{"id":"https://openalex.org/W4417529479","doi":"https://doi.org/10.48550/arxiv.2512.15738","title":"Hybrid Quantum-Classical Ensemble Learning for S\\&amp;P 500 Directional Prediction","display_name":"Hybrid Quantum-Classical Ensemble Learning for S\\&amp;P 500 Directional Prediction","publication_year":2025,"publication_date":"2025-12-06","ids":{"openalex":"https://openalex.org/W4417529479","doi":"https://doi.org/10.48550/arxiv.2512.15738"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2512.15738","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.15738","pdf_url":"https://arxiv.org/pdf/2512.15738","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2512.15738","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037956466","display_name":"Abraham Itzhak Weinberg","orcid":"https://orcid.org/0000-0002-2505-9653"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Weinberg, Abraham Itzhak","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5037956466"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"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/T10682","display_name":"Quantum Computing Algorithms and Architecture","score":0.49470001459121704,"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"}},"topics":[{"id":"https://openalex.org/T10682","display_name":"Quantum Computing Algorithms and Architecture","score":0.49470001459121704,"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"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.313400000333786,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.010099999606609344,"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/ensemble-learning","display_name":"Ensemble learning","score":0.621399998664856},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5202999711036682},{"id":"https://openalex.org/keywords/sharpe-ratio","display_name":"Sharpe ratio","score":0.5005000233650208},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.44609999656677246},{"id":"https://openalex.org/keywords/mean-squared-prediction-error","display_name":"Mean squared prediction error","score":0.3628000020980835},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.3431999981403351},{"id":"https://openalex.org/keywords/statistical-ensemble","display_name":"Statistical ensemble","score":0.3154999911785126},{"id":"https://openalex.org/keywords/statistical-hypothesis-testing","display_name":"Statistical hypothesis testing","score":0.3151000142097473},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.30889999866485596}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6528000235557556},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.621399998664856},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5253000259399414},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.524399995803833},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5202999711036682},{"id":"https://openalex.org/C139938925","wikidata":"https://www.wikidata.org/wiki/Q1501898","display_name":"Sharpe ratio","level":3,"score":0.5005000233650208},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.44609999656677246},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.3628000020980835},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.3431999981403351},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33329999446868896},{"id":"https://openalex.org/C50311922","wikidata":"https://www.wikidata.org/wiki/Q898535","display_name":"Statistical ensemble","level":4,"score":0.3154999911785126},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.3151000142097473},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.30889999866485596},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.30329999327659607},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.299699991941452},{"id":"https://openalex.org/C84114770","wikidata":"https://www.wikidata.org/wiki/Q46344","display_name":"Quantum","level":2,"score":0.2937999963760376},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.2915000021457672},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2581000030040741},{"id":"https://openalex.org/C2777115002","wikidata":"https://www.wikidata.org/wiki/Q7168246","display_name":"Performance prediction","level":2,"score":0.2535000145435333}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2512.15738","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.15738","pdf_url":"https://arxiv.org/pdf/2512.15738","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2512.15738","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.15738","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2512.15738","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.15738","pdf_url":"https://arxiv.org/pdf/2512.15738","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Financial":[0],"market":[1,34,154],"prediction":[2],"is":[3],"a":[4,38,61,116,184],"challenging":[5],"application":[6],"of":[7,72,187],"machine":[8],"learning,":[9],"where":[10],"even":[11],"small":[12],"improvements":[13],"in":[14],"directional":[15,55],"accuracy":[16,27,56],"can":[17],"yield":[18],"substantial":[19],"value.":[20],"Most":[21],"models":[22],"struggle":[23],"to":[24,29,126],"exceed":[25],"55--57\\%":[26],"due":[28],"high":[30],"noise,":[31],"non-stationarity,":[32],"and":[33,49,166],"efficiency.":[35],"We":[36,150],"introduce":[37],"hybrid":[39],"ensemble":[40,140],"framework":[41,68],"combining":[42,81],"quantum":[43,119],"sentiment":[44,122],"analysis,":[45,123],"Decision":[46,86],"Transformer":[47],"architecture,":[48],"strategic":[50],"model":[51,181],"selection,":[52],"achieving":[53],"60.14\\%":[54,144],"on":[57,93,101,152],"S\\&amp;P":[58],"500":[59],"prediction,":[60],"3.10\\%":[62],"improvement":[63],"over":[64],"individual":[65],"models.":[66],"Our":[67],"addresses":[69],"three":[70],"limitations":[71],"prior":[73],"approaches.":[74],"First,":[75],"architecture":[76],"diversity":[77],"dominates":[78],"dataset":[79],"diversity:":[80],"different":[82],"learning":[83],"algorithms":[84],"(LSTM,":[85],"Transformer,":[87],"XGBoost,":[88],"Random":[89],"Forest,":[90],"Logistic":[91],"Regression)":[92],"the":[94,163],"same":[95],"data":[96,155],"outperforms":[97],"training":[98],"identical":[99],"architectures":[100],"multiple":[102],"datasets":[103],"(60.14\\%":[104],"vs.\\":[105,145],"52.80\\%),":[106],"confirmed":[107],"by":[108],"correlation":[109],"analysis":[110],"($r&gt;0.6$":[111],"among":[112],"same-architecture":[113],"models).":[114],"Second,":[115],"4-qubit":[117],"variational":[118],"circuit":[120],"enhances":[121],"providing":[124],"+0.8\\%":[125],"+1.5\\%":[127],"gains":[128],"per":[129],"model.":[130],"Third,":[131],"smart":[132],"filtering":[133,179],"excludes":[134],"weak":[135],"predictors":[136],"(accuracy":[137],"$&lt;52\\%$),":[138],"improving":[139],"performance":[141],"(Top-7":[142],"models:":[143,148],"all":[146],"35":[147],"51.2\\%).":[149],"evaluate":[151],"2020--2023":[153],"across":[156],"seven":[157],"instruments,":[158],"covering":[159],"diverse":[160],"regimes":[161],"including":[162],"COVID-19":[164],"crash":[165],"inflation-driven":[167],"correction.":[168],"McNemar's":[169],"test":[170],"confirms":[171],"statistical":[172],"significance":[173],"($p&lt;0.05$).":[174],"Preliminary":[175],"backtesting":[176],"with":[177],"confidence-based":[178],"(6+":[180],"consensus)":[182],"yields":[183],"Sharpe":[185],"ratio":[186],"1.2":[188],"versus":[189],"buy-and-hold's":[190],"0.8,":[191],"demonstrating":[192],"practical":[193],"trading":[194],"potential.":[195]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-12-21T00:00:00"}
