{"id":"https://openalex.org/W7204994686","doi":"https://doi.org/10.3389/frai.2026.1902018","title":"Evaluating hybrid quantum\u2013classical learning for financial forecasting: a comparative study with classical machine learning models on BSE indices","display_name":"Evaluating hybrid quantum\u2013classical learning for financial forecasting: a comparative study with classical machine learning models on BSE indices","publication_year":2026,"publication_date":"2026-09-02","ids":{"openalex":"https://openalex.org/W7204994686","doi":"https://doi.org/10.3389/frai.2026.1902018","pmid":"https://pubmed.ncbi.nlm.nih.gov/42751118","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/13579563"},"language":"en","primary_location":{"id":"doi:10.3389/frai.2026.1902018","doi":"https://doi.org/10.3389/frai.2026.1902018","is_oa":true,"landing_page_url":"https://doi.org/10.3389/frai.2026.1902018","pdf_url":"https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1902018/pdf","source":{"id":"https://openalex.org/S4210197006","display_name":"Frontiers in Artificial Intelligence","issn_l":"2624-8212","issn":["2624-8212"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence","raw_type":"journal-article"},"volume":null,"type":"review","is_traditional":true,"indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1902018/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5149010217","display_name":"Rashmi Nair","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210162439","display_name":"MIT Art, Design and Technology University","ror":"https://ror.org/05b69xa56","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210162439"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Rashmi Nair","raw_affiliation_strings":["Department of Computer Science and Engineering, MIT School of Computing, MIT Art Design and Technology University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, MIT School of Computing, MIT Art Design and Technology University","institution_ids":["https://openalex.org/I4210162439"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074987941","display_name":"Pankaj Ramchandra Chandre","orcid":"https://orcid.org/0000-0002-4538-754X","observed_orcids":["https://orcid.org/0000-0002-4538-754X"]},"institutions":[{"id":"https://openalex.org/I4210162439","display_name":"MIT Art, Design and Technology University","ror":"https://ror.org/05b69xa56","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210162439"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Pankaj Chandre","raw_affiliation_strings":["Department of Computer Science and Engineering, MIT School of Computing, MIT Art Design and Technology University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, MIT School of Computing, MIT Art Design and Technology University","institution_ids":["https://openalex.org/I4210162439"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092924828","display_name":"Shahin Shoukat Makubhai","orcid":"https://orcid.org/0000-0001-7737-8798","observed_orcids":["https://orcid.org/0000-0001-7737-8798"]},"institutions":[{"id":"https://openalex.org/I4210162439","display_name":"MIT Art, Design and Technology University","ror":"https://ror.org/05b69xa56","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210162439"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shahin Makubhai","raw_affiliation_strings":["Department of Computer Science and Engineering, MIT School of Computing, MIT Art Design and Technology University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, MIT School of Computing, MIT Art Design and Technology University","institution_ids":["https://openalex.org/I4210162439"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114662519","display_name":"Bhagyashree D. Shendkar","orcid":"https://orcid.org/0000-0003-4808-2642","observed_orcids":["https://orcid.org/0000-0003-4808-2642"]},"institutions":[{"id":"https://openalex.org/I4210162439","display_name":"MIT Art, Design and Technology University","ror":"https://ror.org/05b69xa56","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210162439"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Bhagyashree Shendkar","raw_affiliation_strings":["Department of Computer Science and Engineering, MIT School of Computing, MIT Art Design and Technology University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, MIT School of Computing, MIT Art Design and Technology University","institution_ids":["https://openalex.org/I4210162439"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083132059","display_name":"Disha Deotale","orcid":"https://orcid.org/0000-0002-1859-2898","observed_orcids":["https://orcid.org/0000-0002-1859-2898"]},"institutions":[{"id":"https://openalex.org/I4210162439","display_name":"MIT Art, Design and Technology University","ror":"https://ror.org/05b69xa56","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210162439"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Disha Deotale","raw_affiliation_strings":["Department of Computer Science and Engineering, MIT School of Computing, MIT Art Design and Technology University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, MIT School of Computing, MIT Art Design and Technology University","institution_ids":["https://openalex.org/I4210162439"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112058976","display_name":"Vijaya S. Patil","orcid":"https://orcid.org/0009-0007-8210-4661","observed_orcids":["https://orcid.org/0009-0007-8210-4661"]},"institutions":[{"id":"https://openalex.org/I4210162439","display_name":"MIT Art, Design and Technology University","ror":"https://ror.org/05b69xa56","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210162439"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vijaya Patil","raw_affiliation_strings":["Department of Computer Science and Engineering, MIT School of Computing, MIT Art Design and Technology University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, MIT School of Computing, MIT Art Design and Technology University","institution_ids":["https://openalex.org/I4210162439"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5149010217"],"corresponding_institution_ids":["https://openalex.org/I4210162439"],"apc_list":{"value":1950,"currency":"CHF","value_usd":2353},"apc_paid":{"value":1950,"currency":"CHF","value_usd":2353},"fwci":0.0,"has_fulltext":true,"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":"9","issue":null,"first_page":"1902018","last_page":"1902018"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9990000128746033,"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":0.9990000128746033,"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/T10682","display_name":"Quantum Computing Algorithms and Architecture","score":0.0006000000284984708,"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/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":[{"id":"https://openalex.org/keywords/hybrid-quantum-classical-machine-learning","display_name":"hybrid quantum-classical machine learning","score":0.8619999885559082},{"id":"https://openalex.org/keywords/quantum-machine-learning","display_name":"quantum machine learning","score":0.8629999756813049},{"id":"https://openalex.org/keywords/financial-time-series-forecasting","display_name":"financial time series forecasting","score":0.9380000233650208},{"id":"https://openalex.org/keywords/variational-quantum-circuits","display_name":"variational quantum circuits","score":0.7820000052452087},{"id":"https://openalex.org/keywords/random-forest-feature-selection","display_name":"random forest feature selection","score":0.8009999990463257},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"principal component analysis","score":0.8790000081062317},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"dimensionality reduction","score":0.8410000205039978},{"id":"https://openalex.org/keywords/esg-index","display_name":"ESG indices","score":0.7710000276565552},{"id":"https://openalex.org/keywords/stock-market-forecasting","display_name":"stock market prediction","score":0.5239999890327454},{"id":"https://openalex.org/keywords/model-comparison","display_name":"model comparison","score":0.35100001096725464}],"concepts":[{"id":"https://openalex.org/C2779094486","wikidata":"https://www.wikidata.org/wiki/Q18811578","display_name":"Quantum machine learning","level":4,"score":0.65829998254776},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5929999947547913},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5922999978065491},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5669000148773193},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5601000189781189},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5519999861717224},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.49230000376701355},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4708000123500824},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.436599999666214},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.43639999628067017},{"id":"https://openalex.org/C84114770","wikidata":"https://www.wikidata.org/wiki/Q46344","display_name":"Quantum","level":2,"score":0.42179998755455017},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.36970001459121704},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3659000098705292},{"id":"https://openalex.org/C117245426","wikidata":"https://www.wikidata.org/wiki/Q235038","display_name":"Technical analysis","level":2,"score":0.3637000024318695},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.35359999537467957},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.34790000319480896},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.32580000162124634},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.29989999532699585},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.2922999858856201},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2874999940395355},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C153874254","wikidata":"https://www.wikidata.org/wiki/Q115542","display_name":"Canonical correlation","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2680000066757202},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2558000087738037},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2533999979496002}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3389/frai.2026.1902018","doi":"https://doi.org/10.3389/frai.2026.1902018","is_oa":true,"landing_page_url":"https://doi.org/10.3389/frai.2026.1902018","pdf_url":"https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1902018/pdf","source":{"id":"https://openalex.org/S4210197006","display_name":"Frontiers in Artificial Intelligence","issn_l":"2624-8212","issn":["2624-8212"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:pubmedcentral.nih.gov:13579563","doi":null,"is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13579563/","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13579563/pdf/frai-09-1902018.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Front Artif Intell","raw_type":"Text"},{"id":"pmid:42751118","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42751118","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in artificial intelligence","raw_type":"Journal Article"},{"id":"pmh:oai:doaj.org/article:0f2b597a9bce4430ad3049ac4f43455f","doi":null,"is_oa":false,"landing_page_url":"https://doaj.org/article/0f2b597a9bce4430ad3049ac4f43455f","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"Frontiers in Artificial Intelligence, Vol 9 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3389/frai.2026.1902018","doi":"https://doi.org/10.3389/frai.2026.1902018","is_oa":true,"landing_page_url":"https://doi.org/10.3389/frai.2026.1902018","pdf_url":"https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1902018/pdf","source":{"id":"https://openalex.org/S4210197006","display_name":"Frontiers in Artificial Intelligence","issn_l":"2624-8212","issn":["2624-8212"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204994686.pdf","grobid_xml":"https://content.openalex.org/works/W7204994686.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W1964357740","https://openalex.org/W1999996900","https://openalex.org/W2559394418","https://openalex.org/W2794444783","https://openalex.org/W2911964244","https://openalex.org/W3040284875","https://openalex.org/W3097610849","https://openalex.org/W4214631008","https://openalex.org/W4224981619","https://openalex.org/W4296126438","https://openalex.org/W4313830797","https://openalex.org/W4323922334","https://openalex.org/W4366268824","https://openalex.org/W4366826586","https://openalex.org/W4376274518","https://openalex.org/W4383879098","https://openalex.org/W4387169617","https://openalex.org/W4388793082","https://openalex.org/W4389162750","https://openalex.org/W4412939327","https://openalex.org/W4413027924","https://openalex.org/W4415918834"],"related_works":["https://openalex.org/W7222813588","https://openalex.org/W4409495709","https://openalex.org/W7172528410","https://openalex.org/W7223123365","https://openalex.org/W4414903531","https://openalex.org/W4413877341","https://openalex.org/W7084112589","https://openalex.org/W4416145397","https://openalex.org/W4396694036","https://openalex.org/W7119475148"],"abstract_inverted_index":{"The":[0,165,217],"forecasting":[1,235],"of":[2,64,77,87,116,124,228],"financial":[3,98,203,234],"time":[4],"series":[5],"has":[6,24,192],"gained":[7],"more":[8],"significance":[9],"in":[10,28,61,68,74,84,96,156,201],"decision":[11],"making":[12],"within":[13],"a":[14,26,42,51,65,69,88],"dynamic":[15],"economic":[16],"setting.":[17],"Over":[18],"the":[19,58,62,75,85,114,122,130,141,184,190,223,229],"last":[20],"few":[21],"years,":[22],"there":[23],"been":[25],"push":[27],"exploring":[29],"both":[30],"classical":[31,131,196],"machine":[32,38,231],"learning":[33,39,232],"methods":[34],"and":[35,82,109,111,118,136,144,149,206,226],"novel":[36],"quantum":[37,148,230],"models":[40,153,209],"with":[41,113,170,179],"view":[43],"to":[44,147,158,233],"enhance":[45],"predictive":[46],"accuracy.":[47],"This":[48],"paper":[49,191],"suggests":[50],"quantum-classical":[52,208],"regression":[53],"framework":[54],"developed":[55],"that":[56,195],"combines":[57],"feature":[59],"selection":[60],"form":[63,86],"Random":[66,134],"Forest":[67,135],"hybrid":[70,150,166,207],"approach,":[71],"dimensionality":[72],"reduction":[73],"mode":[76],"Principal":[78],"Component":[79],"Analysis":[80],"(PCA),":[81],"predicting":[83],"Variational":[89],"Quantum":[90,152,159],"Circuit":[91],"(VQC).":[92],"It":[93],"is":[94,162],"tested":[95],"various":[97],"indicators,":[99],"as":[100,121],"BSE":[101,185],"ESG,":[102],"Carbon":[103],"Exchange,":[104,107],"Energy,":[105],"Green":[106],"Oil":[108],"Gas":[110],"Power,":[112],"help":[115],"RMSE":[117],"R":[119],"2":[120],"measure":[123],"performance.":[125],"As":[126],"experimental":[127],"findings":[128],"indicate,":[129],"algorithms":[132],"like":[133],"k-Nearest":[137],"Neighbors":[138],"always":[139],"have":[140],"best":[142,200],"accuracy":[143],"stability":[145],"compared":[146],"ones.":[151],"are":[154,198,210],"unstable,":[155],"contrast":[157],"kNN":[160],"which":[161],"fairly":[163,175],"stable.":[164],"model":[167],"performs":[168],"moderately":[169],"consistent":[171],"error":[172],"values":[173],"but":[174,213],"lower":[176],"explanatory":[177],"power":[178],"significant":[180],"results":[181,218],"mostly":[182],"on":[183,222],"ESG":[186],"data.":[187],"In":[188],"general,":[189],"pointed":[193],"out":[194],"approaches":[197],"still":[199],"structured":[202],"prediction":[204],"tasks":[205],"potentially":[211],"effective,":[212],"not":[214],"yet":[215],"competent.":[216],"give":[219],"valuable":[220],"information":[221],"existing":[224],"limitation":[225],"future":[227],"applications.":[236]},"counts_by_year":[],"updated_date":"2026-11-12T12:28:57.059296","created_date":"2026-09-02T00:00:00"}