{"id":"https://openalex.org/W4409869039","doi":"https://doi.org/10.1145/3718751.3718881","title":"Forecasting Government Bond Yields Based on Multi-Factor LSTM Model","display_name":"Forecasting Government Bond Yields Based on Multi-Factor LSTM Model","publication_year":2024,"publication_date":"2024-11-15","ids":{"openalex":"https://openalex.org/W4409869039","doi":"https://doi.org/10.1145/3718751.3718881"},"language":"en","primary_location":{"id":"doi:10.1145/3718751.3718881","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3718751.3718881","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3718751.3718881","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3718751.3718881","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5106943170","display_name":"Guangwu Kuang","orcid":null},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangwu Kuang","raw_affiliation_strings":["Wuhan University of Technology, Wuhan, Hubei, China"],"raw_orcid":"https://orcid.org/0009-0004-6983-6655","affiliations":[{"raw_affiliation_string":"Wuhan University of Technology, Wuhan, Hubei, China","institution_ids":["https://openalex.org/I196699116"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yue Yu","orcid":"https://orcid.org/0009-0006-0774-5138"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Yu","raw_affiliation_strings":["Wuhan University of Technology, Wuhan, Hubei, China"],"raw_orcid":"https://orcid.org/0009-0006-0774-5138","affiliations":[{"raw_affiliation_string":"Wuhan University of Technology, Wuhan, Hubei, China","institution_ids":["https://openalex.org/I196699116"]}]},{"author_position":"last","author":{"id":null,"display_name":"Ruoxi Cheng","orcid":"https://orcid.org/0009-0001-1729-4995"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruoxi Cheng","raw_affiliation_strings":["Wuhan University of Technology, Wuhan, Hubei, China"],"raw_orcid":"https://orcid.org/0009-0001-1729-4995","affiliations":[{"raw_affiliation_string":"Wuhan University of Technology, Wuhan, Hubei, China","institution_ids":["https://openalex.org/I196699116"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I196699116"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.42787836,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"797","last_page":"803"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9905999898910522,"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":0.9905999898910522,"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":0.979200005531311,"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":0.9718999862670898,"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/factor","display_name":"Factor (programming language)","score":0.6113309860229492},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6004812121391296},{"id":"https://openalex.org/keywords/bond","display_name":"Bond","score":0.5649809241294861},{"id":"https://openalex.org/keywords/government","display_name":"Government (linguistics)","score":0.563984751701355},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.500037670135498},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34035930037498474},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.17809796333312988},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.17779341340065002},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.12290114164352417}],"concepts":[{"id":"https://openalex.org/C2781039887","wikidata":"https://www.wikidata.org/wiki/Q1391724","display_name":"Factor (programming language)","level":2,"score":0.6113309860229492},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6004812121391296},{"id":"https://openalex.org/C69738904","wikidata":"https://www.wikidata.org/wiki/Q11693","display_name":"Bond","level":2,"score":0.5649809241294861},{"id":"https://openalex.org/C2778137410","wikidata":"https://www.wikidata.org/wiki/Q2732820","display_name":"Government (linguistics)","level":2,"score":0.563984751701355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.500037670135498},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34035930037498474},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.17809796333312988},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.17779341340065002},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.12290114164352417},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3718751.3718881","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3718751.3718881","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3718751.3718881","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3718751.3718881","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3718751.3718881","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3718751.3718881","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4409869039.pdf","grobid_xml":"https://content.openalex.org/works/W4409869039.grobid-xml"},"referenced_works_count":12,"referenced_works":["https://openalex.org/W2059856226","https://openalex.org/W2101300975","https://openalex.org/W3035736507","https://openalex.org/W3093742691","https://openalex.org/W3164284510","https://openalex.org/W4230644069","https://openalex.org/W4247066346","https://openalex.org/W4312561791","https://openalex.org/W4378716223","https://openalex.org/W4386917653","https://openalex.org/W4393140345","https://openalex.org/W6666761814"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Government":[0],"bond":[1,17,49,103],"yields":[2,18],"reflect":[3],"macroeconomic":[4],"conditions":[5],"and":[6,28,73,86],"the":[7,46,57,64,91],"borrowing":[8],"costs":[9],"of":[10,20,54],"government":[11,16,48,102],"debt":[12],"financing.":[13],"Predicting":[14],"future":[15],"is":[19,42],"significant":[21],"importance":[22],"for":[23],"governments":[24],"to":[25,44],"formulate":[26],"rational":[27],"efficient":[29],"fiscal":[30],"policies.":[31],"In":[32],"this":[33],"study,":[34],"a":[35,52],"multi-factor":[36,92],"LSTM":[37,84,93],"(Long":[38],"Short-Term":[39],"Memory)":[40],"model":[41,94],"established":[43],"forecast":[45],"ten-year":[47,101],"yields,":[50],"considering":[51],"combination":[53],"factors":[55],"including":[56],"Shanghai":[58],"Composite":[59],"Index,":[60,67],"Shibor":[61],"overnight":[62],"rates,":[63],"US":[65],"Dollar":[66],"gold":[68],"prices,":[69],"CRB":[70],"spot":[71],"index,":[72],"fuel":[74],"oil":[75],"futures":[76],"prices.":[77],"The":[78],"results":[79],"are":[80],"compared":[81],"with":[82],"single-factor":[83],"models":[85],"ARIMA":[87],"models,":[88],"revealing":[89],"that":[90],"demonstrates":[95],"significantly":[96],"superior":[97],"performance":[98],"in":[99],"predicting":[100],"yields.":[104]},"counts_by_year":[],"updated_date":"2026-08-24T07:32:12.397491","created_date":"2025-10-10T00:00:00"}