q13583781","display_name":"A-weighting","level":3,"score":0.365200012922287},{"id":"https://openalex.org/C88389905","wikidata":"https://www.wikidata.org/wiki/Q223371","display_name":"Stock market index","level":4,"score":0.3476000130176544},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3467000126838684},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33149999380111694},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3294999897480011},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32829999923706055},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.32179999351501465},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2957000136375427},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.2867000102996826},{"id":"https://openalex.org/C19244329","wikidata":"https://www.wikidata.org/wiki/Q208697","display_name":"Financial market","level":2,"score":0.28450000286102295},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.28380000591278076},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2727999985218048},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.27140000462532043},{"id":"https://openalex.org/C175706884","wikidata":"https://www.wikidata.org/wiki/Q1130194","display_name":"Moving average","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C110593043","wikidata":"https://www.wikidata.org/wiki/Q7300787","display_name":"Real-time data","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C117245426","wikidata":"https://www.wikidata.org/wiki/Q235038","display_name":"Technical analysis","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.24130552","doi":"https://doi.org/10.5281/zenodo.24130552","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24130552","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"JournalArticle"},{"id":"doi:10.5281/zenodo.24130553","doi":"https://doi.org/10.5281/zenodo.24130553","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24130553","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"JournalArticle"}],"best_oa_location":{"id":"doi:10.5281/zenodo.24130552","doi":"https://doi.org/10.5281/zenodo.24130552","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24130552","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"JournalArticle"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Prediction":[0],"of":[1,10,31,88,94,109,143,165,217],"stock":[2,32,57,145],"markets":[3],"is":[4,91,188,231],"challenging":[5],"due":[6],"to":[7,158,203,227,233,237,245],"the":[8,42,51,54,92,107,123,152,180,189,212,215,223,239],"presence":[9],"factors":[11],"like":[12,76],"non-":[13],"linearity,":[14],"dependencies,":[15],"volatilities,":[16],"and":[17,63,84,117,173,197],"others":[18],"in":[19,151],"financial":[20],"time":[21,34,137],"series.":[22],"Various":[23],"machine":[24],"learning/deep":[25],"learning":[26],"models":[27],"capture":[28],"different":[29],"features":[30],"price":[33,58,97],"series":[35],"dynamics.":[36],"In":[37,127],"this":[38],"paper,":[39],"we":[40],"propose":[41],"framework":[43],"called":[44],"Adaptive":[45],"Performance-Weighted":[46],"Ensemble":[47],"(APWE)":[48],"that":[49,186,214],"utilizes":[50],"information":[52],"about":[53],"future":[55],"day":[56],"direction":[59],"via":[60],"LSTM,":[61],"GRU,":[62],"XGBoost":[64],"models.":[65],"APWE":[66,105,130,198],"uses":[67],"historical":[68],"daily":[69,77],"OHLCV":[70],"data":[71,142,163],"plus":[72],"some":[73],"technical":[74],"indicators,":[75],"return,":[78],"moving":[79],"averages,":[80],"RSI,":[81],"MACD,":[82],"volatility,":[83],"volume":[85],"change.":[86],"Output":[87],"each":[89,110],"model":[90,111,125,193],"probability":[93],"an":[95,119],"upward":[96],"movement.":[98],"Rather":[99],"than":[100],"keeping":[101],"static":[102],"ensemble":[103,120],"weights,":[104],"determines":[106],"weight":[108,205],"depending":[112],"on":[113,179],"its":[114],"recent":[115],"performance":[116,131],"produces":[118],"output":[121],"from":[122,148,154,211],"individual":[124,192],"outputs.":[126],"our":[128],"experiment,":[129],"was":[132],"tested":[133],"for":[134,171,176],"a":[135],"particular":[136],"interval":[138],"using":[139],"TCS.":[140],"Historical":[141],"NS":[144],"were":[146],"obtained":[147],"Yahoo":[149],"Finance":[150],"period":[153],"January":[155],"1,":[156,160],"2018,":[157],"September":[159],"2026.":[161],"Cleaned":[162],"consisted":[164],"2,117":[166],"observations,":[167],"including":[168],"1,693":[169],"observations":[170,175],"training":[172],"424":[174],"testing.":[177],"Based":[178],"results,":[181],"it":[182],"can":[183,208],"be":[184,209,246],"stated":[185],"GRU":[187],"most":[190],"accurate":[191],"with":[194],"49.76%":[195],"accuracy,":[196],"has":[199],"44.05%":[200],"accuracy":[201,224],"equivalent":[202],"equal":[204,228],"ensembles.":[206],"It":[207,230],"concluded":[210],"results":[213],"use":[216],"adaptive":[218,242],"weighting":[219,243],"does":[220],"not":[221],"improve":[222],"as":[225],"compared":[226],"weighting.":[229],"necessary":[232],"conduct":[234],"additional":[235],"research":[236],"reveal":[238],"cases":[240],"when":[241],"proves":[244],"beneficial.":[247]},"counts_by_year":[],"updated_date":"2026-11-12T12:28:57.059296","created_date":"2026-11-12T00:00:00"}