{"id":"https://openalex.org/W4212889427","doi":"https://doi.org/10.1142/s0218194021400222","title":"Forecasting Stock Index Using a Volume-Aware Positional Attention-Based Recurrent Neural Network","display_name":"Forecasting Stock Index Using a Volume-Aware Positional Attention-Based Recurrent Neural Network","publication_year":2021,"publication_date":"2021-12-01","ids":{"openalex":"https://openalex.org/W4212889427","doi":"https://doi.org/10.1142/s0218194021400222"},"language":"en","primary_location":{"id":"doi:10.1142/s0218194021400222","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218194021400222","pdf_url":null,"source":{"id":"https://openalex.org/S131442419","display_name":"International Journal of Software Engineering and Knowledge Engineering","issn_l":"0218-1940","issn":["0218-1940","1793-6403"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Software Engineering and Knowledge Engineering","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/A5054440450","display_name":"Xinpeng Yu","orcid":"https://orcid.org/0000-0002-0482-8310"},"institutions":[{"id":"https://openalex.org/I187380120","display_name":"Agricultural Bank of China","ror":"https://ror.org/015g9sa94","country_code":"CN","type":"other","lineage":["https://openalex.org/I187380120"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinpeng Yu","raw_affiliation_strings":["Agricultural Bank of China, Beijing, P. R. China"],"affiliations":[{"raw_affiliation_string":"Agricultural Bank of China, Beijing, P. R. China","institution_ids":["https://openalex.org/I187380120"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100605483","display_name":"Dagang Li","orcid":"https://orcid.org/0000-0002-8134-0538"},"institutions":[{"id":"https://openalex.org/I111950717","display_name":"Macau University of Science and Technology","ror":"https://ror.org/03jqs2n27","country_code":"MO","type":"education","lineage":["https://openalex.org/I111950717","https://openalex.org/I4391767947"]}],"countries":["MO"],"is_corresponding":true,"raw_author_name":"Dagang Li","raw_affiliation_strings":["International Institute of Next Generation Internet, Macau University of Science and Technology, Zhuhai-M.U.S.T. Science and Technology, Research Institute, Macau, P. R. China"],"affiliations":[{"raw_affiliation_string":"International Institute of Next Generation Internet, Macau University of Science and Technology, Zhuhai-M.U.S.T. Science and Technology, Research Institute, Macau, P. R. China","institution_ids":["https://openalex.org/I111950717"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074799043","display_name":"Ying Shen","orcid":"https://orcid.org/0000-0002-3220-904X"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Shen","raw_affiliation_strings":["School of Electronics and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, P. R. China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, P. R. China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100605483"],"corresponding_institution_ids":["https://openalex.org/I111950717"],"apc_list":null,"apc_paid":null,"fwci":0.5228,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.70154106,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"31","issue":"11n12","first_page":"1783","last_page":"1801"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9794999957084656,"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/recurrent-neural-network","display_name":"Recurrent neural network","score":0.8623224496841431},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7668491005897522},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6080443859100342},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5429025888442993},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5381286144256592},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.48783162236213684},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.47396227717399597},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.46205320954322815},{"id":"https://openalex.org/keywords/stock-trading","display_name":"Stock trading","score":0.4294910728931427},{"id":"https://openalex.org/keywords/position","display_name":"Position (finance)","score":0.427554726600647},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4231411814689636},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.07609501481056213},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.07072299718856812}],"concepts":[{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.8623224496841431},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7668491005897522},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6080443859100342},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5429025888442993},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5381286144256592},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.48783162236213684},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.47396227717399597},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.46205320954322815},{"id":"https://openalex.org/C2989233474","wikidata":"https://www.wikidata.org/wiki/Q7831917","display_name":"Stock trading","level":4,"score":0.4294910728931427},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.427554726600647},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4231411814689636},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.07609501481056213},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.07072299718856812},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","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},{"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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0218194021400222","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218194021400222","pdf_url":null,"source":{"id":"https://openalex.org/S131442419","display_name":"International Journal of Software Engineering and Knowledge Engineering","issn_l":"0218-1940","issn":["0218-1940","1793-6403"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Software Engineering and Knowledge Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.44999998807907104,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1905882502","https://openalex.org/W2025291942","https://openalex.org/W2064675550","https://openalex.org/W2107878631","https://openalex.org/W2143612262","https://openalex.org/W2484997644","https://openalex.org/W2510046892","https://openalex.org/W2624385633","https://openalex.org/W2759211898","https://openalex.org/W2898892115","https://openalex.org/W2905054207","https://openalex.org/W2979835384","https://openalex.org/W2997421965","https://openalex.org/W2998470965","https://openalex.org/W3008246618","https://openalex.org/W3036722537","https://openalex.org/W3098583349","https://openalex.org/W3123852583"],"related_works":["https://openalex.org/W3008584592","https://openalex.org/W2370669686","https://openalex.org/W2185889083","https://openalex.org/W2888254471","https://openalex.org/W1481739708","https://openalex.org/W4283277338","https://openalex.org/W4287219598","https://openalex.org/W2794710198","https://openalex.org/W3130821343","https://openalex.org/W2504342329"],"abstract_inverted_index":{"With":[0],"the":[1,27,49,59,77,81,119,123,129,140,158,163],"rapid":[2],"development":[3],"of":[4,114,142],"deep":[5],"learning,":[6],"more":[7],"researchers":[8],"have":[9],"attempted":[10],"to":[11,25,38,57,118,133],"apply":[12],"nonlinear":[13],"learning":[14],"methods":[15],"such":[16],"as":[17],"recurrent":[18,101],"neural":[19,102],"networks":[20],"(RNNs)":[21],"and":[22,66,155,157],"attention":[23,43,82,120,131],"mechanisms":[24],"capture":[26],"complex":[28],"patterns":[29],"hidden":[30],"in":[31,91],"stock":[32,88,147,151],"market":[33,148],"trends.":[34],"Most":[35],"existing":[36,170],"approaches":[37],"this":[39,92,106],"task":[40],"employ":[41],"an":[42],"mechanism":[44,83],"that":[45,162],"primarily":[46],"relies":[47],"on":[48],"information":[50],"extracted":[51],"from":[52],"input":[53],"features":[54,85],"but":[55],"fails":[56],"consider":[58],"other":[60],"important":[61],"factors":[62],"(e.g.":[63],"trading":[64,124],"volume":[65,125],"position),":[67],"which":[68],"can":[69,166],"potentially":[70],"enhance":[71],"these":[72],"attention-based":[73,100],"approaches.":[74],"Motivated":[75],"by":[76],"observation,":[78],"we":[79,95,109,144],"extend":[80],"with":[84],"needed":[86],"for":[87,105,150],"performance":[89],"prediction":[90],"paper.":[93],"Specifically,":[94],"propose":[96,110],"a":[97,111,135],"volume-aware":[98],"positional":[99],"network":[103],"(VPA-RNN)":[104],"task.":[107],"First,":[108],"generic":[112],"method":[113],"adding":[115],"position":[116],"awareness":[117],"mechanism.":[121],"Next,":[122],"is":[126],"incorporated":[127],"into":[128],"original":[130],"distribution":[132],"form":[134],"revised":[136],"distribution.":[137],"To":[138],"evaluate":[139],"effectiveness":[141],"VPA-RNN,":[143],"collected":[145],"real":[146],"data":[149],"indexes":[152],"S&amp;P":[153],"500":[154],"DJIA,":[156],"experimental":[159],"results":[160],"show":[161],"proposed":[164],"VPA-RNN":[165],"significantly":[167],"outperform":[168],"several":[169],"highly":[171],"competitive":[172],"methods.":[173]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
