{"id":"https://openalex.org/W4225632085","doi":"https://doi.org/10.1109/access.2022.3163723","title":"A Hybrid Prediction Model Integrating GARCH Models With a Distribution Manipulation Strategy Based on LSTM Networks for Stock Market Volatility","display_name":"A Hybrid Prediction Model Integrating GARCH Models With a Distribution Manipulation Strategy Based on LSTM Networks for Stock Market Volatility","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4225632085","doi":"https://doi.org/10.1109/access.2022.3163723"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3163723","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3163723","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09745535.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09745535.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070250870","display_name":"Eunho Koo","orcid":"https://orcid.org/0000-0001-9235-1876"},"institutions":[{"id":"https://openalex.org/I125608817","display_name":"Korea Institute for Advanced Study","ror":"https://ror.org/041hz9568","country_code":"KR","type":"facility","lineage":["https://openalex.org/I125608817"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Eunho Koo","raw_affiliation_strings":["Center for AI and Natural Sciences, Korea Institute for Advanced Study, Seoul, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for AI and Natural Sciences, Korea Institute for Advanced Study, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I125608817"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101498387","display_name":"Geonwoo Kim","orcid":"https://orcid.org/0000-0001-5965-2415"},"institutions":[{"id":"https://openalex.org/I118373667","display_name":"Seoul National University of Science and Technology","ror":"https://ror.org/00chfja07","country_code":"KR","type":"education","lineage":["https://openalex.org/I118373667"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Geonwoo Kim","raw_affiliation_strings":["School of Liberal Arts, Seoul National University of Science and Technology, Seoul, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0001-5965-2415","affiliations":[{"raw_affiliation_string":"School of Liberal Arts, Seoul National University of Science and Technology, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I118373667"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":9.6366,"has_fulltext":true,"cited_by_count":63,"citation_normalized_percentile":{"value":0.98613231,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"10","issue":null,"first_page":"34743","last_page":"34754"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9975000023841858,"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"}},{"id":"https://openalex.org/T10282","display_name":"Financial Risk and Volatility Modeling","score":0.9962000250816345,"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/volatility","display_name":"Volatility (finance)","score":0.7349832653999329},{"id":"https://openalex.org/keywords/autoregressive-conditional-heteroskedasticity","display_name":"Autoregressive conditional heteroskedasticity","score":0.7007312774658203},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6244308352470398},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5022337436676025},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5006818771362305},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.4385618567466736},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.39283692836761475},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.31333503127098083},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24984616041183472},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13903957605361938}],"concepts":[{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.7349832653999329},{"id":"https://openalex.org/C23922673","wikidata":"https://www.wikidata.org/wiki/Q180752","display_name":"Autoregressive conditional heteroskedasticity","level":3,"score":0.7007312774658203},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6244308352470398},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5022337436676025},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5006818771362305},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.4385618567466736},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.39283692836761475},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31333503127098083},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24984616041183472},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13903957605361938},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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":2,"locations":[{"id":"doi:10.1109/access.2022.3163723","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3163723","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09745535.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:334f9e80af9f460094c91be9f7e8b886","is_oa":true,"landing_page_url":"https://doaj.org/article/334f9e80af9f460094c91be9f7e8b886","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 10, Pp 34743-34754 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3163723","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3163723","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09745535.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.6000000238418579,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G2786181745","display_name":null,"funder_award_id":"NRF-2017R1E1A1A03070886","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G4651605180","display_name":null,"funder_award_id":"2017R1E1A1A03070886","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G7389944064","display_name":null,"funder_award_id":"AP086801","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4225632085.pdf","grobid_xml":"https://content.openalex.org/works/W4225632085.grobid-xml"},"referenced_works_count":42,"referenced_works":["https://openalex.org/W307184769","https://openalex.org/W1522301498","https://openalex.org/W1733533215","https://openalex.org/W1967885203","https://openalex.org/W1979575715","https://openalex.org/W1980287119","https://openalex.org/W1990412506","https://openalex.org/W1999814123","https://openalex.org/W1999996900","https://openalex.org/W2013109882","https://openalex.org/W2022695740","https://openalex.org/W2026318079","https://openalex.org/W2029126090","https://openalex.org/W2064675550","https://openalex.org/W2068527816","https://openalex.org/W2069143585","https://openalex.org/W2073188415","https://openalex.org/W2110242546","https://openalex.org/W2172164632","https://openalex.org/W2214006042","https://openalex.org/W2320529113","https://openalex.org/W2511771736","https://openalex.org/W2514033443","https://openalex.org/W2561462867","https://openalex.org/W2580977989","https://openalex.org/W2609559763","https://openalex.org/W2613300343","https://openalex.org/W2789364533","https://openalex.org/W2854999190","https://openalex.org/W2901176530","https://openalex.org/W2911494597","https://openalex.org/W2924657543","https://openalex.org/W2943209282","https://openalex.org/W2962752580","https://openalex.org/W2963221859","https://openalex.org/W2981537679","https://openalex.org/W3038833021","https://openalex.org/W3043008751","https://openalex.org/W3043661073","https://openalex.org/W3098184859","https://openalex.org/W6631190155","https://openalex.org/W6637242042"],"related_works":["https://openalex.org/W3121451151","https://openalex.org/W2372876907","https://openalex.org/W2102148524","https://openalex.org/W1996655716","https://openalex.org/W3124905861","https://openalex.org/W2464186835","https://openalex.org/W2370586879","https://openalex.org/W1964520514","https://openalex.org/W4386802740","https://openalex.org/W1986811129"],"abstract_inverted_index":{"Accurate":[0],"prediction":[1,74,95,223,237],"of":[2,6,47,53,56,60,80,130,146,173,208,230],"volatility":[3,61,148,172],"is":[4,63,162,176,183],"one":[5],"the":[7,16,33,51,54,77,87,98,108,133,156,165,170,179,186,198,205,209,219,226,236,241],"most":[8],"important":[9],"tasks":[10],"in":[11,86,225],"financial":[12,57],"decision":[13],"making.":[14],"Recently,":[15],"hybrid":[17,48,114,188,211],"models":[18,25,34,49,118,212],"integrating":[19,213],"artificial":[20],"neural":[21],"networks":[22,88],"with":[23,116,195,215],"GARCH-type":[24,117,216],"have":[26,35,43],"been":[27,36,44],"developed,":[28],"and":[29,68,143,169],"performance":[30,75,193,224],"gains":[31],"from":[32],"found":[37,184],"to":[38,92,126,149,155,197,240],"be":[39,90],"outstanding.":[40],"However,":[41],"there":[42],"few":[45],"studies":[46],"considering":[50],"nature":[52],"distribution":[55,145,153,238],"data.":[58],"Distribution":[59],"time-series":[62],"highly":[64],"concentrated":[65],"near":[66,104],"zero,":[67],"such":[69],"aspect":[70],"can":[71,89],"cause":[72],"low":[73],"on":[76,120],"whole":[78],"domain":[79,228],"probability":[81,232],"density":[82,233],"function":[83],"because":[84],"weights":[85],"trained":[91],"obtain":[93],"accurate":[94],"only":[96],"for":[97],"high":[99],"frequency":[100],"region,":[101],"that":[102,139,185],"is,":[103],"zero.":[105],"To":[106],"overcome":[107],"challenge,":[109],"we":[110,135],"propose":[111],"a":[112,121,150],"new":[113],"model":[115,189,221],"based":[119],"novel":[122],"non-linear":[123],"filtering":[124],"method":[125],"mitigate":[127],"concentration":[128],"property":[129],"volatility.":[131],"For":[132],"filtering,":[134],"utilize":[136],"root-type":[137],"functions":[138],"transform":[140],"extremely":[141],"left-biased":[142],"pointed":[144],"original":[147],"volume-upped":[151],"(VU)":[152],"shifted":[154],"right.":[157],"Long":[158],"short-term":[159],"memory":[160],"(LSTM)":[161],"employed":[163],"as":[164],"basic":[166],"implementation":[167],"model,":[168],"realized":[171],"S&P":[174],"500":[175],"predicted":[177],"using":[178],"proposed":[180,187,220],"models.":[181,217],"It":[182],"(VU-GARCH-LSTM)":[190],"obtains":[191],"21.03%":[192],"gain":[194],"respect":[196],"root":[199],"mean":[200,206],"square":[201],"error":[202],"(RMSE)":[203],"against":[204],"performances":[207],"existing":[210],"LSTM":[214],"Furthermore,":[218],"improves":[222],"right":[227],"region":[229],"label":[231,242],"by":[234],"making":[235],"comparable":[239],"distribution.":[243]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":23},{"year":2023,"cited_by_count":15},{"year":2022,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}