{"id":"https://openalex.org/W3169546924","doi":"https://doi.org/10.1109/tcss.2021.3073964","title":"A Hybrid Approach of Bayesian Structural Time Series With LSTM to Identify the Influence of News Sentiment on Short-Term Forecasting of Stock Price","display_name":"A Hybrid Approach of Bayesian Structural Time Series With LSTM to Identify the Influence of News Sentiment on Short-Term Forecasting of Stock Price","publication_year":2021,"publication_date":"2021-06-09","ids":{"openalex":"https://openalex.org/W3169546924","doi":"https://doi.org/10.1109/tcss.2021.3073964","mag":"3169546924"},"language":"en","primary_location":{"id":"doi:10.1109/tcss.2021.3073964","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcss.2021.3073964","pdf_url":null,"source":{"id":"https://openalex.org/S2490693980","display_name":"IEEE Transactions on Computational Social Systems","issn_l":"2329-924X","issn":["2329-924X","2373-7476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Computational Social Systems","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/A5102723567","display_name":"Paramita Ray","orcid":"https://orcid.org/0000-0001-8087-1807"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Paramita Ray","raw_affiliation_strings":["Dinabandhu Andrews Institute of Technology and Management, Patuli, India"],"affiliations":[{"raw_affiliation_string":"Dinabandhu Andrews Institute of Technology and Management, Patuli, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039683896","display_name":"Bhaswati Ganguli","orcid":null},"institutions":[{"id":"https://openalex.org/I106542073","display_name":"University of Calcutta","ror":"https://ror.org/01e7v7w47","country_code":"IN","type":"education","lineage":["https://openalex.org/I106542073"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Bhaswati Ganguli","raw_affiliation_strings":["University of Calcutta, Kolkata, India"],"affiliations":[{"raw_affiliation_string":"University of Calcutta, Kolkata, India","institution_ids":["https://openalex.org/I106542073"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043543748","display_name":"Amlan Chakrabarti","orcid":"https://orcid.org/0000-0003-4380-3172"},"institutions":[{"id":"https://openalex.org/I106542073","display_name":"University of Calcutta","ror":"https://ror.org/01e7v7w47","country_code":"IN","type":"education","lineage":["https://openalex.org/I106542073"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Amlan Chakrabarti","raw_affiliation_strings":["A. K. Choudhury School of Information Technology, University of Calcutta, Kolkata, India"],"affiliations":[{"raw_affiliation_string":"A. K. Choudhury School of Information Technology, University of Calcutta, Kolkata, India","institution_ids":["https://openalex.org/I106542073"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5102723567"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.8798,"has_fulltext":false,"cited_by_count":42,"citation_normalized_percentile":{"value":0.95264293,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"8","issue":"5","first_page":"1153","last_page":"1162"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9994999766349792,"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":0.9994999766349792,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.996399998664856,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.7145928144454956},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6572389602661133},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.634128749370575},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.5626627802848816},{"id":"https://openalex.org/keywords/vector-autoregression","display_name":"Vector autoregression","score":0.5164477825164795},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5160014629364014},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.5153957605361938},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4844067692756653},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44080328941345215},{"id":"https://openalex.org/keywords/financial-market","display_name":"Financial market","score":0.4225956201553345},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3435118496417999},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3213275372982025},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.22697600722312927},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.20481646060943604}],"concepts":[{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.7145928144454956},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6572389602661133},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.634128749370575},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5626627802848816},{"id":"https://openalex.org/C133029050","wikidata":"https://www.wikidata.org/wiki/Q385593","display_name":"Vector autoregression","level":2,"score":0.5164477825164795},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5160014629364014},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.5153957605361938},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4844067692756653},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44080328941345215},{"id":"https://openalex.org/C19244329","wikidata":"https://www.wikidata.org/wiki/Q208697","display_name":"Financial market","level":2,"score":0.4225956201553345},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3435118496417999},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3213275372982025},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.22697600722312927},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.20481646060943604},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C2780762169","wikidata":"https://www.wikidata.org/wiki/Q5905368","display_name":"Horse","level":2,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcss.2021.3073964","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcss.2021.3073964","pdf_url":null,"source":{"id":"https://openalex.org/S2490693980","display_name":"IEEE Transactions on Computational Social Systems","issn_l":"2329-924X","issn":["2329-924X","2373-7476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Computational Social Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W5709642","https://openalex.org/W15334911","https://openalex.org/W20761671","https://openalex.org/W137110188","https://openalex.org/W1541382389","https://openalex.org/W1573937741","https://openalex.org/W1598816125","https://openalex.org/W1600957637","https://openalex.org/W1666386643","https://openalex.org/W1683055710","https://openalex.org/W1711412747","https://openalex.org/W1793984240","https://openalex.org/W1969852690","https://openalex.org/W1970978220","https://openalex.org/W1999974018","https://openalex.org/W2005570377","https://openalex.org/W2008151606","https://openalex.org/W2013255028","https://openalex.org/W2069300606","https://openalex.org/W2069539533","https://openalex.org/W2078639378","https://openalex.org/W2080248829","https://openalex.org/W2081375810","https://openalex.org/W2091442045","https://openalex.org/W2108765529","https://openalex.org/W2123442489","https://openalex.org/W2141409867","https://openalex.org/W2149223397","https://openalex.org/W2157765050","https://openalex.org/W2158518844","https://openalex.org/W2171468534","https://openalex.org/W2173361515","https://openalex.org/W2295803031","https://openalex.org/W2296719434","https://openalex.org/W2472119793","https://openalex.org/W2522803339","https://openalex.org/W2584276464","https://openalex.org/W2603718856","https://openalex.org/W2620661538","https://openalex.org/W2740129552","https://openalex.org/W2765486244","https://openalex.org/W2797283471","https://openalex.org/W2910068345","https://openalex.org/W3033381105","https://openalex.org/W3104712019","https://openalex.org/W3126053622","https://openalex.org/W4233787372","https://openalex.org/W4238015707","https://openalex.org/W4244217408","https://openalex.org/W4248506559","https://openalex.org/W6605631014","https://openalex.org/W6634392663","https://openalex.org/W6685536428","https://openalex.org/W6720330311","https://openalex.org/W6727629398","https://openalex.org/W6741966319","https://openalex.org/W6758101687"],"related_works":["https://openalex.org/W3196086590","https://openalex.org/W3175321409","https://openalex.org/W4312561791","https://openalex.org/W2389894046","https://openalex.org/W2215717369","https://openalex.org/W2974356760","https://openalex.org/W3197822338","https://openalex.org/W4312309719","https://openalex.org/W4313123484","https://openalex.org/W2146461990"],"abstract_inverted_index":{"In":[0],"the":[1,4,38,46,72,90,98,108,111,115,122,144,154,158,163,187,198,219],"financial":[2,35,64],"sector,":[3],"stock":[5,52,188],"market":[6,53,175],"and":[7,22,51,63,83,97,211,228],"its":[8],"trends":[9],"are":[10],"highly":[11],"volatile":[12],"in":[13,143],"nature.":[14],"Recent":[15],"studies":[16],"have":[17,27],"shown":[18],"that":[19,79,136,166],"news":[20,49,59,171],"articles":[21],"social":[23],"media":[24],"analysis":[25],"can":[26,137],"an":[28,69],"immense":[29],"impact":[30],"on":[31],"investors\u2019":[32],"opinion":[33],"toward":[34],"markets.":[36],"Thus,":[37],"purpose":[39],"of":[40,71,87,110,114,118,124,186,218],"this":[41,119],"study":[42,67],"is":[43,80,121,132],"to":[44,173,197],"explore":[45],"relationship":[47],"between":[48],"sentiment":[50],"movement":[54],"using":[55,104],"information":[56,106,168],"from":[57,169],"different":[58,170],"agencies,":[60],"business":[61],"magazines,":[62],"portals.":[65],"This":[66],"offers":[68],"application":[70],"Bayesian":[73],"structural":[74],"time":[75],"(BST)":[76],"series":[77],"model":[78,96,120,131,135,156,160,179,194,204],"more":[81],"transparent":[82],"facilitates":[84],"better":[85],"handling":[86],"uncertainty":[88],"than":[89],"autoregressive":[91],"integrated":[92],"moving":[93],"average":[94],"(ARIMA)":[95],"vector":[99],"autoregression":[100],"(VAR)":[101],"method":[102],"by":[103],"prior":[105],"about":[107],"structure":[109],"model.":[112,230],"One":[113],"main":[116],"pitfalls":[117],"presumption":[123],"linearity.":[125],"The":[126,177],"long":[127],"short-term":[128],"memory":[129],"(LSTM)":[130],"a":[133,149,213],"nonlinear":[134,140],"capture":[138],"various":[139],"structures":[141],"present":[142],"data":[145],"set.":[146],"We":[147],"propose":[148],"hybrid":[150,203,222],"model,":[151],"which":[152,191],"combines":[153],"LSTM":[155],"with":[157,162,207],"BST":[159],"along":[161],"regression":[164],"component":[165],"captures":[167],"sources":[172],"identify":[174],"predictors.":[176],"proposed":[178],"detects":[180],"unusual":[181],"behavior":[182],"or":[183],"anomalous":[184],"pattern":[185],"price":[189],"movement,":[190],"makes":[192],"our":[193],"superior":[195],"compared":[196],"traditional":[199],"methods.":[200],"Our":[201],"new":[202],"accumulates":[205],"error":[206],"lower":[208],"rates":[209],"(3.5%)":[210],"shows":[212],"remarkable":[214],"performance":[215],"over":[216],"some":[217],"other":[220],"existing":[221],"models,":[223],"such":[224],"as":[225],"AR-MLP,":[226],"ARIMA-LSTM,":[227],"VAR-LSTM":[229]},"counts_by_year":[{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}