{"id":"https://openalex.org/W2989657645","doi":null,"title":"Predicting a stock portfolio with the multivariate bayesian structural time series model: Do news or emotions matter?","display_name":"Predicting a stock portfolio with the multivariate bayesian structural time series model: Do news or emotions matter?","publication_year":2019,"publication_date":"2019-09-21","ids":{"openalex":"https://openalex.org/W2989657645","mag":"2989657645"},"language":"en","primary_location":{"id":"pmh:oai:escholarship.org/ark:/13030/qt47m0302b","doi":null,"is_oa":true,"landing_page_url":"https://escholarship.org/uc/item/47m0302b","pdf_url":"https://escholarship.org/uc/item/47m0302b","source":{"id":"https://openalex.org/S4306400115","display_name":"eScholarship (California Digital Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801248553","host_organization_name":"California Digital Library","host_organization_lineage":["https://openalex.org/I2801248553"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"International Journal of Artificial Intelligence, vol 17, iss 2","raw_type":"article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://escholarship.org/uc/item/47m0302b","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088761598","display_name":"S. Rao Jammalamadaka","orcid":"https://orcid.org/0000-0003-4479-4175","observed_orcids":["https://orcid.org/0000-0003-4479-4175"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"S. Rao Jammalamadaka","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-4479-4175","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031258295","display_name":"Jinwen Qiu","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jinwen Qiu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100749760","display_name":"Ning Ning","orcid":"https://orcid.org/0000-0002-8950-1279","observed_orcids":["https://orcid.org/0000-0002-8950-1279"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ning Ning","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-8950-1279","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.7682,"has_fulltext":true,"cited_by_count":34,"citation_normalized_percentile":{"value":0.95133116,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"17","issue":"2","first_page":"81","last_page":"104"},"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/T10047","display_name":"Financial Markets and Investment Strategies","score":0.00019999999494757503,"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"}},{"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/business-news","display_name":"business news","score":0.8389999866485596},{"id":"https://openalex.org/keywords/twitter-sentiment","display_name":"Twitter sentiment","score":0.777999997138977},{"id":"https://openalex.org/keywords/text-mining","display_name":"text mining","score":0.6729999780654907},{"id":"https://openalex.org/keywords/stock-price-prediction","display_name":"stock price prediction","score":0.746999979019165},{"id":"https://openalex.org/keywords/amazon","display_name":"Amazon","score":0.8180000185966492},{"id":"https://openalex.org/keywords/ebay","display_name":"eBay","score":0.996999979019165},{"id":"https://openalex.org/keywords/forecast-error-comparison","display_name":"forecast error comparison","score":0.4950000047683716},{"id":"https://openalex.org/keywords/arima","display_name":"ARIMA","score":0.7549999952316284},{"id":"https://openalex.org/keywords/lstm","display_name":"LSTM","score":0.8870000243186951}],"concepts":[{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.7934137582778931},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6659290790557861},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6542169451713562},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.6491393446922302},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6325843334197998},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5610144734382629},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5033525824546814},{"id":"https://openalex.org/C2780821815","wikidata":"https://www.wikidata.org/wiki/Q5340806","display_name":"Portfolio","level":2,"score":0.4988539218902588},{"id":"https://openalex.org/C197640229","wikidata":"https://www.wikidata.org/wiki/Q2534066","display_name":"Predictability","level":2,"score":0.4987635612487793},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.47853073477745056},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.425495445728302},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.411073237657547},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.24649614095687866},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1717640459537506},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.15116724371910095},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1383078396320343}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:escholarship.org/ark:/13030/qt47m0302b","doi":null,"is_oa":true,"landing_page_url":"https://escholarship.org/uc/item/47m0302b","pdf_url":"https://escholarship.org/uc/item/47m0302b","source":{"id":"https://openalex.org/S4306400115","display_name":"eScholarship (California Digital Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801248553","host_organization_name":"California Digital Library","host_organization_lineage":["https://openalex.org/I2801248553"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"International Journal of Artificial Intelligence, vol 17, iss 2","raw_type":"article"},{"id":"pmh:oai:escholarship.org:ark:/13030/qt47m0302b","doi":null,"is_oa":false,"landing_page_url":"https://escholarship.org/uc/item/47m0302b","pdf_url":null,"source":{"id":"https://openalex.org/S4306400115","display_name":"eScholarship (California Digital Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801248553","host_organization_name":"California Digital Library","host_organization_lineage":["https://openalex.org/I2801248553"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"International Journal of Artificial Intelligence, vol 17, iss 2","raw_type":"article"},{"id":"mag:2989657645","doi":null,"is_oa":false,"landing_page_url":"https://escholarship.org/uc/item/47m0302b","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"International Journal of Artificial Intelligence","raw_type":null}],"best_oa_location":{"id":"pmh:oai:escholarship.org/ark:/13030/qt47m0302b","doi":null,"is_oa":true,"landing_page_url":"https://escholarship.org/uc/item/47m0302b","pdf_url":"https://escholarship.org/uc/item/47m0302b","source":{"id":"https://openalex.org/S4306400115","display_name":"eScholarship (California Digital Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801248553","host_organization_name":"California Digital Library","host_organization_lineage":["https://openalex.org/I2801248553"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"International Journal of Artificial Intelligence, vol 17, iss 2","raw_type":"article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2989657645.pdf","grobid_xml":"https://content.openalex.org/works/W2989657645.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W1603903339","https://openalex.org/W1971259144","https://openalex.org/W1986078433","https://openalex.org/W1990709028","https://openalex.org/W2030559659","https://openalex.org/W2030888282","https://openalex.org/W2048347611","https://openalex.org/W2049303100","https://openalex.org/W2053043119","https://openalex.org/W2101618658","https://openalex.org/W2102146295","https://openalex.org/W2106156256","https://openalex.org/W2111051773","https://openalex.org/W2114463818","https://openalex.org/W2116512828","https://openalex.org/W2117922789","https://openalex.org/W2130189616","https://openalex.org/W2136755402","https://openalex.org/W2137527984","https://openalex.org/W2143050169","https://openalex.org/W2144276202","https://openalex.org/W2153040915","https://openalex.org/W2162635690","https://openalex.org/W2163453025","https://openalex.org/W2171468534","https://openalex.org/W2551393996","https://openalex.org/W2571446175","https://openalex.org/W2952076949","https://openalex.org/W2964010423","https://openalex.org/W3121260070"],"related_works":["https://openalex.org/W3169546924","https://openalex.org/W3194885286","https://openalex.org/W4221036632","https://openalex.org/W7017188311","https://openalex.org/W2938903045","https://openalex.org/W4408862006","https://openalex.org/W4385250194","https://openalex.org/W6903914323","https://openalex.org/W4361192923","https://openalex.org/W3200258120"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,67,221],"provide":[4],"methods":[5],"for":[6],"creatively":[7],"incorporating":[8],"information":[9],"from":[10],"financial":[11],"news":[12,213],"and":[13,78,80,115,141,185,201,207,238],"Twitter":[14],"feeds":[15],"into":[16],"predicting":[17],"the":[18,26,29,64,96,108,116,156,162,178,196,210,239,247],"prices":[19],"of":[20,23,28,57,63,98,103,130,180,198,212],"a":[21,39,55,99,217],"portfolio":[22],"stocks,":[24],"using":[25,54],"framework":[27],"Multivariate":[30],"Bayesian":[31,40,170],"Structural":[32],"Time":[33],"Series":[34],"(MBSTS)":[35],"model.":[36],"MBSTS":[37,240],"is":[38,155],"machine":[41,171,186],"learning":[42,172,187],"model":[43,121,154,241],"designed":[44],"to":[45,85,95,135,166],"capture":[46],"correlations":[47],"among":[48],"multiple":[49],"target":[50],"time":[51,173],"series,":[52],"while":[53],"number":[56],"contemporaneous":[58],"predictors.":[59],"As":[60],"an":[61,167],"illustration":[62],"current":[65],"model,":[66,114,175],"use":[68],"data":[69],"on":[70,216],"two":[71],"leading":[72],"online":[73,163],"commerce":[74],"companies,":[75],"namely":[76],"Amazon":[77],"eBay,":[79],"run":[81],"extensive":[82],"empirical":[83],"experiments":[84],"examine":[86],"which":[87,176],"if":[88],"any,":[89],"text":[90,164,183],"mining":[91,165,184],"predictors":[92,203,230,244],"would":[93],"add":[94],"predictability":[97],"stock":[100],"price.":[101],"Evaluation":[102],"competing":[104],"models":[105,225,233],"such":[106],"as":[107],"autoregressive":[109],"integrated":[110],"moving":[111],"average":[112],"(ARIMA)":[113],"recurrent":[117],"neural":[118],"network":[119],"(RNN)":[120],"with":[122,133,140,228,242],"long":[123],"short":[124],"term":[125],"memory":[126],"(LSTM),":[127],"in":[128,189,204],"terms":[129],"their":[131],"performances":[132],"respect":[134],"cumulative":[136],"one-step-ahead":[137],"forecast":[138],"errors":[139],"without":[142,234],"sentimental":[143,229,236,243],"predictors,":[144,237],"were":[145],"carried":[146],"out.":[147],"Our":[148],"contributions":[149],"are":[150],"threefold:":[151],"Firstly,":[152],"our":[153],"first":[157],"one":[158],"that":[159,223],"successfully":[160],"incorporated":[161],"advanced":[168],"multivariate":[169],"series":[174],"opens":[177],"door":[179],"applying":[181],"both":[182,199,205],"simultaneously":[188],"modern":[190,200],"quantitative":[191],"finance":[192],"research;":[193],"Secondly,":[194],"under":[195,226],"presence":[197],"classical":[202],"fundamental":[206],"technical":[208],"sense,":[209],"polarity":[211],"still":[214],"adds":[215],"complementary":[218],"effect;":[219],"Thirdly,":[220],"discover":[222],"all":[224,246],"investigation":[227],"do":[231],"outperform":[232],"these":[235],"outperforms":[245],"other":[248],"models.":[249]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":18},{"year":2020,"cited_by_count":5}],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2019-12-05T00:00:00"}
