{"id":"https://openalex.org/W2182581211","doi":"https://doi.org/10.1109/dsaa.2015.7344855","title":"Sentiment and stock market volatility predictive modelling \u2014 A hybrid approach","display_name":"Sentiment and stock market volatility predictive modelling \u2014 A hybrid approach","publication_year":2015,"publication_date":"2015-10-01","ids":{"openalex":"https://openalex.org/W2182581211","doi":"https://doi.org/10.1109/dsaa.2015.7344855","mag":"2182581211"},"language":"en","primary_location":{"id":"doi:10.1109/dsaa.2015.7344855","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa.2015.7344855","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5024058923","display_name":"Rapheal Olaniyan","orcid":null},"institutions":[{"id":"https://openalex.org/I124357947","display_name":"University of London","ror":"https://ror.org/04cw6st05","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Rapheal Olaniyan","raw_affiliation_strings":["Department of Computing, University of London"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computing, University of London","institution_ids":["https://openalex.org/I124357947"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069649514","display_name":"Daniel Stamate","orcid":"https://orcid.org/0000-0001-8565-6890"},"institutions":[{"id":"https://openalex.org/I124357947","display_name":"University of London","ror":"https://ror.org/04cw6st05","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Daniel Stamate","raw_affiliation_strings":["Department of Computing, University of London"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computing, University of London","institution_ids":["https://openalex.org/I124357947"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041863006","display_name":"Lahcen Ouarbya","orcid":null},"institutions":[{"id":"https://openalex.org/I124357947","display_name":"University of London","ror":"https://ror.org/04cw6st05","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Lahcen Ouarbya","raw_affiliation_strings":["Department of Computing, University of London"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computing, University of London","institution_ids":["https://openalex.org/I124357947"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015934326","display_name":"Doina Logof\u0103tu","orcid":"https://orcid.org/0000-0002-1678-3527"},"institutions":[{"id":"https://openalex.org/I114090438","display_name":"Goethe University Frankfurt","ror":"https://ror.org/04cvxnb49","country_code":"DE","type":"education","lineage":["https://openalex.org/I114090438"]},{"id":"https://openalex.org/I5237613","display_name":"Frankfurt University of Applied Sciences","ror":"https://ror.org/02r625m11","country_code":"DE","type":"education","lineage":["https://openalex.org/I5237613"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Doina Logofatu","raw_affiliation_strings":["Department of Computer Science, Frankfurt University of Applied Sciences"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Frankfurt University of Applied Sciences","institution_ids":["https://openalex.org/I114090438","https://openalex.org/I5237613"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.4028,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.80141984,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","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":0.9998999834060669,"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":0.9998999834060669,"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/T11059","display_name":"Market Dynamics and Volatility","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9972000122070312,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/volatility","display_name":"Volatility (finance)","score":0.696307897567749},{"id":"https://openalex.org/keywords/predictive-power","display_name":"Predictive power","score":0.6868432760238647},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.6586334109306335},{"id":"https://openalex.org/keywords/autoregressive-conditional-heteroskedasticity","display_name":"Autoregressive conditional heteroskedasticity","score":0.620873749256134},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.6160954833030701},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.5381224155426025},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5016942024230957},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.43749186396598816},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.4243241548538208},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.42088523507118225},{"id":"https://openalex.org/keywords/financial-economics","display_name":"Financial economics","score":0.39394456148147583},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.23537522554397583},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08961278200149536},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08083197474479675},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.06639519333839417}],"concepts":[{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.696307897567749},{"id":"https://openalex.org/C2778136018","wikidata":"https://www.wikidata.org/wiki/Q10350689","display_name":"Predictive power","level":2,"score":0.6868432760238647},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.6586334109306335},{"id":"https://openalex.org/C23922673","wikidata":"https://www.wikidata.org/wiki/Q180752","display_name":"Autoregressive conditional heteroskedasticity","level":3,"score":0.620873749256134},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.6160954833030701},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.5381224155426025},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5016942024230957},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.43749186396598816},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.4243241548538208},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.42088523507118225},{"id":"https://openalex.org/C106159729","wikidata":"https://www.wikidata.org/wiki/Q2294553","display_name":"Financial economics","level":1,"score":0.39394456148147583},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23537522554397583},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08961278200149536},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08083197474479675},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.06639519333839417},{"id":"https://openalex.org/C2780762169","wikidata":"https://www.wikidata.org/wiki/Q5905368","display_name":"Horse","level":2,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/dsaa.2015.7344855","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa.2015.7344855","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.gold.ac.uk:17664","is_oa":false,"landing_page_url":"https://research.gold.ac.uk/view/goldsmiths/Stamate=3ADaniel=3A=3A.html>;","pdf_url":null,"source":{"id":"https://openalex.org/S4306400496","display_name":"Goldsmiths (University of London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I166337079","host_organization_name":"Queen Mary University of London","host_organization_lineage":["https://openalex.org/I166337079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"Conference or Workshop Item"},{"id":"pmh:oai:fhffm.bsz-bw.de:5552","is_oa":false,"landing_page_url":"https://fhffm.bsz-bw.de/frontdoor/index/index/docId/5552","pdf_url":null,"source":{"id":"https://openalex.org/S4306400774","display_name":"Scientific Publication Server (Frankfurt University of Applied Sciences)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I5237613","host_organization_name":"Frankfurt University of Applied Sciences","host_organization_lineage":["https://openalex.org/I5237613"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"conferenceobject"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.49000000953674316,"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W586799479","https://openalex.org/W1508206474","https://openalex.org/W1594226207","https://openalex.org/W1596728080","https://openalex.org/W1965600549","https://openalex.org/W1966916709","https://openalex.org/W1988790447","https://openalex.org/W1999814123","https://openalex.org/W2013675302","https://openalex.org/W2015174807","https://openalex.org/W2018331222","https://openalex.org/W2029949252","https://openalex.org/W2049239673","https://openalex.org/W2058076138","https://openalex.org/W2074107564","https://openalex.org/W2083844448","https://openalex.org/W2140336868","https://openalex.org/W2145339411","https://openalex.org/W2171468534","https://openalex.org/W2333981896","https://openalex.org/W3005117407","https://openalex.org/W3124401063","https://openalex.org/W3124986135","https://openalex.org/W4300402905","https://openalex.org/W7072425790"],"related_works":["https://openalex.org/W3121451151","https://openalex.org/W2372876907","https://openalex.org/W1996655716","https://openalex.org/W3124905861","https://openalex.org/W2464186835","https://openalex.org/W2370586879","https://openalex.org/W3164428676","https://openalex.org/W4386802740","https://openalex.org/W1986811129","https://openalex.org/W3009375958"],"abstract_inverted_index":{"The":[0,11],"frequent":[1],"ups":[2],"and":[3,116,121,141],"downs":[4],"are":[5],"characteristic":[6],"to":[7,25,43,45,54,104,145],"the":[8,27,30,65,71,77,98,123],"stock":[9,31,66,78,106],"market.":[10,67],"conventional":[12],"standard":[13],"models":[14,110],"that":[15,17,87,138],"assume":[16],"investors":[18],"act":[19],"rationally":[20],"have":[21,146],"not":[22],"been":[23],"able":[24],"capture":[26,55],"irregularities":[28],"in":[29,63,92],"market":[32,79,107],"patterns":[33],"for":[34],"years.":[35],"As":[36],"a":[37,83,101,113,129],"result,":[38],"behavioural":[39],"finance":[40],"is":[41],"embraced":[42],"attempt":[44],"correct":[46],"these":[47],"model":[48],"shortcomings":[49],"by":[50,81,111],"adding":[51],"some":[52],"factors":[53],"sentimental":[56],"contagion":[57],"which":[58],"may":[59],"be":[60],"at":[61],"play":[62],"determining":[64],"This":[68],"paper":[69,99],"assesses":[70],"predictive":[72,109,148],"influence":[73],"of":[74,125],"sentiment":[75,143],"on":[76],"returns":[80],"using":[82],"non-parametric":[84],"nonlinear":[85],"approach":[86,103],"corrects":[88],"specific":[89],"limitations":[90],"encountered":[91],"previous":[93],"related":[94],"work.":[95],"In":[96],"addition,":[97],"proposes":[100],"new":[102],"developing":[105],"volatility":[108,140],"incorporating":[112],"hybrid":[114],"GARCH":[115,130],"artificial":[117],"neural":[118],"network":[119],"framework,":[120],"proves":[122],"advantage":[124],"this":[126],"framework":[127],"over":[128,150],"only":[131],"based":[132],"framework.":[133],"Our":[134],"results":[135],"reveal":[136],"also":[137],"past":[139],"positive":[142],"appear":[144],"strong":[147],"power":[149],"future":[151],"volatility.":[152]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}