{"id":"https://openalex.org/W4401023812","doi":"https://doi.org/10.24963/ijcai.2024/676","title":"RSAP-DFM: Regime-Shifting Adaptive Posterior Dynamic Factor Model for Stock Returns Prediction","display_name":"RSAP-DFM: Regime-Shifting Adaptive Posterior Dynamic Factor Model for Stock Returns Prediction","publication_year":2024,"publication_date":"2024-07-26","ids":{"openalex":"https://openalex.org/W4401023812","doi":"https://doi.org/10.24963/ijcai.2024/676"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2024/676","doi":"https://doi.org/10.24963/ijcai.2024/676","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2024/676","pdf_url":"https://www.ijcai.org/proceedings/2024/0676.pdf","source":{"id":"https://openalex.org/S4306419999","display_name":"International Joint Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://www.ijcai.org/proceedings/2024/0676.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Quanzhou Xiang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Quanzhou Xiang","raw_affiliation_strings":["Fudan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054123780","display_name":"Zhan Chen","orcid":"https://orcid.org/0000-0001-8687-8348","observed_orcids":["https://orcid.org/0000-0001-8687-8348"]},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhan Chen","raw_affiliation_strings":["Fudan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115602614","display_name":"Qi Sun","orcid":"https://orcid.org/0009-0009-6129-953X","observed_orcids":["https://orcid.org/0009-0009-6129-953X","https://orcid.org/0000-0002-1698-8741"]},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qi Sun","raw_affiliation_strings":["Fudan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055104771","display_name":"Rujun Jiang","orcid":"https://orcid.org/0000-0002-6610-6778","observed_orcids":["https://orcid.org/0000-0002-6610-6778"]},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rujun Jiang","raw_affiliation_strings":["Fudan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":3.4245,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.93310818,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"6116","last_page":"6124"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.8759999871253967,"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":0.8759999871253967,"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/T10047","display_name":"Financial Markets and Investment Strategies","score":0.12150000035762787,"subfield":{"id":"https://openalex.org/subfields/2003","display_name":"Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12123","display_name":"Financial Analysis and Corporate Governance","score":0.0008999999845400453,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/dynamic-factor-models","display_name":"dynamic factor models","score":0.7210000157356262},{"id":"https://openalex.org/keywords/stock-return-prediction","display_name":"stock return prediction","score":0.8629999756813049},{"id":"https://openalex.org/keywords/two-stage-optimization","display_name":"two-stage optimization","score":0.6840000152587891},{"id":"https://openalex.org/keywords/quantitative-investment","display_name":"quantitative investment","score":0.49399998784065247}],"concepts":[{"id":"https://openalex.org/C62064638","wikidata":"https://www.wikidata.org/wiki/Q553878","display_name":"Design for manufacturability","level":2,"score":0.7579355835914612},{"id":"https://openalex.org/C155702961","wikidata":"https://www.wikidata.org/wiki/Q5318975","display_name":"Dynamic factor","level":2,"score":0.6741581559181213},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.5106905102729797},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4635296165943146},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.34520184993743896},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17797017097473145},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.15972566604614258},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2024/676","doi":"https://doi.org/10.24963/ijcai.2024/676","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2024/676","pdf_url":"https://www.ijcai.org/proceedings/2024/0676.pdf","source":{"id":"https://openalex.org/S4306419999","display_name":"International Joint Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2024/676","doi":"https://doi.org/10.24963/ijcai.2024/676","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2024/676","pdf_url":"https://www.ijcai.org/proceedings/2024/0676.pdf","source":{"id":"https://openalex.org/S4306419999","display_name":"International Joint Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G1075128521","display_name":null,"funder_award_id":"12171100","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320309612","display_name":"Natural Science Foundation of Shanghai","ror":null},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4401023812.pdf","grobid_xml":"https://content.openalex.org/works/W4401023812.grobid-xml"},"referenced_works_count":5,"referenced_works":["https://openalex.org/W1945616565","https://openalex.org/W3087827640","https://openalex.org/W3199125616","https://openalex.org/W3208499031","https://openalex.org/W4307310739"],"related_works":["https://openalex.org/W7143280918","https://openalex.org/W4283804236","https://openalex.org/W7196961339","https://openalex.org/W4392539504","https://openalex.org/W4385768209","https://openalex.org/W4403582598","https://openalex.org/W4409346853","https://openalex.org/W7204847723","https://openalex.org/W1966223861","https://openalex.org/W4402802684"],"abstract_inverted_index":{"As":[0],"the":[1,15,32,43,54,58,70,75,88,104,112,120,126,149,152,161],"latest":[2],"development":[3],"of":[4,17,24,31,108,129,151,163],"asset":[5],"pricing":[6],"research,":[7],"how":[8,49,66],"to":[9,13,39,50,52,67,74],"use":[10],"machine":[11],"learning":[12],"improve":[14],"performance":[16],"factor":[18,45,93],"models":[19],"has":[20],"become":[21],"a":[22],"topic":[23],"concern":[25],"in":[26],"recent":[27],"years.":[28],"The":[29],"variability":[30],"instantaneous":[33],"macro":[34,55,72],"environment":[35],"brings":[36],"great":[37],"difficulties":[38],"quantitative":[40],"investment,":[41],"so":[42],"extended":[44],"model":[46,135],"must":[47],"learn":[48],"self-adapt":[51],"extract":[53],"pattern":[56,73],"from":[57],"massive":[59],"stock":[60,76,109,158],"volume":[61],"and":[62,65,102,142],"price":[63],"information,":[64],"continuously":[68],"map":[69],"extracted":[71],"investment":[77],"is":[78],"also":[79],"an":[80,137],"open":[81],"question.":[82],"To":[83],"this":[84],"end,":[85],"we":[86],"propose":[87],"first":[89,113],"continuous":[90,99],"regime-based":[91],"dynamic":[92,105],"model,":[94,165],"RSAP-DFM,":[95],"which":[96,146,166],"adaptively":[97],"extracts":[98],"macroeconomic":[100],"information":[101],"completes":[103],"explicit":[106],"mapping":[107,127],"returns":[110],"for":[111],"time":[114],"through":[115],"dual":[116],"regime":[117],"shifting,":[118],"while":[119],"adversarial":[121],"posterior":[122],"factors":[123],"effectively":[124],"correct":[125],"deviation":[128],"prior":[130],"factors.":[131],"In":[132],"addition,":[133],"our":[134,164],"integrates":[136],"innovative":[138],"two-stage":[139],"optimization":[140],"algorithm":[141],"normally":[143],"distributed":[144],"sampling,":[145],"further":[147],"enhances":[148],"robustness":[150],"model.":[153],"Performance":[154],"on":[155],"three":[156],"real":[157],"datasets":[159],"validates":[160],"validity":[162],"exceeds":[167],"any":[168],"previous":[169],"methods":[170],"available.":[171]},"counts_by_year":[{"year":2026,"cited_by_count":6}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2024-07-27T00:00:00"}
