{"id":"https://openalex.org/W3208873264","doi":"https://doi.org/10.4018/joeuc.289222","title":"Application of Improved Convolution Neural Network in Financial Forecasting","display_name":"Application of Improved Convolution Neural Network in Financial Forecasting","publication_year":2021,"publication_date":"2021-09-29","ids":{"openalex":"https://openalex.org/W3208873264","doi":"https://doi.org/10.4018/joeuc.289222","mag":"3208873264"},"language":"en","primary_location":{"id":"doi:10.4018/joeuc.289222","doi":"https://doi.org/10.4018/joeuc.289222","is_oa":true,"landing_page_url":"https://doi.org/10.4018/joeuc.289222","pdf_url":"https://www.igi-global.com/ViewTitle.aspx?TitleId=289222&isxn=9781799893264","source":{"id":"https://openalex.org/S80285584","display_name":"Journal of Organizational and End User Computing","issn_l":"1546-2234","issn":["1546-2234","1546-5012"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-b","cwts-core","dongbi-b","erih-plus","fnege-4","jufo-1","norway-1","poland-40","russia-white-list-1"],"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Organizational and End User Computing","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.igi-global.com/ViewTitle.aspx?TitleId=289222&isxn=9781799893264","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066270432","display_name":"Wensheng Dai","orcid":"https://orcid.org/0000-0001-9644-1684","observed_orcids":["https://orcid.org/0000-0001-9644-1684"]},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wensheng Dai","raw_affiliation_strings":["China Financial Policy Research Center, International Monetary Institute, Renmin University of China, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Financial Policy Research Center, International Monetary Institute, Renmin University of China, China","institution_ids":["https://openalex.org/I78988378"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5066270432"],"corresponding_institution_ids":["https://openalex.org/I78988378"],"apc_list":null,"apc_paid":null,"fwci":3.3195,"has_fulltext":true,"cited_by_count":31,"citation_normalized_percentile":{"value":0.92530818,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":99},"biblio":{"volume":"34","issue":"3","first_page":"1","last_page":"16"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9965999722480774,"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":0.9965999722480774,"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":0.00139999995008111,"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/T10320","display_name":"Neural Networks and Applications","score":0.0006000000284984708,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/improved-convolutional-neural-network","display_name":"improved convolutional neural network","score":0.8960000276565552},{"id":"https://openalex.org/keywords/financial-forecasting","display_name":"financial forecasting","score":0.9789999723434448},{"id":"https://openalex.org/keywords/credit-risk-assessment","display_name":"credit risk assessment","score":0.5889999866485596},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"principal component analysis","score":0.8450000286102295},{"id":"https://openalex.org/keywords/data-denoising","display_name":"data denoising","score":0.7350000143051147},{"id":"https://openalex.org/keywords/stock-market-forecasting","display_name":"stock market prediction","score":0.6800000071525574},{"id":"https://openalex.org/keywords/financial-information","display_name":"financial information","score":0.4429999887943268}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6459041833877563},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.5989347696304321},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5599180459976196},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.5235226154327393},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5224974751472473},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.5187007188796997},{"id":"https://openalex.org/C19244329","wikidata":"https://www.wikidata.org/wiki/Q208697","display_name":"Financial market","level":2,"score":0.4983186721801758},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.43320178985595703},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.42811456322669983},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.41670021414756775},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.41322070360183716},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3848378658294678},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3767319619655609},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.23349308967590332},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.21510154008865356},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15832677483558655},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14577975869178772},{"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},{"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:10.4018/joeuc.289222","doi":"https://doi.org/10.4018/joeuc.289222","is_oa":true,"landing_page_url":"https://doi.org/10.4018/joeuc.289222","pdf_url":"https://www.igi-global.com/ViewTitle.aspx?TitleId=289222&isxn=9781799893264","source":{"id":"https://openalex.org/S80285584","display_name":"Journal of Organizational and End User Computing","issn_l":"1546-2234","issn":["1546-2234","1546-5012"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-b","cwts-core","dongbi-b","erih-plus","fnege-4","jufo-1","norway-1","poland-40","russia-white-list-1"],"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Organizational and End User Computing","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.4018/joeuc.289222","doi":"https://doi.org/10.4018/joeuc.289222","is_oa":true,"landing_page_url":"https://doi.org/10.4018/joeuc.289222","pdf_url":"https://www.igi-global.com/ViewTitle.aspx?TitleId=289222&isxn=9781799893264","source":{"id":"https://openalex.org/S80285584","display_name":"Journal of Organizational and End User Computing","issn_l":"1546-2234","issn":["1546-2234","1546-5012"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-b","cwts-core","dongbi-b","erih-plus","fnege-4","jufo-1","norway-1","poland-40","russia-white-list-1"],"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Organizational and End User Computing","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3208873264.pdf","grobid_xml":"https://content.openalex.org/works/W3208873264.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W2770377017","https://openalex.org/W2786908239","https://openalex.org/W2790836455","https://openalex.org/W2793357412","https://openalex.org/W2890597972","https://openalex.org/W2892719431","https://openalex.org/W2903033372","https://openalex.org/W2909545703","https://openalex.org/W2913026714","https://openalex.org/W2922360817","https://openalex.org/W2925877073","https://openalex.org/W2937930874","https://openalex.org/W2940569086","https://openalex.org/W2942039563","https://openalex.org/W2944495915","https://openalex.org/W2944900301","https://openalex.org/W2946749059","https://openalex.org/W2947281693","https://openalex.org/W2948023744","https://openalex.org/W2948184028","https://openalex.org/W2948461581","https://openalex.org/W2953175414","https://openalex.org/W2955411915","https://openalex.org/W2964670245","https://openalex.org/W2966350350","https://openalex.org/W2974409628","https://openalex.org/W3001472289","https://openalex.org/W3011028810","https://openalex.org/W3024646332","https://openalex.org/W3042981023","https://openalex.org/W3116380314","https://openalex.org/W4248350653"],"related_works":["https://openalex.org/W7033338928","https://openalex.org/W2997720291","https://openalex.org/W4226091908","https://openalex.org/W2947053643","https://openalex.org/W4406362456","https://openalex.org/W4220794015","https://openalex.org/W6960266760","https://openalex.org/W7155213396","https://openalex.org/W3120669780","https://openalex.org/W2994069988"],"abstract_inverted_index":{"Financial":[0],"status":[1],"and":[2,22,28,52,71,111,119,124,131,161],"its":[3],"role":[4],"in":[5,40,156],"the":[6,17,41,49,62,65,73,102,109,121,134,150,162],"national":[7,44],"economy":[8],"have":[9,30],"been":[10],"increasingly":[11],"recognized.":[12],"In":[13],"order":[14],"to":[15,55,88,96],"deduce":[16],"source":[18],"of":[19,43,64,92,104,113,116],"monetary":[20],"funds":[21],"determine":[23,133],"their":[24],"whereabouts,":[25],"financial":[26,56,129],"information":[27],"prediction":[29,163],"become":[31],"a":[32,90],"scientific":[33],"method":[34],"that":[35,149],"can":[36],"not":[37],"be":[38],"ignored":[39],"development":[42],"economy.":[45],"This":[46],"paper":[47,158],"improves":[48],"existing":[50],"CNN":[51],"applies":[53],"it":[54],"credit":[57,128],"from":[58,144],"different":[59],"perspectives.":[60],"Firstly,":[61],"noise":[63],"collected":[66],"data":[67,143],"set":[68,91],"is":[69,76],"deleted,":[70],"then":[72,132],"clustering":[74],"result":[75],"more":[77],"stable":[78],"by":[79],"principal":[80],"component":[81],"analysis.":[82],"The":[83],"observation":[84,93,117],"vectors":[85,94],"are":[86],"segmented":[87],"obtain":[89],"corresponding":[95],"each":[97],"hidden":[98],"state.":[99],"Based":[100],"on":[101,141],"output":[103],"PCA":[105],"algorithm,":[106],"we":[107],"recalculate":[108],"mean":[110,123],"variance":[112],"all":[114],"kinds":[115],"vectors,":[118],"use":[120],"new":[122],"covariance":[125],"matrix":[126],"as":[127],"credit,":[130],"best":[135],"model":[136],"parameters.The":[137],"empirical":[138],"results":[139],"based":[140],"specific":[142],"China's":[145],"stock":[146],"market":[147],"show":[148],"improved":[151],"convolutional":[152],"neural":[153],"network":[154],"proposed":[155],"this":[157],"has":[159],"advantages":[160],"accuracy":[164],"reaches.":[165]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":1}],"updated_date":"2026-09-06T09:10:53.072986","created_date":"2021-11-08T00:00:00"}
