{"id":"https://openalex.org/W2050063324","doi":"https://doi.org/10.1142/s0219622006002209","title":"CONSTRAINED FORMULATIONS AND ALGORITHMS FOR PREDICTING STOCK PRICES BY RECURRENT FIR NEURAL NETWORKS","display_name":"CONSTRAINED FORMULATIONS AND ALGORITHMS FOR PREDICTING STOCK PRICES BY RECURRENT FIR NEURAL NETWORKS","publication_year":2006,"publication_date":"2006-12-01","ids":{"openalex":"https://openalex.org/W2050063324","doi":"https://doi.org/10.1142/s0219622006002209","mag":"2050063324"},"language":"en","primary_location":{"id":"doi:10.1142/s0219622006002209","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0219622006002209","pdf_url":null,"source":{"id":"https://openalex.org/S207089700","display_name":"International Journal of Information Technology & Decision Making","issn_l":"0219-6220","issn":["0219-6220","1793-6845"],"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 Information Technology & Decision Making","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/A5080392568","display_name":"Benjamin W. Wah","orcid":"https://orcid.org/0000-0002-2335-3904"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"BENJAMIN W. WAH","raw_affiliation_strings":["Department of Electrical and Computer Engineering, and the Coordinated Science Laboratory, University of Illinois, Urbana-Champaign, 1308 West Main Street, Urbana, IL 61801, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, and the Coordinated Science Laboratory, University of Illinois, Urbana-Champaign, 1308 West Main Street, Urbana, IL 61801, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008527711","display_name":"Minglun Qian","orcid":null},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"MING-LUN QIAN","raw_affiliation_strings":["Department of Computer Science, University of Illinois, Urbana-Champaign, 201 North Goodwin Avenue, Urbana, IL 61801, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Illinois, Urbana-Champaign, 201 North Goodwin Avenue, Urbana, IL 61801, USA","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5080392568"],"corresponding_institution_ids":["https://openalex.org/I157725225"],"apc_list":null,"apc_paid":null,"fwci":1.3972,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.84603651,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"05","issue":"04","first_page":"639","last_page":"658"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9969000220298767,"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"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9969000220298767,"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"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9962999820709229,"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":0.9872999787330627,"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/computer-science","display_name":"Computer science","score":0.6363919377326965},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4978353977203369},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.47980454564094543},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4660789668560028},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4595794081687927},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.44256144762039185},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.44241511821746826},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.44126054644584656},{"id":"https://openalex.org/keywords/stock-price","display_name":"Stock price","score":0.44047749042510986},{"id":"https://openalex.org/keywords/finite-impulse-response","display_name":"Finite impulse response","score":0.418459951877594},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4125569760799408},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2989234924316406},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.26369380950927734},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2631695866584778}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6363919377326965},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4978353977203369},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.47980454564094543},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4660789668560028},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4595794081687927},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.44256144762039185},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.44241511821746826},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.44126054644584656},{"id":"https://openalex.org/C2988984586","wikidata":"https://www.wikidata.org/wiki/Q1020013","display_name":"Stock price","level":3,"score":0.44047749042510986},{"id":"https://openalex.org/C198386975","wikidata":"https://www.wikidata.org/wiki/Q117785","display_name":"Finite impulse response","level":2,"score":0.418459951877594},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4125569760799408},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2989234924316406},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26369380950927734},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2631695866584778},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1142/s0219622006002209","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0219622006002209","pdf_url":null,"source":{"id":"https://openalex.org/S207089700","display_name":"International Journal of Information Technology & Decision Making","issn_l":"0219-6220","issn":["0219-6220","1793-6845"],"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 Information Technology & Decision Making","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:wsi:ijitdm:v:05:y:2006:i:04:n:s0219622006002209","is_oa":false,"landing_page_url":"http://www.worldscientific.com/doi/abs/10.1142/S0219622006002209","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},{"id":"pmh:oai:RePEc:wsi:ijitdm:v:05:y:2006:i:04:p:639-658","is_oa":false,"landing_page_url":"http://www.worldscinet.com/cgi-bin/details.cgi?type=html&id=pii:S0219622006002209","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W8173372","https://openalex.org/W1583823036","https://openalex.org/W2039839897","https://openalex.org/W2048665112","https://openalex.org/W2068625901","https://openalex.org/W2105119576","https://openalex.org/W2110485445","https://openalex.org/W2124776405","https://openalex.org/W2313953460","https://openalex.org/W3115084465","https://openalex.org/W4289744562","https://openalex.org/W4301502985","https://openalex.org/W4388215482"],"related_works":["https://openalex.org/W4225394202","https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W3032952384","https://openalex.org/W3034302643","https://openalex.org/W1847088711","https://openalex.org/W3036642985","https://openalex.org/W2964335273","https://openalex.org/W1889624880","https://openalex.org/W2153627311"],"abstract_inverted_index":{"In":[0],"this":[1,97],"paper,":[2],"we":[3,47,83],"develop":[4,57],"a":[5,20,31,43,53],"new":[6],"constrained":[7,54,80],"artificial-neural-network":[8],"(ANN)":[9],"formulation":[10],"and":[11,36,63,122,144],"the":[12,49,65,72,100,112,115,123,133],"associated":[13],"learning":[14,50,66],"algorithm":[15],"for":[16,59,78],"predicting":[17],"stock":[18,28],"prices,":[19],"difficult":[21],"time-series":[22],"prediction":[23,86,142],"problem.":[24],"We":[25],"characterize":[26],"daily":[27],"prices":[29],"as":[30,52],"noisy":[32,105],"non-stationary":[33],"time":[34,91,106],"series":[35,107],"identify":[37],"its":[38],"predictable":[39],"low-frequency":[40],"components.":[41],"Using":[42],"recurrent":[44],"finite-impulse-response":[45],"ANN,":[46],"formulate":[48],"problem":[51,67],"optimization":[55],"problem,":[56],"constraints":[58,126],"incorporating":[60],"cross":[61,128],"validations,":[62],"solve":[64],"using":[68],"algorithms":[69],"based":[70],"on":[71,88,127],"theory":[73],"of":[74,104,114,125,135],"extended":[75],"saddle":[76],"points":[77],"nonlinear":[79],"optimization.":[81],"Finally,":[82],"illustrate":[84],"our":[85],"results":[87,139],"ten":[89],"stock-price":[90],"series.":[92],"Our":[93,137],"main":[94],"contributions":[95],"in":[96],"paper":[98],"are":[99],"channel-specific":[101],"low-pass":[102,116],"filtering":[103],"obtained":[108],"by":[109],"wavelet":[110],"decomposition,":[111],"transformation":[113],"signals":[117],"to":[118],"improve":[119,132],"their":[120],"stationarity,":[121],"incorporation":[124],"validation":[129],"that":[130],"can":[131],"accuracy":[134,143],"predictions.":[136],"experimental":[138],"demonstrate":[140],"good":[141],"annual":[145],"returns.":[146]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2014,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}