{"id":"https://openalex.org/W3214241962","doi":"https://doi.org/10.1108/k-06-2021-0457","title":"Combined deep learning classifiers for stock market prediction: integrating stock price and news sentiments","display_name":"Combined deep learning classifiers for stock market prediction: integrating stock price and news sentiments","publication_year":2021,"publication_date":"2021-11-08","ids":{"openalex":"https://openalex.org/W3214241962","doi":"https://doi.org/10.1108/k-06-2021-0457","mag":"3214241962"},"language":"en","primary_location":{"id":"doi:10.1108/k-06-2021-0457","is_oa":false,"landing_page_url":"https://doi.org/10.1108/k-06-2021-0457","pdf_url":null,"source":{"id":"https://openalex.org/S168682784","display_name":"Kybernetes","issn_l":"0368-492X","issn":["0368-492X","1758-7883"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Kybernetes","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/A5043441071","display_name":"B L Shilpa","orcid":null},"institutions":[{"id":"https://openalex.org/I4210089092","display_name":"GS Engineering (United States)","ror":"https://ror.org/007anfg84","country_code":"US","type":"company","lineage":["https://openalex.org/I4210089092"]},{"id":"https://openalex.org/I4210137436","display_name":"JSS Science and Technology University","ror":"https://ror.org/04mnmkz07","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210137436"]}],"countries":["IN","US"],"is_corresponding":true,"raw_author_name":"Shilpa B L","raw_affiliation_strings":["GSSSIETW Computer Science and Engineering, , ,","Computer Science and Engineering, GSSSIETW, Mysuru, India"],"affiliations":[{"raw_affiliation_string":"GSSSIETW Computer Science and Engineering, , ,","institution_ids":["https://openalex.org/I4210089092"]},{"raw_affiliation_string":"Computer Science and Engineering, GSSSIETW, Mysuru, India","institution_ids":["https://openalex.org/I4210137436"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038059877","display_name":"B R Shambhavi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210147455","display_name":"Institute of Information Science","ror":"https://ror.org/055q8rh54","country_code":"SI","type":"education","lineage":["https://openalex.org/I4210147455"]}],"countries":["SI"],"is_corresponding":false,"raw_author_name":"Shambhavi B R","raw_affiliation_strings":["BMSCE Information Science and Engineering, , ,","Information Science and Engineering, BMSCE, Bengaluru, India"],"affiliations":[{"raw_affiliation_string":"BMSCE Information Science and Engineering, , ,","institution_ids":["https://openalex.org/I4210147455"]},{"raw_affiliation_string":"Information Science and Engineering, BMSCE, Bengaluru, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5043441071"],"corresponding_institution_ids":["https://openalex.org/I4210089092","https://openalex.org/I4210137436"],"apc_list":null,"apc_paid":null,"fwci":3.9384,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.93674809,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"52","issue":"3","first_page":"748","last_page":"773"},"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/T10047","display_name":"Financial Markets and Investment Strategies","score":0.9904999732971191,"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/T11059","display_name":"Market Dynamics and Volatility","score":0.9833999872207642,"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/sentiment-analysis","display_name":"Sentiment analysis","score":0.7008838057518005},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.652743935585022},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.6068925857543945},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.5831196308135986},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.542441725730896},{"id":"https://openalex.org/keywords/stock-price","display_name":"Stock price","score":0.4660668671131134},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46471938490867615},{"id":"https://openalex.org/keywords/stock-market-prediction","display_name":"Stock market prediction","score":0.4587540626525879},{"id":"https://openalex.org/keywords/technical-analysis","display_name":"Technical analysis","score":0.4440877437591553},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4356311559677124},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3748399019241333},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35685738921165466},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.2302229106426239},{"id":"https://openalex.org/keywords/financial-economics","display_name":"Financial economics","score":0.22925031185150146}],"concepts":[{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.7008838057518005},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.652743935585022},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.6068925857543945},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.5831196308135986},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.542441725730896},{"id":"https://openalex.org/C2988984586","wikidata":"https://www.wikidata.org/wiki/Q1020013","display_name":"Stock price","level":3,"score":0.4660668671131134},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46471938490867615},{"id":"https://openalex.org/C2776256503","wikidata":"https://www.wikidata.org/wiki/Q7617906","display_name":"Stock market prediction","level":4,"score":0.4587540626525879},{"id":"https://openalex.org/C117245426","wikidata":"https://www.wikidata.org/wiki/Q235038","display_name":"Technical analysis","level":2,"score":0.4440877437591553},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4356311559677124},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3748399019241333},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35685738921165466},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.2302229106426239},{"id":"https://openalex.org/C106159729","wikidata":"https://www.wikidata.org/wiki/Q2294553","display_name":"Financial economics","level":1,"score":0.22925031185150146},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"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/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1108/k-06-2021-0457","is_oa":false,"landing_page_url":"https://doi.org/10.1108/k-06-2021-0457","pdf_url":null,"source":{"id":"https://openalex.org/S168682784","display_name":"Kybernetes","issn_l":"0368-492X","issn":["0368-492X","1758-7883"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Kybernetes","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W746481651","https://openalex.org/W883434633","https://openalex.org/W1259090559","https://openalex.org/W1988566638","https://openalex.org/W2077913352","https://openalex.org/W2121571815","https://openalex.org/W2134532107","https://openalex.org/W2159058417","https://openalex.org/W2290883490","https://openalex.org/W2327031957","https://openalex.org/W2546350184","https://openalex.org/W2550277654","https://openalex.org/W2552885812","https://openalex.org/W2584403383","https://openalex.org/W2587000990","https://openalex.org/W2599008208","https://openalex.org/W2853380097","https://openalex.org/W2890354514","https://openalex.org/W2890938971","https://openalex.org/W2911979743","https://openalex.org/W2914672588","https://openalex.org/W2922854612","https://openalex.org/W2949202718","https://openalex.org/W2962809314","https://openalex.org/W2965613425","https://openalex.org/W2966861509","https://openalex.org/W2967723546","https://openalex.org/W2979656568","https://openalex.org/W2979835384","https://openalex.org/W2983557821","https://openalex.org/W2989876423","https://openalex.org/W2990318439","https://openalex.org/W2996472320","https://openalex.org/W3005880472","https://openalex.org/W3007377825","https://openalex.org/W3009012433","https://openalex.org/W3009550266","https://openalex.org/W3023211633","https://openalex.org/W3047814101","https://openalex.org/W3087417123","https://openalex.org/W3091271518","https://openalex.org/W3096727623","https://openalex.org/W3126271061","https://openalex.org/W3126444236","https://openalex.org/W3126639052","https://openalex.org/W3127096806","https://openalex.org/W3128491619","https://openalex.org/W3129648733","https://openalex.org/W3168928417","https://openalex.org/W3175102871","https://openalex.org/W4237700447","https://openalex.org/W4240575149","https://openalex.org/W4249385098","https://openalex.org/W4249680802","https://openalex.org/W4253598439"],"related_works":["https://openalex.org/W3170278308","https://openalex.org/W4283371150","https://openalex.org/W4313268783","https://openalex.org/W3093992164","https://openalex.org/W2886395332","https://openalex.org/W3022211661","https://openalex.org/W4385787967","https://openalex.org/W4251964078","https://openalex.org/W2994645096","https://openalex.org/W3184378686"],"abstract_inverted_index":{"Purpose":[0],"Stock":[1],"market":[2,23,707],"forecasters":[3],"are":[4,121,157,181,190,297,693],"focusing":[5],"to":[6,28,35,84,123,192,275,486,701],"create":[7],"a":[8,58,71,90,143,661],"positive":[9],"approach":[10],"for":[11,50,461,503,524,534,603,623,705],"predicting":[12,251],"the":[13,30,37,43,55,61,65,75,114,125,130,135,150,161,184,187,194,241,246,252,254,277,281,293,300,307,313,375,386,406,434,440,466,468,471,487,528,532,562,568,571,587,611,670,681,696],"stock":[14,22,39,44,62,78,136,151,162,278,284,322,671,682,706],"price.":[15],"The":[16,304,321,506],"fundamental":[17],"principle":[18],"of":[19,67,77,93,256,283,295,306,318,339,453,470,518,570,610],"an":[20,68],"effective":[21],"prediction":[24,76,107,126,144,662],"is":[25,80,220,230,237,258,273,310],"not":[26],"only":[27,113],"produce":[29],"maximum":[31],"outcomes":[32],"but":[33],"also":[34,158,678],"reduce":[36],"unreliable":[38],"price":[40,137],"estimate.":[41],"In":[42,73,334,606],"market,":[45],"sentiment":[46,132,147,155,206,217,242,287,665,675],"analysis":[47,63],"enables":[48],"people":[49],"making":[51],"educated":[52],"decisions":[53],"regarding":[54],"investment":[56],"in":[57,101,316,343,355,368,451],"business.":[59],"Moreover,":[60,374],"identifies":[64],"business":[66],"organization":[69],"or":[70,117],"company.":[72],"fact,":[74],"prices":[79],"more":[81,127,248],"complex":[82],"due":[83],"high":[85],"volatile":[86],"nature":[87],"that":[88,239,279,443,567],"varies":[89],"large":[91],"range":[92],"investor":[94],"sentiment,":[95,253],"economic":[96],"and":[97,103,153,177,205,216,286,331,350,362,366,397,426,449,459,483,498,553,583,598,648,673,691],"political":[98],"factors,":[99],"changes":[100],"leadership":[102],"other":[104,627],"factors.":[105],"This":[106,140,657],"often":[108],"becomes":[109],"ineffective,":[110],"while":[111],"considering":[112],"historical":[115],"data":[116,152,156,189,285,672,676],"textural":[118],"information.":[119,138],"Attempts":[120],"made":[122],"make":[124,245],"precise":[128],"with":[129,134,669],"news":[131,154,188,290,674],"along":[133,668],"Design/methodology/approach":[139],"paper":[141,658],"introduces":[142],"framework":[145,663],"via":[146,664],"analysis.":[148,666],"Thereby,":[149,667],"considered.":[159,679],"From":[160,561,680],"data,":[163,683],"technical":[164,684],"indicator-based":[165],"features":[166,229,282,687],"like":[167,199,389,410,456,490,590,630,688],"moving":[168,178],"average":[169,179],"convergence":[170],"divergence":[171],"(MACD),":[172],"relative":[173],"strength":[174],"index":[175],"(RSI)":[176],"(MA)":[180],"extracted.":[182,231,694],"At":[183],"same":[185],"time,":[186],"processed":[191],"determine":[193],"sentiments":[195],"by":[196,261,299],"certain":[197,319,454],"processes":[198],"(1)":[200],"pre-processing,":[201],"where":[202,214,225],"keyword":[203,212],"extraction":[204],"categorization":[207,218],"process":[208,219],"takes":[209],"place;":[210],"(2)":[211],"extraction,":[213,224],"WordNet":[215],"done;":[221],"(3)":[222],"feature":[223],"Proposed":[226],"holoentropy":[227],"based":[228,438,686],"(4)":[232],"Classification,":[233],"deep":[234,269],"neural":[235],"network":[236,271],"used":[238,274],"returns":[240],"output.":[243],"To":[244],"system":[247],"accurate":[249],"on":[250,439],"training":[255],"NN":[257,377,391,394,398,411,416,421,427,473,492,495,499,508,535,541,547,554,573,592,595,599,613,631,636,642,649],"carried":[259],"out":[260],"self-improved":[262],"whale":[263],"optimization":[264,408],"algorithm":[265],"(SIWOA).":[266],"Finally,":[267],"optimized":[268],"belief":[270],"(DBN)":[272],"predict":[276],"considers":[280],"results":[288],"from":[289,445],"data.":[291],"Here,":[292],"weights":[294],"DBN":[296,379,413,418,423,429,475,510,537,543,549,556,575,615,633,638,644,651],"tuned":[298],"new":[301],"SIWOA.":[302],"Findings":[303],"performance":[305,435],"adopted":[308,376,472,612],"scheme":[309],"computed":[311,384],"over":[312,385,405],"existing":[314,407,488,588,628],"models":[315,530],"terms":[317,452],"measures.":[320],"dataset":[323,504,525,604,624],"includes":[324],"two":[325],"companies":[326],"such":[327],"as":[328],"Reliance":[329],"Communications":[330],"Relaxo":[332],"Footwear.":[333],"addition,":[335,607],"each":[336],"company":[337],"consists":[338],"three":[340],"datasets":[341],"(a)":[342],"daily":[344],"option,":[345,357,370],"set":[346,358,371],"start":[347,359],"day":[348,352],"1-1-2019":[349],"end":[351,363],"1-12-2020,":[353],"(b)":[354],"monthly":[356],"Jan":[360],"2000":[361],"Dec":[364],"2020":[365],"(c)":[367],"yearly":[369],"year":[372],"2000.":[373],"+":[378,380,392,395,399,412,414,417,419,422,424,428,430,474,476,493,496,500,509,511,536,538,542,544,548,550,555,557,574,576,593,596,600,614,616,632,634,637,639,643,645,650,652],"SIWOA":[381,477,512,577,617],"model":[382,478,578],"was":[383,403,436,479,565,579,699],"traditional":[387,529],"classifiers":[388,489,589],"LSTM,":[390,491,591],"RF,":[393,494,594],"MLP":[396,497,597],"SVM;":[400],"also,":[401],"it":[402,564],"compared":[404],"algorithms":[409],"MFO,":[415],"CSA,":[420],"WOA":[425,558,653],"PSO,":[431],"correspondingly.":[432],"Further,":[433],"calculated":[437],"learning":[441,521],"percentage":[442,522],"ranges":[444],"60,":[446],"70,":[447],"80":[448,523],"90":[450],"measures":[455],"MAE,":[457],"MSE":[458,609],"RMSE":[460],"six":[462],"datasets.":[463],"On":[464],"observing":[465],"graph,":[467],"MAE":[469,516],"91.67,":[480],"80,":[481],"91.11":[482],"93.33%":[484],"superior":[485],"SVM,":[501,601],"respectively":[502],"1.":[505],"proposed":[507,572,697],"method":[513,618],"holds":[514,531],"minimum":[515],"value":[517,533],"(\u223c0.21)":[519],"at":[520],"1;":[526],"whereas,":[527],"CSA":[539],"(\u223c1.20),":[540],"MFO":[545,640],"(\u223c1.21),":[546],"PSO":[551,646],"(\u223c0.23)":[552],"(\u223c0.25),":[559],"respectively.":[560,655],"table,":[563],"clear":[566],"RMSRE":[569],"3.14,":[580],"1.08,":[581],"1.38":[582],"15.28%":[584],"better":[585],"than":[586,626],"respectively,":[602],"6.":[605],"he":[608],"attain":[619],"lower":[620],"values":[621],"(\u223c54944.41)":[622],"2":[625],"schemes":[629],"CSA(\u223c9.43),":[635],"(\u223c56728.68),":[641],"(\u223c2.95)":[647],"(\u223c56767.88),":[654],"Originality/value":[656],"has":[659],"introduced":[660],"were":[677],"indicator":[685],"MACD,":[689],"RSI":[690],"MA":[692],"Therefore,":[695],"work":[698],"said":[700],"be":[702],"much":[703],"appropriate":[704],"prediction.":[708]},"counts_by_year":[{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-20T20:47:17.329874","created_date":"2025-10-10T00:00:00"}
