{"id":"https://openalex.org/W3139035125","doi":"https://doi.org/10.1109/bigdata50022.2020.9378345","title":"BELT: A Pipeline for Stock Price Prediction Using News","display_name":"BELT: A Pipeline for Stock Price Prediction Using News","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3139035125","doi":"https://doi.org/10.1109/bigdata50022.2020.9378345","mag":"3139035125"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata50022.2020.9378345","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378345","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-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/A5074030269","display_name":"Yingzhe Dong","orcid":null},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yingzhe Dong","raw_affiliation_strings":["School of Business Administration, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"School of Business Administration, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070582509","display_name":"Da Yan","orcid":"https://orcid.org/0000-0002-4653-0408"},"institutions":[{"id":"https://openalex.org/I32389192","display_name":"University of Alabama at Birmingham","ror":"https://ror.org/008s83205","country_code":"US","type":"education","lineage":["https://openalex.org/I32389192"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Da Yan","raw_affiliation_strings":["Department of Computer Science, The University of Alabama at Birmingham, Birmingham, AL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, The University of Alabama at Birmingham, Birmingham, AL, USA","institution_ids":["https://openalex.org/I32389192"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032201681","display_name":"Abdullateef I. Almudaifer","orcid":null},"institutions":[{"id":"https://openalex.org/I32389192","display_name":"University of Alabama at Birmingham","ror":"https://ror.org/008s83205","country_code":"US","type":"education","lineage":["https://openalex.org/I32389192"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abdullateef Ibrahim Almudaifer","raw_affiliation_strings":["Department of Computer Science, The University of Alabama at Birmingham, Birmingham, AL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, The University of Alabama at Birmingham, Birmingham, AL, USA","institution_ids":["https://openalex.org/I32389192"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020660716","display_name":"Sibo Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I27103197","display_name":"Freddie Mac (United States)","ror":"https://ror.org/02ga7kh69","country_code":"US","type":"company","lineage":["https://openalex.org/I27103197"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sibo Yan","raw_affiliation_strings":["Freddie Mac, Washington, D.C., USA"],"affiliations":[{"raw_affiliation_string":"Freddie Mac, Washington, D.C., USA","institution_ids":["https://openalex.org/I27103197"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021681759","display_name":"Zhe Jiang","orcid":"https://orcid.org/0000-0002-3576-6976"},"institutions":[{"id":"https://openalex.org/I17301866","display_name":"University of Alabama","ror":"https://ror.org/03xrrjk67","country_code":"US","type":"education","lineage":["https://openalex.org/I17301866"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhe Jiang","raw_affiliation_strings":["Department of Computer Science, The University of Alabama, Tuscaloosa, AL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, The University of Alabama, Tuscaloosa, AL, USA","institution_ids":["https://openalex.org/I17301866"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089665236","display_name":"Yang Zhou","orcid":"https://orcid.org/0000-0002-1148-0666"},"institutions":[{"id":"https://openalex.org/I82497590","display_name":"Auburn University","ror":"https://ror.org/02v80fc35","country_code":"US","type":"education","lineage":["https://openalex.org/I82497590"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yang Zhou","raw_affiliation_strings":["Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA","institution_ids":["https://openalex.org/I82497590"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5074030269"],"corresponding_institution_ids":["https://openalex.org/I9224756"],"apc_list":null,"apc_paid":null,"fwci":2.2286,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.89016778,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1137","last_page":"1146"},"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9908000230789185,"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":0.9904000163078308,"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/stock","display_name":"Stock (firearms)","score":0.6360723376274109},{"id":"https://openalex.org/keywords/stock-price","display_name":"Stock price","score":0.5914170145988464},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5303053855895996},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5103952288627625},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.2526387572288513},{"id":"https://openalex.org/keywords/history","display_name":"History","score":0.10779297351837158},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.07874572277069092},{"id":"https://openalex.org/keywords/paleontology","display_name":"Paleontology","score":0.06642848253250122},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.06411698460578918},{"id":"https://openalex.org/keywords/archaeology","display_name":"Archaeology","score":0.05875754356384277}],"concepts":[{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.6360723376274109},{"id":"https://openalex.org/C2988984586","wikidata":"https://www.wikidata.org/wiki/Q1020013","display_name":"Stock price","level":3,"score":0.5914170145988464},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5303053855895996},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5103952288627625},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.2526387572288513},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.10779297351837158},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.07874572277069092},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.06642848253250122},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.06411698460578918},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.05875754356384277}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata50022.2020.9378345","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378345","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W2607045400","https://openalex.org/W2734777338","https://openalex.org/W2798413829","https://openalex.org/W2845688424","https://openalex.org/W2885054548","https://openalex.org/W2889928394","https://openalex.org/W2896457183","https://openalex.org/W2903501216","https://openalex.org/W2912036663","https://openalex.org/W2914120296","https://openalex.org/W2921829327","https://openalex.org/W2963341956","https://openalex.org/W2963403868","https://openalex.org/W2964110616","https://openalex.org/W2970049541","https://openalex.org/W2970597249","https://openalex.org/W2974142446","https://openalex.org/W2994673210","https://openalex.org/W2996428491","https://openalex.org/W3030163527","https://openalex.org/W3093733835","https://openalex.org/W3095114851","https://openalex.org/W3207029408","https://openalex.org/W4292779060","https://openalex.org/W4295838474","https://openalex.org/W4302438404","https://openalex.org/W4385245566","https://openalex.org/W6739901393","https://openalex.org/W6754779804","https://openalex.org/W6755207826","https://openalex.org/W6760513793","https://openalex.org/W6763701032","https://openalex.org/W6767737316","https://openalex.org/W6768021236","https://openalex.org/W6771626834","https://openalex.org/W6778883912"],"related_works":["https://openalex.org/W247222457","https://openalex.org/W3124131549","https://openalex.org/W3008476150","https://openalex.org/W2152348935","https://openalex.org/W2887069341","https://openalex.org/W2554106722","https://openalex.org/W1797892342","https://openalex.org/W2093710055","https://openalex.org/W2344827208","https://openalex.org/W4206714504"],"abstract_inverted_index":{"Stock":[0],"investment":[1,37],"is":[2,50,83,263,288],"a":[3,25,35,72,84,92,107,124,133,136,177,197,236],"vehicle":[4],"for":[5,24,56,87,282,295],"many":[6,45],"people":[7],"to":[8,21,68,105,114,127,176,188,206,216,234,241,247,265],"grow":[9],"their":[10,65],"wealth.":[11],"However,":[12,60],"market":[13,42,112],"downturns":[14],"can":[15,78],"cause":[16,79],"huge":[17,81],"losses":[18],"and":[19,75,90,215,226,238,257,284],"need":[20],"be":[22],"predicted":[23],"timely":[26,93,128],"sell.":[27],"In":[28],"fact,":[29],"with":[30],"effective":[31],"prediction,":[32,283],"stocks":[33,46],"are":[34,47,171,213,221,231,279],"good":[36],"even":[38],"during":[39],"periods":[40],"of":[41,54,95,118,192,223,292],"volatility":[43],"as":[44,174,246],"\"on":[48],"sale\".News":[49],"an":[51],"important":[52],"source":[53,86],"signal":[55],"stock":[57,61,98,144,155,186,194,200,255,267,297],"price":[58,156,224,277],"movement.":[59],"analysts":[62],"usually":[63],"adjust":[64],"analysis":[66],"according":[67],"the":[69,111,115,142,162,190,289,293],"news":[70,131,160,201,210,243,259,272],"in":[71],"subject":[73],"manner,":[74],"wrong":[76],"judgments":[77],"investors":[80],"losses.Twitter":[82],"great":[85,108],"breaking":[88],"news,":[89],"provides":[91],"stream":[94],"signals":[96,244],"on":[97,101,110,141,154,253],"trends.":[99],"News":[100],"Twitter":[102,119,130,159,258],"also":[103,183],"tends":[104],"have":[106],"impact":[109],"due":[113],"large":[116],"number":[117],"users.":[120],"This":[121],"paper":[122],"proposes":[123],"data-driven":[125,237],"pipeline":[126,240],"incorporate":[129,242],"about":[132],"company":[134],"into":[135],"time":[137],"series":[138],"prediction":[139],"model":[140,168,181,229],"company's":[143],"price.":[145,195],"Our":[146],"approach,":[147],"called":[148],"BERT-LSTM":[149],"(BELT),":[150],"extracts":[151],"informative":[152],"features":[153,219],"direction":[157,191],"from":[158],"using":[161],"state-of-the-art":[163],"natural":[164],"language":[165],"processing":[166],"(NLP)":[167],"BERT,":[169],"which":[170,287],"then":[172],"used":[173,280],"covariates":[175],"many-to-many":[178],"stacked":[179],"LSTM":[180],"that":[182,212,220,261],"utilizes":[184],"historical":[185,276],"prices":[187,256,268],"predict":[189,266],"future":[193],"Utilizing":[196],"carefully":[198],"curated":[199],"dataset,":[202],"we":[203],"fine-tune":[204],"BERT":[205],"effectively":[207],"identify":[208],"those":[209],"tweets":[211],"relevant,":[214],"extract":[217],"NLP":[218],"indicative":[222],"rises":[225],"falls.":[227],"All":[228],"parameters":[230],"trained":[232],"end-to-end":[233],"provide":[235],"objective":[239],"so":[245],"avoid":[248],"subjective":[249],"analysis.":[250],"Extensive":[251],"experiments":[252],"real":[254],"show":[260],"BELT":[262],"able":[264],"more":[269],"accurately":[270],"utilizing":[271],"information":[273],"than":[274],"if":[275],"data":[278],"alone":[281],"beats":[285],"StockNet":[286],"current":[290],"state":[291],"art":[294],"news-based":[296],"movement":[298],"prediction.":[299]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":3}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}
