{"id":"https://openalex.org/W2965511779","doi":"https://doi.org/10.1109/ssci50451.2021.9660134","title":"Machine Learning for Stock Prediction Based on Fundamental Analysis","display_name":"Machine Learning for Stock Prediction Based on Fundamental Analysis","publication_year":2021,"publication_date":"2021-12-05","ids":{"openalex":"https://openalex.org/W2965511779","doi":"https://doi.org/10.1109/ssci50451.2021.9660134","mag":"2965511779"},"language":"en","primary_location":{"id":"doi:10.1109/ssci50451.2021.9660134","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci50451.2021.9660134","pdf_url":null,"source":{"id":"https://openalex.org/S4363604921","display_name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2202.05702","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005373227","display_name":"Yuxuan Huang","orcid":"https://orcid.org/0000-0002-4842-8781"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yuxuan Huang","raw_affiliation_strings":["Broadridge Financial Solutions,Toronto,Canada","Broadridge Financial Solutions, Toronto, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Broadridge Financial Solutions,Toronto,Canada","institution_ids":[]},{"raw_affiliation_string":"Broadridge Financial Solutions, Toronto, Canada","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043333026","display_name":"Luiz Fernando Capretz","orcid":"https://orcid.org/0000-0001-6966-2369"},"institutions":[{"id":"https://openalex.org/I125749732","display_name":"Western University","ror":"https://ror.org/02grkyz14","country_code":"CA","type":"education","lineage":["https://openalex.org/I125749732"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Luiz Fernando Capretz","raw_affiliation_strings":["Western University,Electrical and Computer Engineering,Canada","Electrical and Computer Engineering, Western University, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Western University,Electrical and Computer Engineering,Canada","institution_ids":["https://openalex.org/I125749732"]},{"raw_affiliation_string":"Electrical and Computer Engineering, Western University, Canada","institution_ids":["https://openalex.org/I125749732"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072824702","display_name":"Danny Ho","orcid":"https://orcid.org/0000-0002-3748-6345"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Danny Ho","raw_affiliation_strings":["NFA Estimation Inc,Richmond Hill,Canada","NFA Estimation Inc, Richmond Hill, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NFA Estimation Inc,Richmond Hill,Canada","institution_ids":[]},{"raw_affiliation_string":"NFA Estimation Inc, Richmond Hill, Canada","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5005373227"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":13.7887,"has_fulltext":true,"cited_by_count":64,"citation_normalized_percentile":{"value":0.99501069,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"01","last_page":"10"},"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9951000213623047,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9829000234603882,"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/computer-science","display_name":"Computer science","score":0.6823755502700806},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6716718673706055},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6206384301185608},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6187579035758972},{"id":"https://openalex.org/keywords/adaptive-neuro-fuzzy-inference-system","display_name":"Adaptive neuro fuzzy inference system","score":0.605984628200531},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5903759002685547},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5619537234306335},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5129954218864441},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.4761331379413605},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.2665557861328125},{"id":"https://openalex.org/keywords/fuzzy-control-system","display_name":"Fuzzy control system","score":0.18707957863807678},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12590795755386353}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6823755502700806},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6716718673706055},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6206384301185608},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6187579035758972},{"id":"https://openalex.org/C186108316","wikidata":"https://www.wikidata.org/wiki/Q352530","display_name":"Adaptive neuro fuzzy inference system","level":4,"score":0.605984628200531},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5903759002685547},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5619537234306335},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5129954218864441},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.4761331379413605},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.2665557861328125},{"id":"https://openalex.org/C195975749","wikidata":"https://www.wikidata.org/wiki/Q1475705","display_name":"Fuzzy control system","level":3,"score":0.18707957863807678},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12590795755386353},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/ssci50451.2021.9660134","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci50451.2021.9660134","pdf_url":null,"source":{"id":"https://openalex.org/S4363604921","display_name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2202.05702","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2202.05702","pdf_url":"https://arxiv.org/pdf/2202.05702","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:ir.lib.uwo.ca:etd-8423","is_oa":true,"landing_page_url":"https://ir.lib.uwo.ca/etd/6148","pdf_url":"https://ir.lib.uwo.ca/etd/6148","source":{"id":"https://openalex.org/S4306400648","display_name":"Scholarship@Western (Western University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I125749732","host_organization_name":"Western University","host_organization_lineage":["https://openalex.org/I125749732"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Electronic Thesis and Dissertation Repository","raw_type":"text"},{"id":"pmh:oai:RePEc:arx:papers:2202.05702","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"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":"preprint"},{"id":"pmh:oai:ir.lib.uwo.ca:electricalpub-1569","is_oa":false,"landing_page_url":"https://ir.lib.uwo.ca/electricalpub/562","pdf_url":null,"source":{"id":"https://openalex.org/S4306400648","display_name":"Scholarship@Western (Western University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I125749732","host_organization_name":"Western University","host_organization_lineage":["https://openalex.org/I125749732"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Electrical and Computer Engineering Publications","raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2202.05702","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2202.05702","pdf_url":"https://arxiv.org/pdf/2202.05702","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.46000000834465027,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1417010456","https://openalex.org/W1898724844","https://openalex.org/W1995319408","https://openalex.org/W2004463884","https://openalex.org/W2007358469","https://openalex.org/W2019207321","https://openalex.org/W2020820283","https://openalex.org/W2025053102","https://openalex.org/W2029583945","https://openalex.org/W2043464706","https://openalex.org/W2049916782","https://openalex.org/W2056763477","https://openalex.org/W2086200094","https://openalex.org/W2090637028","https://openalex.org/W2116814040","https://openalex.org/W2124776405","https://openalex.org/W2155744005","https://openalex.org/W2195085701","https://openalex.org/W2323521202","https://openalex.org/W2607162077","https://openalex.org/W2619016901","https://openalex.org/W2889010933","https://openalex.org/W2896000537","https://openalex.org/W3124298675","https://openalex.org/W3124580969","https://openalex.org/W3128792745"],"related_works":["https://openalex.org/W2114654021","https://openalex.org/W2263529430","https://openalex.org/W2389800468","https://openalex.org/W4390103748","https://openalex.org/W4388745254","https://openalex.org/W2980082554","https://openalex.org/W1517228774","https://openalex.org/W2767419625","https://openalex.org/W2389704471","https://openalex.org/W2117019857"],"abstract_inverted_index":{"Application":[0],"of":[1,11,19,49,74,149],"machine":[2,34,82,180],"learning":[3,35,83,181],"for":[4,99,172],"stock":[5,42,75,100,193],"prediction":[6,58,101,138],"is":[7,143],"attracting":[8],"a":[9],"lot":[10],"attention":[12],"in":[13,24,117],"recent":[14],"years.":[15],"A":[16],"large":[17],"amount":[18],"research":[20],"has":[21],"been":[22],"conducted":[23],"this":[25,67],"area":[26],"and":[27,63,79,92,114,123,140,151],"multiple":[28],"existing":[29,51],"results":[30,130],"have":[31,53],"shown":[32],"that":[33,132,179],"methods":[36],"could":[37,183],"be":[38,184],"successfully":[39],"used":[40,185],"toward":[41],"predicting":[43],"using":[44,59],"stocks'":[45,60],"historical":[46,61],"data.":[47],"Most":[48],"these":[50],"approaches":[52],"focused":[54],"on":[55,103],"short":[56],"term":[57],"price":[62],"technical":[64],"indicators.":[65],"In":[66,106],"paper,":[68],"we":[69,108],"prepared":[70],"22":[71],"years'":[72],"worth":[73],"quarterly":[76],"financial":[77],"data":[78],"investigated":[80],"three":[81],"algorithms:":[84],"Feed-forward":[85],"Neural":[86,94],"Network":[87],"(FNN),":[88],"Random":[89],"Forest":[90],"(RF)":[91],"Adaptive":[93],"Fuzzy":[95],"Inference":[96],"System":[97],"(ANFIS)":[98],"based":[102,111],"fundamental":[104,188],"analysis.":[105],"addition,":[107],"applied":[109],"RF":[110,133],"feature":[112,141],"selection":[113,142],"bootstrap":[115],"aggregation":[116],"order":[118],"to":[119,145,186],"improve":[120,146],"model":[121,134,156],"performance":[122,148],"aggregate":[124],"predictions":[125],"from":[126],"different":[127],"models.":[128],"Our":[129,176],"show":[131],"achieves":[135],"the":[136,154,164,173],"best":[137],"results,":[139],"able":[144],"test":[147,174],"FNN":[150],"ANFIS.":[152],"Moreover,":[153],"aggregated":[155],"outperforms":[157],"all":[158],"baseline":[159],"models":[160,182],"as":[161,163],"well":[162],"benchmark":[165],"DJIA":[166],"index":[167],"by":[168],"an":[169],"acceptable":[170],"margin":[171],"period.":[175],"findings":[177],"demonstrate":[178],"aid":[187],"analysts":[189],"with":[190],"decision-making":[191],"regarding":[192],"investment.":[194]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":18},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2}],"updated_date":"2026-05-14T08:36:36.166977","created_date":"2025-10-10T00:00:00"}
