{"id":"https://openalex.org/W4405520989","doi":"https://doi.org/10.1109/access.2024.3519438","title":"Price Forecast of Treasury Bond Market Yield: Optimize Method Based on Deep Learning Model","display_name":"Price Forecast of Treasury Bond Market Yield: Optimize Method Based on Deep Learning Model","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4405520989","doi":"https://doi.org/10.1109/access.2024.3519438"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3519438","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3519438","pdf_url":null,"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":null,"license_id":null,"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://doi.org/10.1109/access.2024.3519438","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016416354","display_name":"Weiying Ping","orcid":null},"institutions":[{"id":"https://openalex.org/I59649739","display_name":"Jiangxi University of Finance and Economics","ror":"https://ror.org/03efmyj29","country_code":"CN","type":"education","lineage":["https://openalex.org/I59649739"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiying Ping","raw_affiliation_strings":["School of Statistics and Data Science, Jiangxi University of Finance and Economics, Nanchang, China","Jiangxi University of Finance and Economics, Nanchang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Statistics and Data Science, Jiangxi University of Finance and Economics, Nanchang, China","institution_ids":["https://openalex.org/I59649739"]},{"raw_affiliation_string":"Jiangxi University of Finance and Economics, Nanchang, China","institution_ids":["https://openalex.org/I59649739"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101130784","display_name":"Yuwen Hu","orcid":"https://orcid.org/0009-0001-7686-0942"},"institutions":[{"id":"https://openalex.org/I59649739","display_name":"Jiangxi University of Finance and Economics","ror":"https://ror.org/03efmyj29","country_code":"CN","type":"education","lineage":["https://openalex.org/I59649739"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuwen Hu","raw_affiliation_strings":["School of Statistics and Data Science, Jiangxi University of Finance and Economics, Nanchang, China","Jiangxi University of Finance and Economics, Nanchang, China"],"raw_orcid":"https://orcid.org/0009-0001-7686-0942","affiliations":[{"raw_affiliation_string":"School of Statistics and Data Science, Jiangxi University of Finance and Economics, Nanchang, China","institution_ids":["https://openalex.org/I59649739"]},{"raw_affiliation_string":"Jiangxi University of Finance and Economics, Nanchang, China","institution_ids":["https://openalex.org/I59649739"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101654999","display_name":"Liangqing Luo","orcid":"https://orcid.org/0000-0002-0835-9208"},"institutions":[{"id":"https://openalex.org/I59649739","display_name":"Jiangxi University of Finance and Economics","ror":"https://ror.org/03efmyj29","country_code":"CN","type":"education","lineage":["https://openalex.org/I59649739"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liangqing Luo","raw_affiliation_strings":["School of Statistics and Data Science, Jiangxi University of Finance and Economics, Nanchang, China","Jiangxi University of Finance and Economics, Nanchang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Statistics and Data Science, Jiangxi University of Finance and Economics, Nanchang, China","institution_ids":["https://openalex.org/I59649739"]},{"raw_affiliation_string":"Jiangxi University of Finance and Economics, Nanchang, China","institution_ids":["https://openalex.org/I59649739"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I59649739"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":2.9549,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.91668875,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"12","issue":null,"first_page":"194521","last_page":"194539"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.6351000070571899,"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":0.6351000070571899,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/treasury","display_name":"Treasury","score":0.7732677459716797},{"id":"https://openalex.org/keywords/yield","display_name":"Yield (engineering)","score":0.668184757232666},{"id":"https://openalex.org/keywords/bond","display_name":"Bond","score":0.6013503074645996},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5090448260307312},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.42810380458831787},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4124014377593994},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34100040793418884},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.2615968585014343},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.15713757276535034}],"concepts":[{"id":"https://openalex.org/C2780889827","wikidata":"https://www.wikidata.org/wiki/Q10756188","display_name":"Treasury","level":2,"score":0.7732677459716797},{"id":"https://openalex.org/C134121241","wikidata":"https://www.wikidata.org/wiki/Q899301","display_name":"Yield (engineering)","level":2,"score":0.668184757232666},{"id":"https://openalex.org/C69738904","wikidata":"https://www.wikidata.org/wiki/Q11693","display_name":"Bond","level":2,"score":0.6013503074645996},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5090448260307312},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.42810380458831787},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4124014377593994},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34100040793418884},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.2615968585014343},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.15713757276535034},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3519438","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3519438","pdf_url":null,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:0d94fa0ea01a43acb42054b593d960d2","is_oa":true,"landing_page_url":"https://doaj.org/article/0d94fa0ea01a43acb42054b593d960d2","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 12, Pp 194521-194539 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3519438","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3519438","pdf_url":null,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W1587521399","https://openalex.org/W2031959887","https://openalex.org/W2053831280","https://openalex.org/W2094871612","https://openalex.org/W2121029939","https://openalex.org/W2546646706","https://openalex.org/W2789364533","https://openalex.org/W2912107577","https://openalex.org/W2922995703","https://openalex.org/W2940603351","https://openalex.org/W2962825836","https://openalex.org/W2985099123","https://openalex.org/W3013793397","https://openalex.org/W3017051726","https://openalex.org/W3026472569","https://openalex.org/W3093310271","https://openalex.org/W3097125401","https://openalex.org/W3112061527","https://openalex.org/W3129701084","https://openalex.org/W3160501632","https://openalex.org/W3172096859","https://openalex.org/W4205921672","https://openalex.org/W4285799944","https://openalex.org/W4293817296","https://openalex.org/W4307465165","https://openalex.org/W4307930671","https://openalex.org/W4308734247","https://openalex.org/W4312105465","https://openalex.org/W4318465104","https://openalex.org/W4321497531","https://openalex.org/W4323317394","https://openalex.org/W4327640798","https://openalex.org/W4327922504","https://openalex.org/W4362585596","https://openalex.org/W4363650660","https://openalex.org/W4379525864","https://openalex.org/W4379525984","https://openalex.org/W4386701601","https://openalex.org/W4386797360","https://openalex.org/W4386947735","https://openalex.org/W4387536966","https://openalex.org/W4388855816","https://openalex.org/W4393140345","https://openalex.org/W4399828875","https://openalex.org/W6639179391","https://openalex.org/W6849157316","https://openalex.org/W6851690559","https://openalex.org/W6853220056","https://openalex.org/W6857279128"],"related_works":["https://openalex.org/W2370750334","https://openalex.org/W3121170756","https://openalex.org/W3122902554","https://openalex.org/W2595814987","https://openalex.org/W2379114542","https://openalex.org/W2383276925","https://openalex.org/W653340811","https://openalex.org/W1508180289","https://openalex.org/W2384781620","https://openalex.org/W1986918211"],"abstract_inverted_index":{"Accurate":[0],"forecasting":[1],"of":[2,32,67,112,133],"the":[3,30,54,60,65,68,75,84,96,113,124],"treasury":[4,114,137],"bond":[5,115,138],"market":[6,116],"is":[7,72,87,105],"beneficial":[8],"for":[9,18,89,136],"financial":[10,25],"institutions":[11],"to":[12,21,107],"formulate":[13],"investment":[14],"research":[15],"strategies":[16],"and":[17,38,45,62,74,92,94,149,151],"national":[19],"managers":[20],"build":[22],"a":[23],"modern":[24],"system.":[26],"This":[27],"paper":[28],"integrates":[29],"ideas":[31],"improved":[33],"multivariate":[34,69],"time":[35,70,110],"series":[36,71],"sampling":[37],"deep":[39,49,98,126],"learning":[40,50,57,99,127],"prediction":[41,111],"model":[42,51,100,128,147],"structure":[43],"optimization,":[44],"proposes":[46],"an":[47],"optimized":[48,103,125],"framework":[52],"under":[53],"LASSO-SMLR-PCA":[55],"machine":[56],"method.":[58],"Through":[59],"LASSO":[61],"SMLR":[63],"methods,":[64],"multicollinearity":[66],"reduced":[73],"variables":[76],"with":[77,101,142],"insignificant":[78],"correlation":[79],"coefficients":[80],"are":[81],"eliminated.":[82],"Then,":[83],"PCA":[85],"method":[86],"used":[88,106],"dimensionality":[90],"reduction":[91],"reconstruction,":[93],"finally,":[95],"LSTM":[97],"Bayesian":[102],"hyperparameters":[104],"achieve":[108],"rolling":[109],"yield":[117,139],"price.":[118],"The":[119],"empirical":[120],"results":[121],"show":[122],"that":[123],"performs":[129],"excellently":[130],"in":[131],"terms":[132],"evaluation":[134],"indicators":[135],"price":[140],"forecasting,":[141],"accurate":[143],"curve":[144],"fitting,":[145],"efficient":[146],"structure,":[148],"stable":[150],"effective":[152],"practical":[153],"application.":[154]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-28T08:01:55.173337","created_date":"2025-10-10T00:00:00"}