{"id":"https://openalex.org/W4386617330","doi":"https://doi.org/10.1108/k-02-2023-0286","title":"Stock price index prediction based on SSA-BiGRU-GSCV model from the perspective of long memory","display_name":"Stock price index prediction based on SSA-BiGRU-GSCV model from the perspective of long memory","publication_year":2023,"publication_date":"2023-09-11","ids":{"openalex":"https://openalex.org/W4386617330","doi":"https://doi.org/10.1108/k-02-2023-0286"},"language":"en","primary_location":{"id":"doi:10.1108/k-02-2023-0286","is_oa":false,"landing_page_url":"https://doi.org/10.1108/k-02-2023-0286","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/A5109637537","display_name":"Zengli Mao","orcid":null},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zengli Mao","raw_affiliation_strings":["School of Management, Harbin Institute of Technology, Harbin, China"],"raw_orcid":"https://orcid.org/0000-0003-1017-4496","affiliations":[{"raw_affiliation_string":"School of Management, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090943151","display_name":"Chong Wu","orcid":"https://orcid.org/0009-0002-7816-6660"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chong Wu","raw_affiliation_strings":["School of Management, Harbin Institute of Technology, Harbin, China"],"raw_orcid":"https://orcid.org/0009-0002-7816-6660","affiliations":[{"raw_affiliation_string":"School of Management, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I204983213"],"apc_list":null,"apc_paid":null,"fwci":1.2566,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.86474249,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"53","issue":"12","first_page":"5905","last_page":"5931"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","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"}},"topics":[{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9984999895095825,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9973999857902527,"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/predictability","display_name":"Predictability","score":0.8099762201309204},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6210724115371704},{"id":"https://openalex.org/keywords/stock-market-index","display_name":"Stock market index","score":0.5483891367912292},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.5438570976257324},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.5284043550491333},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.48424264788627625},{"id":"https://openalex.org/keywords/composite-index","display_name":"Composite index","score":0.47460049390792847},{"id":"https://openalex.org/keywords/index","display_name":"Index (typography)","score":0.4445129632949829},{"id":"https://openalex.org/keywords/capitalization-weighted-index","display_name":"Capitalization-weighted index","score":0.42860960960388184},{"id":"https://openalex.org/keywords/cost-price","display_name":"Cost price","score":0.42798054218292236},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.31725481152534485},{"id":"https://openalex.org/keywords/stock-exchange","display_name":"Stock exchange","score":0.28768789768218994},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.2047128677368164},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18120521306991577},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.15390002727508545}],"concepts":[{"id":"https://openalex.org/C197640229","wikidata":"https://www.wikidata.org/wiki/Q2534066","display_name":"Predictability","level":2,"score":0.8099762201309204},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6210724115371704},{"id":"https://openalex.org/C88389905","wikidata":"https://www.wikidata.org/wiki/Q223371","display_name":"Stock market index","level":4,"score":0.5483891367912292},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.5438570976257324},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.5284043550491333},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.48424264788627625},{"id":"https://openalex.org/C2778098375","wikidata":"https://www.wikidata.org/wiki/Q19596433","display_name":"Composite index","level":3,"score":0.47460049390792847},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.4445129632949829},{"id":"https://openalex.org/C192737826","wikidata":"https://www.wikidata.org/wiki/Q5035802","display_name":"Capitalization-weighted index","level":5,"score":0.42860960960388184},{"id":"https://openalex.org/C47143671","wikidata":"https://www.wikidata.org/wiki/Q1450203","display_name":"Cost price","level":4,"score":0.42798054218292236},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.31725481152534485},{"id":"https://openalex.org/C200870193","wikidata":"https://www.wikidata.org/wiki/Q11691","display_name":"Stock exchange","level":2,"score":0.28768789768218994},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.2047128677368164},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18120521306991577},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15390002727508545},{"id":"https://openalex.org/C2780762169","wikidata":"https://www.wikidata.org/wiki/Q5905368","display_name":"Horse","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/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1108/k-02-2023-0286","is_oa":false,"landing_page_url":"https://doi.org/10.1108/k-02-2023-0286","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1682599076","https://openalex.org/W1993966239","https://openalex.org/W2015390758","https://openalex.org/W2038993396","https://openalex.org/W2058805224","https://openalex.org/W2156397254","https://openalex.org/W2765945751","https://openalex.org/W2890227220","https://openalex.org/W2897733922","https://openalex.org/W2924689854","https://openalex.org/W2949985842","https://openalex.org/W2989199981","https://openalex.org/W2998475677","https://openalex.org/W3008246618","https://openalex.org/W3017051726","https://openalex.org/W3018933895","https://openalex.org/W3049611042","https://openalex.org/W3080113688","https://openalex.org/W3083756696","https://openalex.org/W3109307714","https://openalex.org/W3113357550","https://openalex.org/W3130456109","https://openalex.org/W3133875174","https://openalex.org/W3177944252","https://openalex.org/W3197486829","https://openalex.org/W3215163093","https://openalex.org/W4205374700","https://openalex.org/W4205807146","https://openalex.org/W4210928019","https://openalex.org/W4224233911","https://openalex.org/W4283746258","https://openalex.org/W4289076482","https://openalex.org/W4312075914","https://openalex.org/W4312268671","https://openalex.org/W4316654893","https://openalex.org/W4320915185","https://openalex.org/W4324052837"],"related_works":["https://openalex.org/W1971081312","https://openalex.org/W2133325433","https://openalex.org/W3123252458","https://openalex.org/W4283826746","https://openalex.org/W2539018841","https://openalex.org/W4386639254","https://openalex.org/W2391550732","https://openalex.org/W3191555699","https://openalex.org/W3186974393","https://openalex.org/W2057927238"],"abstract_inverted_index":{"Purpose":[0],"Because":[1],"the":[2,6,25,28,43,50,61,72,97,146,166,177,184,218,222,228,237,247,265,268,272,276,296,318],"dynamic":[3],"characteristics":[4],"of":[5,27,156,180,249,254,267,373],"stock":[7,15,29,54,73,99,147,193,212,223,255,277,284,319],"market":[8,224,306],"are":[9,132,174,329],"nonlinear,":[10],"it":[11],"is":[12,141,231,286,303],"unclear":[13],"whether":[14],"prices":[16,285],"can":[17,164,190,323],"be":[18,324],"predicted.":[19,325],"This":[20],"paper":[21,57,349],"aims":[22,58],"to":[23,41,59,95,199,264,294,305],"explore":[24,49],"predictability":[26],"price":[30,46,74,100,148,194,256,320,327],"index":[31,47,75,149,195,219,321],"from":[32,176],"a":[33,68,115,206,242],"long-memory":[34,226],"perspective.":[35],"The":[36,56,65,102,151,171,188,252,292,343,367],"authors":[37,66,368],"propose":[38,84],"hybrid":[39,92],"models":[40,377],"predict":[42,96,192],"next-day":[44,98],"closing":[45],"and":[48,80,83,119,137,161,204,271,299,309,331,333,336,359],"policy":[51,229],"effects":[52,266],"behind":[53],"prices.":[55],"discuss":[60],"aforementioned":[62],"ideas.":[63],"Design/methodology/approach":[64],"found":[67],"long":[69,133,142,315],"memory":[70,143,316,357],"in":[71,145,183,221,236,246,258,347],"series":[76,220,322],"using":[77],"modified":[78],"R/S":[79],"GPH":[81],"tests,":[82],"an":[85,121],"improved":[86],"bi-directional":[87],"gated":[88],"recurrent":[89],"units":[90],"(BiGRU)":[91],"network":[93,344,365],"framework":[94,104,153,345],"index.":[101],"proposed":[103,152,346],"integrates":[105],"(1)":[106],"A":[107],"de-noising":[108,160],"module\u2014Singular":[109],"Spectrum":[110],"Analysis":[111],"(SSA)":[112],"algorithm,":[113],"(2)":[114],"predictive":[116],"module\u2014BiGRU":[117],"model,":[118],"(3)":[120],"optimization":[122,163],"module\u2014Grid":[123],"Search":[124],"Cross-validation":[125],"(GSCV)":[126],"algorithm.":[127],"Findings":[128],"Three":[129],"critical":[130,287],"findings":[131],"memory,":[134],"fit":[135],"effectiveness":[136],"model":[138,167,189],"optimization.":[139],"There":[140],"(predictability)":[144],"series.":[150],"yields":[154],"predictions":[155],"optimum":[157],"fit.":[158,168],"Data":[159],"parameter":[162],"improve":[165],"Practical":[169],"implications":[170,216],"empirical":[172],"data":[173,179],"obtained":[175],"financial":[178,290],"listed":[181],"companies":[182,308],"Wind":[185],"Financial":[186],"Terminal.":[187],"accurately":[191],"series,":[196],"guide":[197],"investors":[198],"make":[200],"reasonable":[201],"investment":[202,213],"decisions,":[203],"provide":[205,340],"basis":[207],"for":[208,241,288],"establishing":[209],"individual":[210],"industry":[211],"strategies.":[214],"Social":[215],"If":[217],"exhibits":[225],"characteristics,":[227],"implication":[230],"that":[232],"fractal":[233],"markets,":[234],"even":[235],"nonlinear":[238],"case,":[239],"allow":[240],"corresponding":[243],"distribution":[244],"pattern":[245],"value":[248],"portfolio":[250],"assets.":[251],"risk":[253],"volatility":[257],"various":[259],"sectors":[260],"has":[261,350],"expanded":[262],"due":[263],"COVID-19":[269],"pandemic":[270],"R-U":[273],"conflict":[274],"on":[275,378],"market.":[278],"Predicting":[279],"future":[280],"trends":[281],"by":[282],"forecasting":[283],"minimizing":[289],"risk.":[291],"ability":[293,362],"mitigate":[295],"epidemic\u2019s":[297],"impact":[298],"stop":[300],"losses":[301],"promptly":[302],"relevant":[304,311],"regulators,":[307],"other":[310],"stakeholders.":[312],"Originality/value":[313],"Although":[314],"exists,":[317],"However,":[326],"fluctuations":[328],"unstable":[330],"chaotic,":[332],"traditional":[334,364],"mathematical":[335],"statistical":[337],"methods":[338],"cannot":[339],"precise":[341],"predictions.":[342],"this":[348],"robust":[351],"horizontal":[352],"connections":[353],"between":[354],"units,":[355],"strong":[356],"capability":[358],"stronger":[360],"generalization":[361],"than":[363],"structures.":[366],"demonstrate":[369],"significant":[370],"performance":[371],"improvements":[372],"SSA-BiGRU-GSCV":[374],"over":[375],"comparison":[376],"Chinese":[379],"stocks.":[380]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3}],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2025-10-10T00:00:00"}