{"id":"https://openalex.org/W4323654313","doi":"https://doi.org/10.1142/s0218213023500343","title":"Predictive Learning Methods to Price European Options Using Ensemble Model and Multi-asset Data","display_name":"Predictive Learning Methods to Price European Options Using Ensemble Model and Multi-asset Data","publication_year":2023,"publication_date":"2023-03-07","ids":{"openalex":"https://openalex.org/W4323654313","doi":"https://doi.org/10.1142/s0218213023500343"},"language":"en","primary_location":{"id":"doi:10.1142/s0218213023500343","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218213023500343","pdf_url":null,"source":{"id":"https://openalex.org/S178780388","display_name":"International Journal of Artificial Intelligence Tools","issn_l":"0218-2130","issn":["0218-2130","1793-6349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal on Artificial Intelligence Tools","raw_type":"journal-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/A5067179853","display_name":"Kumar Shubham","orcid":null},"institutions":[{"id":"https://openalex.org/I188963388","display_name":"International Institute of Information Technology","ror":"https://ror.org/02dernx73","country_code":"IN","type":"education","lineage":["https://openalex.org/I188963388"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kumar Shubham","raw_affiliation_strings":["Department of Computer Science and Engineering, International Institute of Information Technology, Naya Raipur, Chhattisgarh, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, International Institute of Information Technology, Naya Raipur, Chhattisgarh, India","institution_ids":["https://openalex.org/I188963388"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101712130","display_name":"Vivek Tiwari","orcid":"https://orcid.org/0000-0002-7411-5113"},"institutions":[{"id":"https://openalex.org/I188963388","display_name":"International Institute of Information Technology","ror":"https://ror.org/02dernx73","country_code":"IN","type":"education","lineage":["https://openalex.org/I188963388"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vivek Tiwari","raw_affiliation_strings":["Department of Computer Science and Engineering, International Institute of Information Technology, Naya Raipur, Chhattisgarh, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, International Institute of Information Technology, Naya Raipur, Chhattisgarh, India","institution_ids":["https://openalex.org/I188963388"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020420871","display_name":"Kuldip Singh Patel","orcid":"https://orcid.org/0000-0001-8108-8195"},"institutions":[{"id":"https://openalex.org/I132153292","display_name":"Indian Institute of Technology Patna","ror":"https://ror.org/01ft5vz71","country_code":"IN","type":"education","lineage":["https://openalex.org/I132153292"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Kuldip Singh Patel","raw_affiliation_strings":["Department of Mathematics, Indian Institute of Technology, Patna, Bihta, Bihar, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, Indian Institute of Technology, Patna, Bihta, Bihar, India","institution_ids":["https://openalex.org/I132153292"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5020420871"],"corresponding_institution_ids":["https://openalex.org/I132153292"],"apc_list":null,"apc_paid":null,"fwci":8.5135,"has_fulltext":false,"cited_by_count":44,"citation_normalized_percentile":{"value":0.98035817,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"32","issue":"07","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":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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9965999722480774,"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"}},{"id":"https://openalex.org/T10067","display_name":"Stochastic processes and financial applications","score":0.9922000169754028,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7744535207748413},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5335137844085693},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5301419496536255},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.514195442199707},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.49598416686058044},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47393566370010376},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4685531556606293},{"id":"https://openalex.org/keywords/black\u2013scholes-model","display_name":"Black\u2013Scholes model","score":0.4402579665184021},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.41341981291770935},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3863586485385895},{"id":"https://openalex.org/keywords/volatility","display_name":"Volatility (finance)","score":0.20534446835517883},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.14350822567939758},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.10948100686073303}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7744535207748413},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5335137844085693},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5301419496536255},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.514195442199707},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.49598416686058044},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47393566370010376},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4685531556606293},{"id":"https://openalex.org/C163128081","wikidata":"https://www.wikidata.org/wiki/Q1338307","display_name":"Black\u2013Scholes model","level":3,"score":0.4402579665184021},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.41341981291770935},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3863586485385895},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.20534446835517883},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.14350822567939758},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10948100686073303},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0218213023500343","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218213023500343","pdf_url":null,"source":{"id":"https://openalex.org/S178780388","display_name":"International Journal of Artificial Intelligence Tools","issn_l":"0218-2130","issn":["0218-2130","1793-6349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal on Artificial Intelligence Tools","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1558904529","https://openalex.org/W1604311391","https://openalex.org/W1847387813","https://openalex.org/W1971217947","https://openalex.org/W1978032414","https://openalex.org/W2024056468","https://openalex.org/W2058076138","https://openalex.org/W2064926847","https://openalex.org/W2064978316","https://openalex.org/W2072757470","https://openalex.org/W2077791698","https://openalex.org/W2080852372","https://openalex.org/W2151065060","https://openalex.org/W2524982171","https://openalex.org/W2616008833","https://openalex.org/W2626256623","https://openalex.org/W2919115771","https://openalex.org/W2963867613","https://openalex.org/W3022650570","https://openalex.org/W3112360446","https://openalex.org/W3122003246","https://openalex.org/W3124436545","https://openalex.org/W3159776334","https://openalex.org/W4232656348"],"related_works":["https://openalex.org/W4200112873","https://openalex.org/W2955796858","https://openalex.org/W4224941037","https://openalex.org/W2004826645","https://openalex.org/W3135818052","https://openalex.org/W4366990902","https://openalex.org/W4317732970","https://openalex.org/W4388550696","https://openalex.org/W4313289487","https://openalex.org/W4224922629"],"abstract_inverted_index":{"Option":[0,14],"contracts":[1],"are":[2,140,172,199,209,235],"financial":[3,22],"instruments":[4],"that":[5,87],"serve":[6],"economic":[7,60],"purposes":[8],"for":[9,137,201],"various":[10,55,127],"institutions":[11],"and":[12,29,41,58,61,91,99,160,169,211],"individuals.":[13],"plays":[15],"a":[16,84,229],"crucial":[17],"role":[18],"in":[19,256],"developing":[20],"the":[21,26,109,124,138,185,191,194,225,233,241,244,251],"market":[23,257],"due":[24,35],"to":[25,36,65,175,189,224,247],"high":[27],"innovation":[28],"liquidity":[30],"associated":[31],"with":[32,184],"it.":[33],"However,":[34],"option":[37,203],"contract\u2019s":[38],"increased":[39],"adaptability":[40],"responsiveness,":[42],"its":[43],"pricing":[44],"mechanism":[45],"has":[46],"become":[47],"complicated.":[48],"The":[49,133,197,205],"conventional":[50],"parametric":[51],"models":[52,76,198,234],"suffer":[53],"from":[54,67,214],"computing":[56],"restrictions":[57],"implausible":[59],"statistical":[62],"presumptions":[63],"leading":[64],"deviations":[66],"real-world":[68],"dynamics.":[69],"Thus,":[70],"data-driven":[71],"strategies":[72],"built":[73],"upon":[74],"non-parametric":[75],"seems":[77],"compelling.":[78],"Machine":[79],"Learning":[80],"(ML)":[81],"serves":[82],"as":[83],"powerful":[85],"tool":[86],"can":[88],"increase":[89],"efficiency":[90],"productivity":[92],"by":[93,102,228],"automated":[94],"processes,":[95],"decreasing":[96],"human":[97],"biases":[98],"errors":[100],"caused":[101],"psychological":[103],"or":[104],"emotional":[105],"factors.":[106],"Most":[107],"of":[108,126,193,243,253],"existing":[110],"literature":[111],"involves":[112],"only":[113],"neural":[114],"networks,":[115],"whereas":[116],"alternative":[117],"algorithms":[118,129,135,221],"remain":[119],"undiscovered.":[120],"This":[121],"study":[122,139],"explores":[123],"effectiveness":[125],"ML":[128,134,195,220],"through":[130],"different":[131],"experimentation.":[132],"harnessed":[136],"Artificial":[141],"Neural":[142],"Networks":[143],"(ANN),":[144],"XGBoost,":[145],"Decision":[146],"Tree":[147],"Regression,":[148,151,154],"Support":[149],"Vector":[150],"Random":[152],"Forest":[153],"Long":[155],"short-term":[156],"memory":[157],"(LSTM)":[158],"Network":[159],"Gated":[161],"recurrent":[162],"unit":[163],"(GRU)":[164],"Network.":[165],"Furthermore,":[166],"multi-asset":[167],"training":[168],"ensemble":[170],"modelling":[171],"carried":[173,182],"out":[174,183],"enhance":[176],"predictive":[177],"performance.":[178],"A":[179],"comparison":[180],"is":[181],"seminal":[186],"Black-Scholes":[187,226],"model":[188,227],"highlight":[190],"advantages":[192],"approach.":[196],"evaluated":[200,236],"European":[202],"contracts.":[204],"underlying":[206],"assets":[207],"used":[208],"NIFTY50":[210],"BANKNIFTY":[212],"indices":[213],"India\u2019s":[215],"National":[216],"Stock":[217],"Exchange":[218],"(NSE).":[219],"performed":[222],"superior":[223],"significant":[230],"margin.":[231],"Additionally,":[232],"on":[237],"data":[238],"collected":[239],"following":[240],"outbreak":[242],"COVID":[245],"epidemic":[246],"get":[248],"insight":[249],"into":[250],"effects":[252],"abrupt":[254],"changes":[255],"sentiment.":[258]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":21}],"updated_date":"2026-08-11T07:18:39.950985","created_date":"2025-10-10T00:00:00"}
